SiteOne Crawler Report - www.llama.com

Summary

Website Quality Score

6.6 Fair

Performance

6.2

SEO

6.0

Security

8.5

Accessibility

5.0

Best Practices

6.7

⛔ Skipped URLs - 219 skipped URLs found.
⛔ Redirects - 21 redirects found.
⛔ Performance CRITICAL - 6 slow non-media URLs found (slower than 3 seconds).
⛔ 5 page(s) with multiple <h1> headings.
⚠️ 44 page(s) do not support Brotli compression.
⚠️ No WebP image found on the website.
⚠️ No AVIF image found on the website.
⚠️ 6 page(s) with skipped heading levels.
⚠️ 44 page(s) with deep DOM (> 30 levels).
⚠️ 1 page(s) without image alt attributes.
⚠️ 44 page(s) without aria labels.
⚠️ 44 page(s) without role attributes.
⚠️ Security - 90 pages(s) with warning(s).
⏩ Loaded robots.txt for domain 'www.llama.com': status code 200, size 1 kB and took 223 ms.
⏩ External URLs - 219 external URL(s) found.
⏩ 404 NOTICE - 1 non-existent page(s) found.
⏩ HTTP headers - found 31 unique headers.
✅ SSL/TLS certificate is valid until Apr 1 23:59:59 2026 GMT. Issued by C = US, O = DigiCert Inc, CN = DigiCert Global G2 TLS RSA SHA256 2020 CA1. Subject is C = US, ST = California, L = Menlo Park, O = Meta Platforms, Inc., CN = llama.com.
✅ SSL/TLS certificate issued by 'C = US, O = DigiCert Inc, CN = DigiCert Global G2 TLS RSA SHA256 2020 CA1'.
✅ All 41 unique title(s) are within the allowed 10% duplicity. Highest duplicity title has 6%.
✅ All 38 description(s) are within the allowed 10% duplicity. Highest duplicity description has 9%.
✅ All pages have quoted attributes.
✅ All pages have inline SVGs smaller than 5120 bytes.
✅ All pages have inline SVGs with less than 5 duplicates.
✅ All pages have valid or none inline SVGs.
✅ All pages have <h1> heading.
✅ All pages have clickable (interactive) phone numbers.
✅ All pages have valid HTML.
✅ All pages have form labels.
✅ All pages have lang attribute.
✅ DNS IPv4 OK: domain www.llama.com resolved to llama.com., 157.240.205.1 (DNS server: 127.0.0.53).
✅ DNS IPv6 OK: domain www.llama.com resolved to llama.com., 2a03:2880:f013:0:face:b00c:0:2 (DNS server: 127.0.0.53).
📌 DNS Aliases: IP(s) for domain www.llama.com were resolved by CNAME chain www.llama.com > llama.com.

Visited URLs

Found 66 row(s).

URL	Status	Type	Time (s)	Size	Cache
/docs	302	Redirect	143 ms	137 B	None
/docs/overview/	200	HTML	1.2 s	954 kB	0s (no-store)
/docs/how-to-guides/quantization/	200	HTML	1.4 s	862 kB	0s (no-store)
/docs/deployment/security-in-production/	200	HTML	1.9 s	960 kB	0s (no-store)
/docs/getting-the-models/kaggle/	200	HTML	1.4 s	788 kB	0s (no-store)
/docs/deployment/autoscaling/	200	HTML	3.5 s	853 kB	0s (no-store)
/docs/getting_the_models/	200	HTML	1.4 s	746 kB	0s (no-store)
/docs/deployment/cost-comparison/	200	HTML	1.7 s	796 kB	0s (no-store)
/docs/model-cards-and-prompt-formats/other-models/	200	HTML	3.5 s	890 kB	0s (no-store)
/docs/model-cards-and-prompt-formats/llama-guard-4/	200	HTML	1.2 s	868 kB	0s (no-store)
/docs/getting_the_models/meta/	200	HTML	1.2 s	753 kB	0s (no-store)
/docs/how-to-guides/prompting/	200	HTML	1.3 s	865 kB	0s (no-store)
/docs/integration-guides/langchain/	200	HTML	1.5 s	811 kB	0s (no-store)
/docs/getting-the-models/405b-partners/	200	HTML	1.4 s	820 kB	0s (no-store)
/docs/llama-everywhere/running-meta-llama-on-linux/	200	HTML	1.1 s	870 kB	0s (no-store)
/docs/deployment/a-b-testing/	200	HTML	1.3 s	1023 kB	0s (no-store)
/docs/how-to-guides/distillation/	200	HTML	1.5 s	797 kB	0s (no-store)
/docs/how-to-guides/responsible-use-guide-resources/	200	HTML	1.5 s	873 kB	0s (no-store)
/docs/how-to-guides/validation/	200	HTML	2.2 s	782 kB	0s (no-store)
/docs/community-support-and-resources/	200	HTML	3.1 s	1007 kB	0s (no-store)
/docs/integration-guides/llamaindex/	200	HTML	3.1 s	792 kB	0s (no-store)
/docs/model-cards-and-prompt-formats/	200	HTML	1.5 s	812 kB	0s (no-store)
/docs/integration-guides/	200	HTML	1.4 s	778 kB	0s (no-store)
/docs/how-to-guides/fine-tuning/	200	HTML	1.2 s	908 kB	0s (no-store)
/docs/deployment/private-cloud-deployment/	200	HTML	1.9 s	919 kB	0s (no-store)
/docs/deployment/infrastructure-migration/	200	HTML	1.9 s	852 kB	0s (no-store)
/docs/deployment/versioning/	200	HTML	1.6 s	878 kB	0s (no-store)
/docs/deployment/regulated-industry-self-hosting/	200	HTML	1.7 s	846 kB	0s (no-store)
/docs/how-to-guides/vision-capabilities/	200	HTML	1.4 s	942 kB	0s (no-store)
/docs/getting-the-models/1b3b-partners/	200	HTML	1.5 s	771 kB	0s (no-store)
/docs/model-cards-and-prompt-formats/llama3_3/	200	HTML	1 s	840 kB	0s (no-store)
/docs/getting-the-models/hugging-face/	200	HTML	2 s	787 kB	0s (no-store)
/docs/model-cards-and-prompt-formats/llama4/	200	HTML	1.5 s	962 kB	0s (no-store)
/docs/how-to-guides/	200	HTML	1.7 s	756 kB	0s (no-store)
/docs/how-to-guides/evaluations/	200	HTML	1.9 s	938 kB	0s (no-store)
/docs/deployment/accelerator-management/	200	HTML	1.4 s	923 kB	0s (no-store)
/docs/llama-everywhere/running-meta-llama-on-windows/	200	HTML	1.3 s	885 kB	0s (no-store)
/docs/deployment/	200	HTML	4.4 s	736 kB	0s (no-store)
/docs/how-to-guides/evaluations	301	Redirect	124 ms	171 B	None
/docs/how-to-guides/distillation	301	Redirect	160 ms	173 B	None
/docs/deployment/private-cloud-deployment	301	Redirect	122 ms	191 B	None
/docs/deployment/regulated-industry-self-hosting	301	Redirect	142 ms	205 B	None
/docs/deployment/production-deployment-pipelines/	200	HTML	5.1 s	1018 kB	0s (no-store)
/docs/deployment/cost-projection/	200	HTML	1.6 s	880 kB	0s (no-store)
/docs/deployment/accelerator-management	301	Redirect	165 ms	187 B	None
/docs/how-to-guides/quantization	301	Redirect	174 ms	173 B	None
/docs/deployment/infrastructure-migration	301	Redirect	178 ms	191 B	None
/docs/model-cards-and-prompt-formats/llama-guard-3/	200	HTML	1.1 s	874 kB	0s (no-store)
/docs/deployment/versioning	301	Redirect	168 ms	163 B	None
/docs/how-to-guides/fine-tuning	301	Redirect	179 ms	171 B	None
/docs/model-cards-and-prompt-formats/prompt-guard/	200	HTML	1.2 s	773 kB	0s (no-store)
/docs/deployment/cost-comparison	301	Redirect	236 ms	173 B	None
/docs/deployment/production-deployment-pipelines	301	Redirect	143 ms	205 B	None
/docs/deployment/autoscaling	301	Redirect	153 ms	165 B	None
/docs/model-cards-and-prompt-formats/llama3_1/	200	HTML	1.1 s	1013 kB	0s (no-store)
/docs/deployment/cost-projection	301	Redirect	147 ms	173 B	None
/docs/how-to-guides/prompting	301	Redirect	169 ms	167 B	None
/docs/model-cards-and-prompt-formats/meta-llama-3/	200	HTML	1.2 s	816 kB	0s (no-store)
/docs/deployment/security-in-production	301	Redirect	191 ms	187 B	None
/docs/model-cards-and-prompt-formats/llama3_2	301	Redirect	179 ms	199 B	None
/docs/model-cards-and-prompt-formats/llama3_1	301	Redirect	158 ms	199 B	None
/docs/deployment/cost_projection	301	Redirect	158 ms	173 B	None
/docs/deployment/a-b-testing	301	Redirect	280 ms	165 B	None
/docs/model-cards-and-prompt-formats/llama3_2/	200	HTML	1.8 s	977 kB	0s (no-store)
/docs/deployment/cost_projection/	404	HTML	366 ms	228 kB	0s (no-store)
/docs/model-cards-and-prompt-formats/llama-guard-3	301	Redirect	169 ms	209 B	None
No rows found, please edit your search term.

(+) Show entire table

Best practices

Found 11 row(s).

Analysis name	OK	Notice	Warning	Critical
DOM depth (> 30)	1	0	44	0
Heading structure	78	1	6	5
Invalid inline SVGs	20	0	0	0
Duplicate inline SVGs (> 5 and > 1024 B)	20	0	0	0
Large inline SVGs (> 5120 B)	20	0	0	0
Non-clickable phone numbers	1	0	0	0
Title uniqueness (> 10%)	41	0	0	0
Description uniqueness (> 10%)	38	0	0	0
Brotli support	0	0	44	0
WebP support	0	0	1	0
AVIF support	0	0	1	0
No rows found, please edit your search term.

Large inline SVGs

No problems found.

Duplicate inline SVGs

No problems found.

Invalid inline SVGs

No problems found.

Missing quotes on attributes

No problems found.

DOM depth

Severity	Occurs	Detail	Affected URLs (max 5)
warning	38	The DOM depth exceeds the warning limit: 30. Found depth: 32.	URL 1, URL 2, URL 3, URL 4, URL 5
warning	5	The DOM depth exceeds the warning limit: 30. Found depth: 30.	URL 1, URL 2, URL 3, URL 4, URL 5
warning	1	The DOM depth exceeds the warning limit: 30. Found depth: 35.	/docs/community-support-and-resources/

Heading structure

Severity	Occurs	Detail	Affected URLs (max 5)
critical	8	Multiple <h1> headings found.	URL 1, URL 2, URL 3, URL 4, URL 5
warning	5	Heading structure is skipping levels: found an <h6> after an <h3>.	/docs/how-to-guides/vision-capabilities/
warning	3	Heading structure is skipping levels: found an <h4> after an <h2>.	URL 1, URL 2, URL 3
warning	2	Heading structure is skipping levels: found an <h3> after an <h1>.	URL 1, URL 2
notice	1	No headings found in the HTML content.	/docs/deployment/cost_projection/

Non-clickable phone numbers

No problems found.

Title uniqueness

No problems found.

Description uniqueness

No problems found.

Accessibility

Analysis name	OK	Warning
Missing html lang attribute	1	0
Missing roles	0	1
Missing image alt attributes	206	1
Missing aria labels	24	10

Valid HTML

No problems found.

Missing image alt attributes

Severity	Occurs	Detail	Affected URLs (max 5)
warning	1	<img class="x193iq5w x1ypdohk" *** >	/docs/deployment/cost-comparison/

Missing form labels

No problems found.

Missing aria labels

Found 11 row(s).

Severity	Occurs	Detail	Affected URLs (max 5)
warning	3036	<a class="x1i10hfl xjbqb8w x1ejq31n x18oe1m7 x1sy0etr xstzfhl x972fbf x10w94by x1qhh985 x14e42zd x9f619 x1ypdohk xt0psk2 x3ct3a4 xdj266r x14z9mp xat24cr x1lziwak xexx8yu xyri2b x18d9i69 x1c1uobl x16tdsg8 x1hl2dhg xggy1nq x1a2a7pz x1heor9g xkrqix3 x1sur9pj x1s688f" *** >	URL 1, URL 2, URL 3, URL 4, URL 5
warning	1276	<a class="x1i10hfl x1qjc9v5 xjbqb8w xjqpnuy xc5r6h4 xqeqjp1 x1phubyo x13fuv20 x18b5jzi x1q0q8m5 x1t7ytsu x972fbf x10w94by x1qhh985 x14e42zd x9f619 x1ypdohk xdl72j9 xdt5ytf x2lah0s x3ct3a4 xdj266r x14z9mp xat24cr x1lziwak x2lwn1j xeuugli xexx8yu xyri2b x18d9i69 x1c1uobl x16tdsg8 xggy1nq x1ja2u2z x1t137rt xt0psk2 x1hl2dhg xt0b8zv x1heor9g x1uhb9sk" *** >	URL 1, URL 2, URL 3, URL 4, URL 5
warning	499	<a class="x1i10hfl x1qjc9v5 xjbqb8w xjqpnuy xc5r6h4 xqeqjp1 x1phubyo x13fuv20 x18b5jzi x1q0q8m5 x1t7ytsu x972fbf x10w94by x1qhh985 x14e42zd x9f619 x1ypdohk xdl72j9 xdt5ytf x2lah0s x3ct3a4 xdj266r x14z9mp xat24cr x1lziwak x2lwn1j xeuugli xexx8yu xyri2b x18d9i69 x1c1uobl x1n2onr6 x16tdsg8 xggy1nq x1ja2u2z x1t137rt xt0psk2 x1hl2dhg xt0b8zv x1heor9g" *** >	URL 1, URL 2, URL 3, URL 4, URL 5
warning	459	<a class="xawggmj" *** >	URL 1, URL 2, URL 3, URL 4, URL 5
warning	56	<a class="x1i10hfl x1qjc9v5 xjbqb8w x1ypdohk xdl72j9 xdt5ytf x2lah0s x3ct3a4 xdj266r x14z9mp xat24cr x1lziwak x2lwn1j xeuugli x16tdsg8 xggy1nq x1ja2u2z x1t137rt x1hl2dhg x1lku1pv x13fuv20 x18b5jzi x1q0q8m5 x1t7ytsu xamhcws x1alpsbp xlxy82 xyumdvf x1ekkm8c x1143rjc xum4auv xj21bgg x9f619 x2wh2y9 x1n2onr6 x87ps6o x889kno x1a8lsjc xz7312c x1o5r3ls xwji4o3 x1g2r6go xawggmj x155eyjv xrpzz58 x102p2p6 xus0ocu x1rg5ohu" *** >	URL 1, URL 2, URL 3, URL 4, URL 5
warning	44	<a class="x1i10hfl x1qjc9v5 x1ypdohk xdl72j9 xdt5ytf x2lah0s x3ct3a4 xdj266r x14z9mp xat24cr x1lziwak x2lwn1j xeuugli x16tdsg8 xggy1nq x1ja2u2z x1t137rt x1hl2dhg x1lku1pv x13fuv20 x18b5jzi x1q0q8m5 x1t7ytsu xamhcws x1alpsbp xlxy82 xyumdvf x1ekkm8c x1143rjc xum4auv xj21bgg x9f619 x2wh2y9 x1n2onr6 x87ps6o x889kno x1a8lsjc xz7312c x1o5r3ls xwji4o3 x1g2r6go x2keuyw x1cgww1y x1v8p93f x1o3jo1z x16stqrj xv5lvn5 x1rg5ohu" *** >	URL 1, URL 2, URL 3, URL 4, URL 5
warning	19	<a class="x1i10hfl x1qjc9v5 xjbqb8w xjqpnuy xc5r6h4 xqeqjp1 x1phubyo x13fuv20 x18b5jzi x1q0q8m5 x1t7ytsu x972fbf x10w94by x1qhh985 x14e42zd x9f619 x1ypdohk xdl72j9 xdt5ytf x2lah0s x3ct3a4 xdj266r x14z9mp xat24cr x1lziwak x2lwn1j xeuugli xexx8yu xyri2b x18d9i69 x1c1uobl x16tdsg8 xggy1nq x1ja2u2z x1t137rt x1hl2dhg x1lku1pv x1n2onr6 x1rg5ohu" *** >	URL 1, URL 2
warning	10	<a class="x1i10hfl x1qjc9v5 xjbqb8w xjqpnuy xc5r6h4 xqeqjp1 x1phubyo x13fuv20 x18b5jzi x1q0q8m5 x1t7ytsu x972fbf x10w94by x1qhh985 x14e42zd x9f619 x1ypdohk xdl72j9 xdt5ytf x2lah0s x3ct3a4 xdj266r x14z9mp xat24cr x1lziwak x2lwn1j xeuugli xexx8yu xyri2b x18d9i69 x1c1uobl x16tdsg8 xggy1nq x1ja2u2z x1t137rt xt0psk2 x1bvjpef xt0b8zv xawggmj x1uhb9sk" *** >	URL 1, URL 2, URL 3, URL 4
warning	9	<a class="x1i10hfl x1qjc9v5 xjbqb8w xjqpnuy xc5r6h4 xqeqjp1 x1phubyo x13fuv20 x18b5jzi x1q0q8m5 x1t7ytsu x972fbf x10w94by x1qhh985 x14e42zd x9f619 x1ypdohk xdl72j9 xdt5ytf x2lah0s x3ct3a4 xdj266r x14z9mp xat24cr x1lziwak x2lwn1j xeuugli xexx8yu xyri2b x18d9i69 x1c1uobl x16tdsg8 xggy1nq x1ja2u2z x1t137rt xt0psk2 x1hl2dhg x1lku1pv x1vg082b x1rujz1s xm5vtmc x1oh3tsa x1uc5f31 x1hteqrk xn1wy4v x1k03ns3 xozxopv xevc6a3 xj4x6ey x51bakv xr3domn x1ld7pqj x156ezf xyo5chj xilbgsz x34cus5 x1847d35 xbh3umz x1heor9g x1uhb9sk" *** >	/docs/overview/
warning	2	<a class="" *** >	/docs/how-to-guides/vision-capabilities/
warning	1	<a class="x1i10hfl x1qjc9v5 x1ypdohk xdl72j9 xdt5ytf x2lah0s x3ct3a4 xdj266r x14z9mp xat24cr x1lziwak x2lwn1j xeuugli x16tdsg8 xggy1nq x1ja2u2z x1t137rt x1hl2dhg x1lku1pv x13fuv20 x18b5jzi x1q0q8m5 x1t7ytsu xamhcws x1alpsbp xlxy82 xyumdvf x1ekkm8c x1143rjc xum4auv xj21bgg x9f619 x2wh2y9 x1n2onr6 x87ps6o xyinxu5 x1g2khh7 x162tt16 x5zjp28 xwji4o3 x1g2r6go x2keuyw x1cgww1y x1v8p93f x1o3jo1z x16stqrj xv5lvn5 x1rg5ohu" *** >	/docs/overview/
No rows found, please edit your search term.

Missing roles

Severity	Occurs	Detail	Affected URLs (max 5)
warning	44	<main class="x1n2onr6 x1vjfegm xwnajww" id="mdc-main-content">	URL 1, URL 2, URL 3, URL 4, URL 5

Missing html lang attribute

No problems found.

Security

Found 11 row(s).

Header	OK	Notice	Warning	Critical	Recommendation
Referrer-Policy	0	0	45	0	Referrer-Policy header is not set. It controls referrer header sharing and enhances privacy and security.
Set-Cookie	0	0	45	0
Access-Control-Allow-Origin	0	45	0	0	Access-Control-Allow-Origin is set to 'https://www.llama.com' which allows this origin to access the resource.
Feature-Policy	0	45	0	0	Feature-Policy header is not set but Permissions-Policy is set. That's enough.
Strict-Transport-Security	45	0	0	0
X-Frame-Options	45	0	0	0
X-XSS-Protection	45	0	0	0
X-Content-Type-Options	45	0	0	0
Content-Security-Policy	45	0	0	0
Permissions-Policy	45	0	0	0
Server	45	0	0	0	Server header is not set or empty. This is recommended.
No rows found, please edit your search term.

Security headers

Severity	Occurs	Detail	Affected URLs (max 5)
warning	45	Referrer-Policy header is not set. It controls referrer header sharing and enhances privacy and security.	URL 1, URL 2, URL 3, URL 4, URL 5
warning	45	Set-Cookie header for 'locale' does not have 'HttpOnly' flag. Attacker can steal the cookie using XSS. Consider using 'HttpOnly' when cookie is not used by JavaScript.	URL 1, URL 2, URL 3, URL 4, URL 5
notice	45	Access-Control-Allow-Origin is set to 'https://www.llama.com' which allows this origin to access the resource.	URL 1, URL 2, URL 3, URL 4, URL 5
notice	45	Server header is not set or empty. This is recommended.	URL 1, URL 2, URL 3, URL 4, URL 5
notice	45	Feature-Policy header is not set but Permissions-Policy is set. That's enough.	URL 1, URL 2, URL 3, URL 4, URL 5

TOP non-unique titles

Count 🔽	Title
3	Docs & Resources \| Llama AI
2	Getting the models

TOP non-unique descriptions

Count 🔽	Description
4	.
3	Explore Llama's full potential with our comprehensive documentation and resources. Drive developer productivity and innovation.
2	You can get the Meta Llama models directly from Meta or through Hugging Face or Kaggle.

SEO metadata

Found 44 row(s).

URL 🔼	Indexing	Title	H1	Description
/docs/community-support-and-resources/	Allowed	Guides, Docs & Videos \| Llama Resources	Meta and Community Resources	Discover Llama resources, including cookbooks, videos, and guides, to help you build, fine-tune, and optimize your models for success.
/docs/deployment/	Allowed	Docs & Resources \| Llama AI	Deployment	Explore Llama's full potential with our comprehensive documentation and resources. Drive developer productivity and innovation.
/docs/deployment/a-b-testing/	Allowed	A/B testing Llama in production \| Deployment guides	A / B testing Llama in production	Learn how to design, implement, and analyze A/B tests for Llama applications to optimize performance and make data-driven decisions, and discover best practices to avoid common pitfalls.
/docs/deployment/accelerator-management/	Allowed	Accelerator management \| Deployment guides	Accelerator management	Learn how to effectively deploy and manage accelerators for large language models, including selecting the right hardware, optimizing usage, and navigating cloud-hosted and on-premises strategies. Understand key considerations for maximizing utilization, minimizing idle time, and reducing costs for LLM inference workloads.
/docs/deployment/autoscaling/	Allowed	Autoscaling self-hosted Llama models \| Deployment guides	Autoscaling self-hosted Llama models	Learn how to optimize autoscaling for self-hosted Llama models by understanding key metrics, implementation strategies, and operational best practices to balance GPU costs and inference performance. Discover how to deploy a simple autoscaled Llama inference service using managed platforms and production-ready configurations.
/docs/deployment/cost-comparison/	Allowed	Cost comparison and basic deployment patterns \| Deployment guides	Cost comparison and basic deployment patterns	Learn how to compare different compute options for Llama inference and determine the most cost-effective infrastructure choice for your specific use case by evaluating key metrics and variables that drive infrastructure costs. Understand the cost components and pricing considerations for managed hosted APIs, serverless GPU, GPU rental, and bare metal ownership to make an informed decision.
/docs/deployment/cost-projection/	Allowed	Cost projection \| Deployment guides	Cost projection	Learn how to project the total cost of operating large language models (LLMs), including costs for hosted APIs, cloud deployments, and on-premises deployments, and understand key cost drivers and optimization strategies. Understand the cost implications of different LLM use cases, such as chatbots, summarization, and code generation, to make informed decisions about LLM deployment.
/docs/deployment/infrastructure-migration/	Allowed	Infrastructure migration \| Deployment guides	Infrastructure migration	Learn how to migrate from OpenAI to Llama models through a four-phase approach: assessment, proof of concept, gradual migration, and optimization. Discover key considerations, tools, and best practices for a successful infrastructure migration.
/docs/deployment/private-cloud-deployment/	Allowed	Private cloud deployment for Llama models \| Deployment guides	Private cloud deployment for Llama models	Learn how to deploy Llama models in private cloud environments across AWS, Azure, and GCP with advanced security features and configuration options, and optimize costs through resource tagging, monitoring, and optimization strategies. Understand various architecture patterns, including VPC-isolated, cross-region, and multi-cloud deployments, for different organizational needs.
/docs/deployment/production-deployment-pipelines/	Allowed	Production pipelines for Llama deployments \| Deployment guides	Production pipelines for Llama deployments	Learn how to design and implement production-ready Llama pipelines that handle data processing, model training, evaluation, deployment, and monitoring at scale, and optimize resource utilization and cost efficiency. Discover strategies for building resilient pipelines, including data ingestion, validation, preprocessing, distributed fine-tuning, and deployment techniques.
/docs/deployment/regulated-industry-self-hosting/	Allowed	Self-hosted Llama deployments for regulated industries \| Deployment guides	Self-hosted Llama deployments for regulated industries	Learn how to deploy self-hosted Llama models for regulated industries like healthcare, ensuring data sovereignty and compliance with regulations such as HIPAA and GDPR. Discover various architecture patterns, model selection, security controls, and deployment approaches to optimize performance and maintain regulatory compliance.
/docs/deployment/security-in-production/	Allowed	Security in production \| Deployment guides	Security in production	Learn how to implement a comprehensive security framework for Llama applications in production environments, covering infrastructure, data, application, and operational security to mitigate risks and protect AI systems. Follow a defense-in-depth strategy to secure your Llama deployment.
/docs/deployment/versioning/	Allowed	Versioning, updates and migration \| Deployment guides	Versioning, updates and migration	Learn how to effectively migrate between different versions of Llama models by understanding their versioning system and implementing a strategic migration plan, and discover how to evaluate performance, assess trade-offs, and optimize prompts for optimal results. Understand Llama's versioning, compare model capabilities using model cards, and plan your migration with a step-by-step guide.
/docs/getting-the-models/1b3b-partners/	Allowed	Edge partners \| Getting the models	Edge partners	Get Llama 3.2 1B and 3B from our partners.
/docs/getting-the-models/405b-partners/	Allowed	Cloud partners \| Getting the models	Cloud partners	Get Llama 3.1 405B from our partners.
/docs/getting-the-models/hugging-face/	Allowed	Hugging Face \| Getting the models	Hugging Face	To obtain the models from Hugging Face (HF), sign into your account at huggingface.co/meta-llama. Select the model you want.
/docs/getting-the-models/kaggle/	Allowed	Kaggle \| Getting the models	Kaggle	To obtain the models from Kaggle–including the HF versions of the models–sign into your account at kaggle.com/organizations/metaresearch/models.
/docs/getting_the_models/	Allowed	Getting the models	Getting the models	You can get the Meta Llama models directly from Meta or through Hugging Face or Kaggle.
/docs/getting_the_models/meta/	Allowed	Getting the models	Getting the models	You can get the Meta Llama models directly from Meta or through Hugging Face or Kaggle.
/docs/how-to-guides/	Allowed	Docs & Resources \| Llama AI	How-to guides	Explore Llama's full potential with our comprehensive documentation and resources. Drive developer productivity and innovation.
/docs/how-to-guides/distillation/	Allowed	Distillation \| How-to guides	Distillation	Learn how to distill a large language model into a smaller one using synthetic data generation and fine-tuning, and evaluate the distilled model's performance and efficiency. Discover techniques for distillation, including hard features, logit targets, and feature targets, to transfer knowledge from a teacher model to a student model.
/docs/how-to-guides/evaluations/	Allowed	Evaluations \| How-to guides	Evaluations	Learn how to systematically evaluate your Llama-powered application using a combination of automated and manual techniques, including code-based tests, Llama-as-judge, and human evaluation, to measure performance and drive improvements. Follow best practices to build a reliable evaluation framework and avoid common pitfalls.
/docs/how-to-guides/fine-tuning/	Allowed	Fine-tuning \| How-to guides	Fine-tuning	Learn how to fine-tune Llama models using various methods, including LoRA, QLoRA, and reinforcement learning, to improve performance on specific tasks and adapt to domain-specific knowledge. Fine-tune Llama using libraries like PyTorch's torchtune, Hugging Face peft, Axolotl, and Unsloth.
/docs/how-to-guides/prompting/	Allowed	Prompt engineering \| How-to Guides	Prompt engineering	Learn how to improve the performance of large language models through prompt engineering by crafting effective prompts and using techniques such as zero-shot and few-shot prompting, role-based prompts, and retrieval-augmented generation. Discover how to reduce hallucinations and improve model accuracy by providing clear context, instructions, and examples.
/docs/how-to-guides/quantization/	Allowed	Quantization and performance optimization \| How-to guides	Quantization and performance optimization	Learn how to optimize machine learning models using quantization techniques, such as weight-only, dynamic, and static quantization, and explore various frameworks and tools like PyTorch and Hugging Face to improve model performance and reduce memory usage. Understand the trade-offs between model accuracy, latency, and cost to make informed decisions for your specific use case.
/docs/how-to-guides/responsible-use-guide-resources/	Allowed	Developer use guide resources \| How-to guides	Developer use guide resources	We are committed to supporting our community in building Llama applications responsibly. As part of that commitment, we provide this Developer Use Guide that outlines best practices in the context of Responsible GenAI.
/docs/how-to-guides/validation/	Allowed	Validation \| How-to guides	Validation	In this section, we are going to cover different ways to measure and ultimately validate Llama so it's possible to determine the improvements provided by different fine tuning techniques.
/docs/how-to-guides/vision-capabilities/	Allowed	Vision Capabilities \| How-to guides	Llama Vision Capabilities	Llama models can now take Image + Text inputs, enabling you to interact with the model in new ways. Multimodal inputs result in conversations that are more natural and flexible.
/docs/integration-guides/	Allowed	Integration guides	Integration guides	.
/docs/integration-guides/langchain/	Allowed	LangChain \| Integration guides	LangChain	LangChain is an open source framework for building LLM powered applications. It implements common abstractions and higher-level APIs to make the app building process easier, so you don't need to call LLM from scratch.
/docs/integration-guides/llamaindex/	Allowed	LlamaIndex \| Integration guides	LlamaIndex	LlamaIndex is another popular open source framework for building LLM applications. Like LangChain, LlamaIndex can also be used to build RAG applications by easily integrating data not built-in the LLM with LLM.
/docs/llama-everywhere/running-meta-llama-on-linux/	Allowed	Running Llama on Linux \| Llama Everywhere	Running Llama on Linux	With a Linux setup having a GPU with a minimum of 16GB VRAM, you should be able to load the 8B Llama models in fp16 locally. If you have an Nvidia GPU, you can confirm your setup by opening the Terminal and typing nvidia-smi (NVIDIA System Management Interface), which will show you the GPU you have, the VRAM available, and other useful information about your setup.
/docs/llama-everywhere/running-meta-llama-on-windows/	Allowed	Running Llama on Windows \| Llama Everywhere	Running Llama on Windows	For this demo, we will be using a Windows OS machine with a RTX 4090 GPU. If you have an Nvidia GPU, you can confirm your setup by opening the Terminal and typing nvidia-smi(NVIDIA System Management Interface), which will show you the GPU you have, the VRAM available, and other useful information about your setup.
/docs/model-cards-and-prompt-formats/	Allowed	Llama Models	Llama Models	To correctly prompt each Llama model, please closely follow the formats described in the following sections.
/docs/model-cards-and-prompt-formats/llama-guard-3/	Allowed	Llama Guard 3 \| Model Cards and Prompt formats	Introduction	Llama Guard 3 builds on the capabilities introduced in Llama Guard 2, adding three new categories.
/docs/model-cards-and-prompt-formats/llama-guard-4/	Allowed	Llama Guard 4 \| Model Cards and Prompt formats	Introduction	Llama Guard 4 builds on the capabilities introduced in Llama Guard 3 and supports both the Llama 4 and Llama 3 model lines.
/docs/model-cards-and-prompt-formats/llama3_1/	Allowed	Llama 3.1 \| Model Cards and Prompt formats	Llama 3.1	Llama 3.1 - the most capable open model.
/docs/model-cards-and-prompt-formats/llama3_2/	Allowed	Llama 3.2 \| Model Cards and Prompt formats	Llama 3.2	.
/docs/model-cards-and-prompt-formats/llama3_3/	Allowed	Llama 3.3 \| Model Cards and Prompt formats	Llama 3.3	.
/docs/model-cards-and-prompt-formats/llama4/	Allowed	Llama 4 \| Model Cards and Prompt formats	Llama 4	Technical details and prompt guidance for Llama 4 Maverick and Llama 4 Scout
/docs/model-cards-and-prompt-formats/meta-llama-3/	Allowed	Llama 3 \| Model Cards and Prompt formats	Llama 3	Special Tokens used with Llama 3. A prompt should contain a single system message, can contain multiple alternating user and assistant messages, and always ends with the last user message followed by the assistant header.
/docs/model-cards-and-prompt-formats/other-models/	Allowed	Other Models \| Model Cards and Prompt formats	Other Models	.
/docs/model-cards-and-prompt-formats/prompt-guard/	Allowed	Llama Prompt Guard 2 \| Model Cards and Prompt formats	Llama Prompt Guard 2	LLM-powered applications are susceptible to prompt attacks, which are prompts intentionally designed to subvert the intended behavior of the LLM as specified by the developer.
/docs/overview/	Allowed	Docs & Resources \| Llama AI	Get started with Llama	Explore Llama's full potential with our comprehensive documentation and resources. Drive developer productivity and innovation.
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URL 🔼	OG Title	OG Description	OG Image	Twitter Title	Twitter Description	Twitter Image
/docs/community-support-and-resources/	Guides, Docs & Videos \| Llama Resources	Discover Llama resources, including cookbooks, videos, and guides, to help you build, fine-tune, and optimize your models for success.	GKtafBvy07VJ5DkEAG6Ing2qnAJsbj0JAABZ	Guides, Docs & Videos \| Llama Resources	Discover Llama resources, including cookbooks, videos, and guides, to help you build, fine-tune, and optimize your models for success.	/static-resource/796726469044102/
/docs/deployment/	Docs & Resources \| Llama AI	Explore Llama's full potential with our comprehensive documentation and resources. Drive developer productivity and innovation.	GKtafBvy07VJ5DkEAG6Ing2qnAJsbj0JAABZ	Docs & Resources \| Llama AI	Explore Llama's full potential with our comprehensive documentation and resources. Drive developer productivity and innovation.	/static-resource/1697643527569263/
/docs/deployment/a-b-testing/	A/B testing Llama in production \| Deployment guides	Learn how to design, implement, and analyze A/B tests for Llama applications to optimize performance and make data-driven decisions, and discover best practices to avoid common pitfalls.	957549148994009	A/B testing Llama in production \| Deployment guides	Learn how to design, implement, and analyze A/B tests for Llama applications to optimize performance and make data-driven decisions, and discover best practices to avoid common pitfalls.	/static-resource/833361905876879/
/docs/deployment/accelerator-management/	Accelerator management \| Deployment guides	Learn how to effectively deploy and manage accelerators for large language models, including selecting the right hardware, optimizing usage, and navigating cloud-hosted and on-premises strategies. Understand key considerations for maximizing utilization, minimizing idle time, and reducing costs for LLM inference workloads.	957549148994009	Accelerator management \| Deployment guides	Learn how to effectively deploy and manage accelerators for large language models, including selecting the right hardware, optimizing usage, and navigating cloud-hosted and on-premises strategies....	/static-resource/1340825667292477/
/docs/deployment/autoscaling/	Autoscaling self-hosted Llama models \| Deployment guides	Learn how to optimize autoscaling for self-hosted Llama models by understanding key metrics, implementation strategies, and operational best practices to balance GPU costs and inference performance. Discover how to deploy a simple autoscaled Llama inference service using managed platforms and production-ready configurations.	957549148994009	Autoscaling self-hosted Llama models \| Deployment guides	Learn how to optimize autoscaling for self-hosted Llama models by understanding key metrics, implementation strategies, and operational best practices to balance GPU costs and inference performance....	/static-resource/756160304052713/
/docs/deployment/cost-comparison/	Cost comparison and basic deployment patterns \| Deployment guides	Learn how to compare different compute options for Llama inference and determine the most cost-effective infrastructure choice for your specific use case by evaluating key metrics and variables that drive infrastructure costs. Understand the cost components and pricing considerations for managed hosted APIs, serverless GPU, GPU rental, and bare metal ownership to make an informed decision.	957549148994009	Cost comparison and basic deployment patterns \| Deployment guides	Learn how to compare different compute options for Llama inference and determine the most cost-effective infrastructure choice for your specific use case by evaluating key metrics and variables that...	/static-resource/1542987983817801/
/docs/deployment/cost-projection/	Cost projection \| Deployment guides	Learn how to project the total cost of operating large language models (LLMs), including costs for hosted APIs, cloud deployments, and on-premises deployments, and understand key cost drivers and optimization strategies. Understand the cost implications of different LLM use cases, such as chatbots, summarization, and code generation, to make informed decisions about LLM deployment.	957549148994009	Cost projection \| Deployment guides	Learn how to project the total cost of operating large language models (LLMs), including costs for hosted APIs, cloud deployments, and on-premises deployments, and understand key cost drivers and...	/static-resource/1160411996017906/
/docs/deployment/infrastructure-migration/	Infrastructure migration \| Deployment guides	Learn how to migrate from OpenAI to Llama models through a four-phase approach: assessment, proof of concept, gradual migration, and optimization. Discover key considerations, tools, and best practices for a successful infrastructure migration.	957549148994009	Infrastructure migration \| Deployment guides	Learn how to migrate from OpenAI to Llama models through a four-phase approach: assessment, proof of concept, gradual migration, and optimization. Discover key considerations, tools, and best...	/static-resource/715525204183130/
/docs/deployment/private-cloud-deployment/	Private cloud deployment for Llama models \| Deployment guides	Learn how to deploy Llama models in private cloud environments across AWS, Azure, and GCP with advanced security features and configuration options, and optimize costs through resource tagging, monitoring, and optimization strategies. Understand various architecture patterns, including VPC-isolated, cross-region, and multi-cloud deployments, for different organizational needs.	957549148994009	Private cloud deployment for Llama models \| Deployment guides	Learn how to deploy Llama models in private cloud environments across AWS, Azure, and GCP with advanced security features and configuration options, and optimize costs through resource tagging,...	/static-resource/866827655678421/
/docs/deployment/production-deployment-pipelines/	Production pipelines for Llama deployments \| Deployment guides	Learn how to design and implement production-ready Llama pipelines that handle data processing, model training, evaluation, deployment, and monitoring at scale, and optimize resource utilization and cost efficiency. Discover strategies for building resilient pipelines, including data ingestion, validation, preprocessing, distributed fine-tuning, and deployment techniques.	957549148994009	Production pipelines for Llama deployments \| Deployment guides	Learn how to design and implement production-ready Llama pipelines that handle data processing, model training, evaluation, deployment, and monitoring at scale, and optimize resource utilization and...	/static-resource/1913192665925982/
/docs/deployment/regulated-industry-self-hosting/	Self-hosted Llama deployments for regulated industries \| Deployment guides	Learn how to deploy self-hosted Llama models for regulated industries like healthcare, ensuring data sovereignty and compliance with regulations such as HIPAA and GDPR. Discover various architecture patterns, model selection, security controls, and deployment approaches to optimize performance and maintain regulatory compliance.	957549148994009	Self-hosted Llama deployments for regulated industries \| Deployment...	Learn how to deploy self-hosted Llama models for regulated industries like healthcare, ensuring data sovereignty and compliance with regulations such as HIPAA and GDPR. Discover various architecture...	/static-resource/792642866814105/
/docs/deployment/security-in-production/	Security in production \| Deployment guides	Learn how to implement a comprehensive security framework for Llama applications in production environments, covering infrastructure, data, application, and operational security to mitigate risks and protect AI systems. Follow a defense-in-depth strategy to secure your Llama deployment.	957549148994009	Security in production \| Deployment guides	Learn how to implement a comprehensive security framework for Llama applications in production environments, covering infrastructure, data, application, and operational security to mitigate risks and...	/static-resource/1543028563526844/
/docs/deployment/versioning/	Versioning, updates and migration \| Deployment guides	Learn how to effectively migrate between different versions of Llama models by understanding their versioning system and implementing a strategic migration plan, and discover how to evaluate performance, assess trade-offs, and optimize prompts for optimal results. Understand Llama's versioning, compare model capabilities using model cards, and plan your migration with a step-by-step guide.	957549148994009	Versioning, updates and migration \| Deployment guides	Learn how to effectively migrate between different versions of Llama models by understanding their versioning system and implementing a strategic migration plan, and discover how to evaluate...	/static-resource/815277647549048/
/docs/getting-the-models/1b3b-partners/	Edge partners \| Getting the models	Get Llama 3.2 1B and 3B from our partners.	https://scontent-hel3-1.xx.fbcdn.net/v/t39.2365-6/461134507_1189552…Sr-zj-ONHzgleILFiong&oe=69C8B718			/static-resource/511812748244662/
/docs/getting-the-models/405b-partners/	Cloud partners \| Getting the models	Get Llama 3.1 405B from our partners.	https://scontent-hel3-1.xx.fbcdn.net/v/t39.2365-6/423162455_1781617…T903LRYwoRTkjq_M-x9Q&oe=69C89726			/static-resource/469044049097139/
/docs/getting-the-models/hugging-face/	Hugging Face \| Getting the models	To obtain the models from Hugging Face (HF), sign into your account at huggingface.co/meta-llama. Select the model you want.	GKtafBvy07VJ5DkEAG6Ing2qnAJsbj0JAABZ	Hugging Face \| Getting the models	To obtain the models from Hugging Face (HF), sign into your account at huggingface.co/meta-llama. Select the model you want.	/static-resource/301629139485490/
/docs/getting-the-models/kaggle/	Kaggle \| Getting the models	To obtain the models from Kaggle–including the HF versions of the models–sign into your account at kaggle.com/organizations/metaresearch/models.	GKtafBvy07VJ5DkEAG6Ing2qnAJsbj0JAABZ	Kaggle \| Getting the models	To obtain the models from Kaggle–including the HF versions of the models–sign into your account at kaggle.com/organizations/metaresearch/models.	/static-resource/741941001261407/
/docs/getting_the_models/	Getting the models	You can get the Meta Llama models directly from Meta or through Hugging Face or Kaggle.	GKtafBvy07VJ5DkEAG6Ing2qnAJsbj0JAABZ	Getting the models	You can get the Meta Llama models directly from Meta or through Hugging Face or Kaggle.	/static-resource/1575897086306939/
/docs/getting_the_models/meta/	Getting the models	You can get the Meta Llama models directly from Meta or through Hugging Face or Kaggle.	GKtafBvy07VJ5DkEAG6Ing2qnAJsbj0JAABZ	Getting the models	You can get the Meta Llama models directly from Meta or through Hugging Face or Kaggle.	/static-resource/350740148072527/
/docs/how-to-guides/	Docs & Resources \| Llama AI	Explore Llama's full potential with our comprehensive documentation and resources. Drive developer productivity and innovation.	GKtafBvy07VJ5DkEAG6Ing2qnAJsbj0JAABZ	Docs & Resources \| Llama AI	Explore Llama's full potential with our comprehensive documentation and resources. Drive developer productivity and innovation.	/static-resource/1663893387757515/
/docs/how-to-guides/distillation/	Distillation \| How-to guides	Learn how to distill a large language model into a smaller one using synthetic data generation and fine-tuning, and evaluate the distilled model's performance and efficiency. Discover techniques for distillation, including hard features, logit targets, and feature targets, to transfer knowledge from a teacher model to a student model.	957549148994009	Distillation \| How-to guides	Learn how to distill a large language model into a smaller one using synthetic data generation and fine-tuning, and evaluate the distilled model's performance and efficiency. Discover techniques for...	/static-resource/1364056098681493/
/docs/how-to-guides/evaluations/	Evaluations \| How-to guides	Learn how to systematically evaluate your Llama-powered application using a combination of automated and manual techniques, including code-based tests, Llama-as-judge, and human evaluation, to measure performance and drive improvements. Follow best practices to build a reliable evaluation framework and avoid common pitfalls.	957549148994009	Evaluations \| How-to guides	Learn how to systematically evaluate your Llama-powered application using a combination of automated and manual techniques, including code-based tests, Llama-as-judge, and human evaluation, to...	/static-resource/2211464519339272/
/docs/how-to-guides/fine-tuning/	Fine-tuning \| How-to guides	Learn how to fine-tune Llama models using various methods, including LoRA, QLoRA, and reinforcement learning, to improve performance on specific tasks and adapt to domain-specific knowledge. Fine-tune Llama using libraries like PyTorch's torchtune, Hugging Face peft, Axolotl, and Unsloth.	957549148994009	Fine-tuning \| How-to guides	Learn how to fine-tune Llama models using various methods, including LoRA, QLoRA, and reinforcement learning, to improve performance on specific tasks and adapt to domain-specific knowledge....	/static-resource/2418965311624049/
/docs/how-to-guides/prompting/	Prompt engineering \| How-to Guides	Learn how to improve the performance of large language models through prompt engineering by crafting effective prompts and using techniques such as zero-shot and few-shot prompting, role-based prompts, and retrieval-augmented generation. Discover how to reduce hallucinations and improve model accuracy by providing clear context, instructions, and examples.	957549148994009	Prompt engineering \| How-to Guides	Learn how to improve the performance of large language models through prompt engineering by crafting effective prompts and using techniques such as zero-shot and few-shot prompting, role-based...	/static-resource/1177707916944344/
/docs/how-to-guides/quantization/	Quantization and performance optimization \| How-to guides	Learn how to optimize machine learning models using quantization techniques, such as weight-only, dynamic, and static quantization, and explore various frameworks and tools like PyTorch and Hugging Face to improve model performance and reduce memory usage. Understand the trade-offs between model accuracy, latency, and cost to make informed decisions for your specific use case.	957549148994009	Quantization and performance optimization \| How-to guides	Learn how to optimize machine learning models using quantization techniques, such as weight-only, dynamic, and static quantization, and explore various frameworks and tools like PyTorch and Hugging...	/static-resource/7859481227403771/
/docs/how-to-guides/responsible-use-guide-resources/	Developer use guide resources \| How-to guides	We are committed to supporting our community in building Llama applications responsibly. As part of that commitment, we provide this Responsible Use Guide that outlines best practices in the context of Responsible GenAI.	957549148994009	Developer use guide resources \| How-to guides	We are committed to supporting our community in building Llama applications responsibly. As part of that commitment, we provide this Developer Use Guide that outlines best practices in the context of...	/static-resource/953939169750773/
/docs/how-to-guides/validation/	Validation \| How-to guides	In this section, we are going to cover different ways to measure and ultimately validate Llama so it's possible to determine the improvements provided by different fine tuning techniques.	957549148994009	Validation \| How-to guides	In this section, we are going to cover different ways to measure and ultimately validate Llama so it's possible to determine the improvements provided by different fine tuning techniques.	/static-resource/1181955742815795/
/docs/how-to-guides/vision-capabilities/	Vision Capabilities \| How-to guides	Llama models can now take Image + Text inputs, enabling you to interact with the model in new ways. Multimodal inputs result in conversations that are more natural and flexible.	957549148994009	Vision Capabilities \| How-to guides	Llama models can now take Image + Text inputs, enabling you to interact with the model in new ways. Multimodal inputs result in conversations that are more natural and flexible.	/static-resource/26709729488675037/
/docs/integration-guides/	Integration guides	.	957549148994009	Integration guides	.	/static-resource/795874906074273/
/docs/integration-guides/langchain/	LangChain \| Integration guides	LangChain is an open source framework for building LLM powered applications. It implements common abstractions and higher-level APIs to make the app building process easier, so you don't need to call LLM from scratch.	GKtafBvy07VJ5DkEAG6Ing2qnAJsbj0JAABZ	LangChain \| Integration guides	LangChain is an open source framework for building LLM powered applications. It implements common abstractions and higher-level APIs to make the app building process easier, so you don't need to call...	/static-resource/1123126622362634/
/docs/integration-guides/llamaindex/	LlamaIndex \| Integration guides	LlamaIndex is another popular open source framework for building LLM applications. Like LangChain, LlamaIndex can also be used to build RAG applications by easily integrating data not built-in the LLM with LLM.	957549148994009	LlamaIndex \| Integration guides	LlamaIndex is another popular open source framework for building LLM applications. Like LangChain, LlamaIndex can also be used to build RAG applications by easily integrating data not built-in the...	/static-resource/951682856284412/
/docs/llama-everywhere/running-meta-llama-on-linux/	Running Llama on Linux \| Llama Everywhere	With a Linux setup having a GPU with a minimum of 16GB VRAM, you should be able to load the 8B Llama models in fp16 locally. If you have an Nvidia GPU, you can confirm your setup by opening the Terminal and typing nvidia-smi (NVIDIA System Management Interface), which will show you the GPU you have, the VRAM available, and other useful information about your setup.	957549148994009	Running Llama on Linux \| Llama Everywhere	With a Linux setup having a GPU with a minimum of 16GB VRAM, you should be able to load the 8B Llama models in fp16 locally. If you have an Nvidia GPU, you can confirm your setup by opening the...	/static-resource/310860238675445/
/docs/llama-everywhere/running-meta-llama-on-windows/	Running Llama on Windows \| Llama Everywhere	For this demo, we will be using a Windows OS machine with a RTX 4090 GPU. If you have an Nvidia GPU, you can confirm your setup by opening the Terminal and typing nvidia-smi(NVIDIA System Management Interface), which will show you the GPU you have, the VRAM available, and other useful information about your setup.	957549148994009	Running Llama on Windows \| Llama Everywhere	For this demo, we will be using a Windows OS machine with a RTX 4090 GPU. If you have an Nvidia GPU, you can confirm your setup by opening the Terminal and typing nvidia-smi(NVIDIA System Management...	/static-resource/985102496573087/
/docs/model-cards-and-prompt-formats/	Model Cards & Prompt formats	To correctly prompt each Llama model, please closely follow the formats described in the following sections.	GKtafBvy07VJ5DkEAG6Ing2qnAJsbj0JAABZ	Llama Models	To correctly prompt each Llama model, please closely follow the formats described in the following sections.	/static-resource/7326338707447918/
/docs/model-cards-and-prompt-formats/llama-guard-3/	Llama Guard 3 \| Model Cards and Prompt formats	Llama Guard 3 builds on the capabilities introduced in Llama Guard 2, adding three new categories.	GKtafBvy07VJ5DkEAG6Ing2qnAJsbj0JAABZ	Llama Guard 3 \| Model Cards and Prompt formats	Llama Guard 3 builds on the capabilities introduced in Llama Guard 2, adding three new categories.	/static-resource/1032845481809561/
/docs/model-cards-and-prompt-formats/llama-guard-4/	Llama Guard 4 \| Model Cards and Prompt formats	Llama Guard 4 builds on the capabilities introduced in Llama Guard 3 and supports both the Llama 4 and Llama 3 model lines.	GKtafBvy07VJ5DkEAG6Ing2qnAJsbj0JAABZ	Llama Guard 4 \| Model Cards and Prompt formats	Llama Guard 4 builds on the capabilities introduced in Llama Guard 3 and supports both the Llama 4 and Llama 3 model lines.	/static-resource/2695146084209786/
/docs/model-cards-and-prompt-formats/llama3_1/	Llama 3.1 \| Model Cards and Prompt formats	Llama 3.1 - the most capable open model.	GKtafBvy07VJ5DkEAG6Ing2qnAJsbj0JAABZ	Llama 3.1 \| Model Cards and Prompt formats	Llama 3.1 - the most capable open model.	/static-resource/471739745818571/
/docs/model-cards-and-prompt-formats/llama3_2/	Llama 3.2 \| Model Cards and Prompt formats	.	GKtafBvy07VJ5DkEAG6Ing2qnAJsbj0JAABZ	Llama 3.2 \| Model Cards and Prompt formats	.	/static-resource/903729188343068/
/docs/model-cards-and-prompt-formats/llama3_3/	Llama 3.3 \| Model Cards and Prompt formats	.	GKtafBvy07VJ5DkEAG6Ing2qnAJsbj0JAABZ	Llama 3.3 \| Model Cards and Prompt formats	.	/static-resource/500119133054262/
/docs/model-cards-and-prompt-formats/llama4/	Llama 4 \| Model Cards and Prompt formats	Technical details and prompt guidance for Llama 4 Maverick and Llama 4 Scout	GKtafBvy07VJ5DkEAG6Ing2qnAJsbj0JAABZ	Llama 4 \| Model Cards and Prompt formats	Technical details and prompt guidance for Llama 4 Maverick and Llama 4 Scout	/static-resource/655094010560439/
/docs/model-cards-and-prompt-formats/meta-llama-3/	Llama 3 \| Model Cards and Prompt formats	Special Tokens used with Llama 3. A prompt should contain a single system message, can contain multiple alternating user and assistant messages, and always ends with the last user message followed by the assistant header.	GKtafBvy07VJ5DkEAG6Ing2qnAJsbj0JAABZ	Llama 3 \| Model Cards and Prompt formats	Special Tokens used with Llama 3. A prompt should contain a single system message, can contain multiple alternating user and assistant messages, and always ends with the last user message followed by...	/static-resource/7358974024222113/
/docs/model-cards-and-prompt-formats/other-models/	Other Models \| Model Cards and Prompt formats	.	GKtafBvy07VJ5DkEAG6Ing2qnAJsbj0JAABZ	Other Models \| Model Cards and Prompt formats	.	/static-resource/448754381380888/
/docs/model-cards-and-prompt-formats/prompt-guard/	Llama Prompt Guard 2 \| Model Cards and Prompt formats	LLM-powered applications are susceptible to prompt attacks, which are prompts intentionally designed to subvert the intended behavior of the LLM as specified by the developer.	GKtafBvy07VJ5DkEAG6Ing2qnAJsbj0JAABZ	Llama Prompt Guard 2 \| Model Cards and Prompt formats	LLM-powered applications are susceptible to prompt attacks, which are prompts intentionally designed to subvert the intended behavior of the LLM as specified by the developer.	/static-resource/456324347367723/
/docs/overview/	Docs & Resources \| Llama AI	Explore Llama's full potential with our comprehensive documentation and resources. Drive developer productivity and innovation.	GKtafBvy07VJ5DkEAG6Ing2qnAJsbj0JAABZ	Docs & Resources \| Llama AI	Explore Llama's full potential with our comprehensive documentation and resources. Drive developer productivity and innovation.	/static-resource/1338827570121596/
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Heading structure

Found 44 row(s).

Heading structure	Count	Errors 🔽	URL
<h1> Other Models <h3> Llama 3 <h3> Llama Guard 2 <h3> Code Llama 70B <h3> Llama Guard 1 <h3> Code Llama <h3> Llama 2	7	6	/docs/model-cards-and-prompt-formats/other-models/
<h1> Meta and Community Resources <h1> Meta Resources <h3> Videos <h1> Community Resources	4	4	/docs/community-support-and-resources/
<h1> Llama 3.2 <h2> Introduction <h1> Llama 3.2 Quantized Models (1B/3B) <h2> Introduction <h2> Fast, Compact, Accurate–and Safe <h2> Getting the Models <h2> Using the Models <h2> Drop-In Replacement for BF16 Models <h2> Quantization Techniques <h3> Quantization-Aware Training and LoRA <h3> SpinQuant <h3> Common Configuration Settings <h3> References <h1> Llama 3.2 Lightweight Models (1B/3B) <h2> Model Card (1B/3B) <h2> Inference with lightweight models <h2> Prompt Template <h2> Code Interpreter <h2> Tool Calling (1B/3B) <h3> Function definitions in the system prompt <h3> Function definitions and query in the user prompt <h1> Llama 3.2 Vision models (11B/90B) <h2> Model Card <h2> Vision Model Architecture <h2> Vision Model Inputs and Outputs <h2> Prompt Template <h3> Special Tokens <h3> Supported Roles <h3> Base Model Prompt <h3> Instruct Model Prompt <h2> Code Interpreter and Tool Calling	31	4	/docs/model-cards-and-prompt-formats/llama3_2/
<h1> Get started with Llama <h2> What's new <h1> Get the models <h2> Llama 4 Scout <h2> Llama 4 Maverick <h2> Llama Guard 4 <h2> Find us on GitHub <h2> Other topics in this guide	8	2	/docs/overview/
<h1> How-to guides <h1> Develop with Llama <h2> Prompt engineering <h2> Fine-tuning <h2> Quantization <h2> Distillation <h2> Evaluations <h2> Validation <h2> Vision Capabilities <h2> Responsible Use Guide	10	2	/docs/how-to-guides/
<h1> Deployment <h1> Production deployment with Llama <h2> Private Cloud Deployment <h2> Production Deployment Pipelines <h2> Infrastructure Migration <h2> Model Versioning and Migration <h2> Accelerator Management <h2> Autoscaling and Resource Optimization <h2> Self-Hosting for Regulated Industries <h2> Security in Production <h2> Cost Projection and Optimization <h2> Comparing Costs <h2> A/B Testing and Experimentation	13	2	/docs/deployment/
<h1> Quantization and performance optimization <h2> What is quantization? <h2> Overview of quantization methods <h3> Post-training weight-only quantization <h3> Post-training dynamic quantization <h3> Post-training static quantization <h3> Quantization-aware training <h2> Quantization frameworks and tools <h3> PyTorch quantization with TorchAO <h3> HF-supported quantization <h2> Tradeoffs of quantization <h2> Performance optimization <h3> Batch sizes <h3> KV caching <h3> Fused kernels <h3> Balancing cost, latency, and quality <h2> Additional resources	17	0	/docs/how-to-guides/quantization/
<h1> Security in production <h2> Introduction <h3> Scope and assumptions <h2> Important concepts <h3> Zero Trust <h3> Least privilege <h3> Defense-in-depth <h3> LLM Data and IP Leakage Vectors <h2> Infrastructure security: Securing the perimeter <h3> Network isolation and segmentation <h3> Secure ingress and egress control <h3> Hardening compute environments <h3> Infrastructure as Code (IaC) security <h2> Data security: Protecting the lifecycle <h3> Encryption at rest <h3> Encryption in transit <h3> Key management lifecycle <h3> Sensitive data detection and masking <h2> Application security: Hardening the stack <h3> Role-Based Access Control (RBAC) patterns <h3> Mitigating LLM-specific threats (OWASP Top 10) <h2> Operational security: Maintaining vigilance <h3> Comprehensive audit logging <h3> Real-time security monitoring and alerting <h3> Supply chain security <h3> Incident response planning <h2> Security checklist <h3> Infrastructure security <h3> Data security <h3> Application security <h3> Operational security <h2> Considerations for Public-Facing Applications <h2> Additional resources	33	0	/docs/deployment/security-in-production/
<h1> Kaggle	1	0	/docs/getting-the-models/kaggle/
<h1> Autoscaling self-hosted Llama models <h2> Overview <h2> Understanding self-hosted autoscaling <h3> Key metrics and scaling triggers <h3> Model sizing and GPU requirements <h3> Cold start optimization strategies <h2> Implementation patterns <h3> Horizontal scaling architectures <h3> Vertical scaling patterns <h2> Framework considerations <h3> Choosing an inference framework <h3> Quantization trade-offs <h2> Production operations <h3> Monitoring and observability <h3> Cost optimization strategies <h3> Troubleshooting common issues <h2> Practical examples <h3> Basic autoscaling setup <h3> Production deployment checklist <h2> Next steps	20	0	/docs/deployment/autoscaling/
<h1> Getting the models	1	0	/docs/getting_the_models/
<h1> Cost comparison and basic deployment patterns <h2> Overview <h2> Basic deployment options <h2> Cost components and pricing considerations <h2> Calculating effective costs <h2> Making your decision <h3> Scale and workload considerations <h3> Latency and privacy requirements <h3> Optimization strategies <h2> Related guides	10	0	/docs/deployment/cost-comparison/
<h1> Introduction <h2> Introduction <h2> Download the Model <h2> Model Card <h2> Architecture <h2> Llama Guard 4 and Llama 3 <h2> Image Support <h2> Prompt format <h2> Prompt Sections <h2> Response	10	0	/docs/model-cards-and-prompt-formats/llama-guard-4/
<h1> Getting the models <h2> Meta	2	0	/docs/getting_the_models/meta/
<h1> Prompt engineering <h2> What is prompt engineering? <h2> Crafting effective prompts <h3> Stylization <h3> Formatting <h3> Restrictions <h2> Prompting techniques <h3> Zero- and few-shot prompting <h3> Role-based prompts <h3> Chain-of-thought prompting <h3> Self-consistency <h3> Retrieval-augmented generation <h3> Limiting extraneous tokens <h3> Program-aided language models <h3> Reducing hallucinations	15	0	/docs/how-to-guides/prompting/
<h1> LangChain	1	0	/docs/integration-guides/langchain/
<h1> Cloud partners <h2> AWS <h2> Azure <h2> Databricks <h2> Fireworks AI <h2> Google Cloud Platform <h2> Groq <h2> NVIDIA <h2> IBM watsonx <h2> Scale AI <h2> Snowflake <h2> Together AI	12	0	/docs/getting-the-models/405b-partners/
<h1> Running Llama on Linux <h2> Introduction to llama models <h2> Running Llama on Linux <h3> Setup <h3> Getting the weights <h3> Running the model	6	0	/docs/llama-everywhere/running-meta-llama-on-linux/
<h1> A/B testing Llama in production <h2> What you will learn <h2> 1. Designing your A/B test <h3> Formulating a hypothesis <h3> Choosing what to test (the variants) <h3> Defining key metrics <h3> Setting the Minimum Detectable Effect (MDE) <h3> Sample size and duration planning <h2> 2. Implementing the framework <h3> Reference architecture for A/B testing <h3> Deploying Llama variants <h3> Traffic splitting strategies <h3> Essential logging schema for Llama applications <h2> 3. Analyzing results and making decisions <h3> Using Llama-as-judge at scale (recommended) <h3> Statistical significance <h3> Making the Decision: The Trade-off Matrix <h3> Closing the Loop: Integrating A/B Testing with Offline Evals <h2> 4. Best practices and common pitfalls <h3> Key recommendations (dos) <h3> Common pitfalls (don'ts) <h2> Next steps	22	0	/docs/deployment/a-b-testing/
<h1> Distillation <h2> What is distillation? <h2> How distillation works <h2> Distillation techniques <h3> Synthetic data generation <h3> Advanced distillation techniques <h2> Evaluating distilled models <h3> Knowledge transfer metrics <h3> Practical benefits <h2> Additional resources	10	0	/docs/how-to-guides/distillation/
<h1> Developer use guide resources <h2> Introduction <h2> Determine use case <h2> Model-level alignment <h3> 1. Prepare data <h3> 2. Train the model <h3> 3. Evaluate and improve performance <h2> System-level alignment <h2> Transparency	9	0	/docs/how-to-guides/responsible-use-guide-resources/
<h1> Validation <h2> Quantitative techniques <h2> Holdout <h2> Standard Evaluation tools <h2> Interpreting Loss and Perplexity <h2> Qualitative techniques	6	0	/docs/how-to-guides/validation/
<h1> LlamaIndex	1	0	/docs/integration-guides/llamaindex/
<h1> Llama Models <h2> Llama 3.1 Enhancements <h2> Prompt Formatting <h2> Upgrading your application from Llama 3 to Llama 3.1	4	0	/docs/model-cards-and-prompt-formats/
<h1> Integration guides	1	0	/docs/integration-guides/
<h1> Fine-tuning <h2> What is fine-tuning? <h2> When to fine-tune Llama <h2> Fine-tuning overview <h2> Fine-tuning methods <h3> Full parameter fine-tuning <h3> Parameter efficient fine-tuning <h3> Reinforcement learning from human feedback (RLHF) <h3> Reinforcement learning from verifiable rewards (RLVR) <h2> How to approach fine-tuning <h2> Experiment tracking <h2> Fine-tuning libraries <h3> Managed fine-tuning <h3> PyTorch torchtune <h3> Third party libraries <h2> Additional resources	16	0	/docs/how-to-guides/fine-tuning/
<h1> Private cloud deployment for Llama models <h2> Overview <h2> Architecture patterns for private cloud <h3> VPC-isolated deployment <h3> Cross-region deployment <h3> Multi-cloud deployment <h3> Architecture patterns comparison <h2> AWS private deployment <h3> Private Network <h3> Identity and Access <h3> Encryption and Key Management <h3> Managed Model Serving <h2> Azure private deployment <h3> Private Network <h3> Identity and Access <h3> Encryption and Key Management <h3> Managed Model Serving <h2> GCP private deployment <h3> Private Network <h3> Identity and Access <h3> Encryption and Key Management <h3> Managed Model Serving <h2> Cloud differences summary <h2> Security and compliance implementation <h3> Data encryption patterns <h3> Audit logging architecture <h3> Network security controls <h2> High availability and disaster recovery <h3> Multi-zone deployment <h3> Backup and recovery procedures <h2> Cost management and optimization <h3> Resource tagging strategy <h3> Cost monitoring and optimization <h2> Additional resources	34	0	/docs/deployment/private-cloud-deployment/
<h1> Infrastructure migration <h2> Overview <h2> Migration methodology <h3> Phase 1: Assessment and planning <h3> Phase 2: Proof of concept <h3> Phase 3: Gradual migration <h3> Phase 4: Optimization and scaling (ongoing) <h2> Risk mitigation strategies <h2> Measuring success <h2> Tools and resources <h3> Essential migration tools <h2> Next steps	12	0	/docs/deployment/infrastructure-migration/
<h1> Versioning, updates and migration <h2> Overview <h2> A general ontology of Llama releases <h3> Llama models vs Llama-based models <h3> Major Versions <h3> Minor Versions <h2> New releases <h3> Llama 4 line <h3> Llama 3.x line <h3> Understanding and comparing model capabilities <h2> When to migrate <h3> Performance considerations <h3> Accuracy vs latency trade-offs <h3> When to migrate to Llama 4 <h3> MoE considerations <h3> Cost considerations <h2> Migration playbook <h3> If it ain't broke don't fix it <h3> Planning your migration <h3> Implementation steps <h3> Common migration patterns <h2> Evaluation considerations <h3> Model-specific testing considerations <h3> Prompt optimization testing <h3> Evaluation metrics for model migrations <h3> Rollback criteria	26	0	/docs/deployment/versioning/
<h1> Self-hosted Llama deployments for regulated industries <h2> Overview <h2> Architecture patterns <h3> Air-gapped deployment <h3> Private network deployment <h3> Hybrid deployment <h2> Model selection and infrastructure <h3> Model sizing guide <h3> Inference server selection <h2> Security and compliance <h3> PHI detection and protection <h3> Audit logging architecture <h3> Data retention and encryption <h2> Deployment approach <h3> Containerized deployment <h3> Kubernetes orchestration <h3> High availability patterns <h2> Monitoring and operations <h3> Key metrics <h3> Compliance validation <h2> Additional resources	21	0	/docs/deployment/regulated-industry-self-hosting/
<h1> Llama Vision Capabilities <h2> Llama as a multimodal model <h3> Capabilities of Llama 3.2 <h2> How to use multimodal <h3> Vision Capability Showcase <h3> OCR and Document Summary in the Real World <h3> Rental Listing Writer (source) <h3> Estimating Calories in a Salad <h3> OCR in the real world <h3> Reading Instructions to use a Washer <h3> Complex QA over Document Scans <h3> OCR + Complex Sarcasm over document scans <h3> OCR in the real world <h3> Caption Writer (source) <h3> Humour Detection: Meta Employee holding the Llama 1 paper <h3> Used-Car Ad writer	16	0	/docs/how-to-guides/vision-capabilities/
<h1> Edge partners <h2> Arm <h2> Dell <h2> MediaTek <h2> Qualcomm	5	0	/docs/getting-the-models/1b3b-partners/
<h1> Llama 3.3 <h2> Introduction <h2> Model Card <h2> Download the Model <h2> Prompt Template <h3> Zero-shot function calling <h3> Notes <h3> Zero-shot function calling in user message <h3> Notes <h3> Builtin Tool Calling	10	0	/docs/model-cards-and-prompt-formats/llama3_3/
<h1> Hugging Face	1	0	/docs/getting-the-models/hugging-face/
<h1> Llama 4 <h2> Introduction <h2> Prompt Template <h3> Suggested System Prompt <h2> Llama 4 - Prompt Formats <h3> Roles <h3> Tokens <h2> Llama 4 Pretrained Model <h3> Text completion - Translation example <h2> Llama 4 Instruct Model <h3> 1. Simple User and assistant conversation <h2> Image prompt format <h3> 1. Single image prompt format - small image (under 336 x 336 px) <h3> 2. Single image prompt format - larger images <h3> 3. Multiple images prompt format <h3> Zero shot function-calling - Python format <h3> Zero shot function-calling - JSON format	17	0	/docs/model-cards-and-prompt-formats/llama4/
<h1> Evaluations <h2> A practical example: Query intent classification <h3> Prerequisites <h3> Prepare the evaluation data <h3> Define the classification function <h3> Run the evaluation <h2> Evaluations deep dive <h3> Define evaluation criteria <h3> Prepare a dataset <h3> Executing evaluations <h3> Analyzing results and driving improvements <h3> Finding failure patterns <h3> Data-driven iteration <h2> Best practices <h3> Key recommendations <h3> Common pitfalls	16	0	/docs/how-to-guides/evaluations/
<h1> Accelerator management <h2> Introduction <h3> Purpose of the guide <h3> Scope and assumptions <h2> Accelerator selection <h3> What factors to consider when selecting an accelerator <h3> How to estimate model requirements <h3> Sharding and multi-node <h2> Cloud-hosted <h3> Instance types matrix <h3> A100 <h3> H100 <h3> H200 <h3> B200 <h3> Custom accelerators <h3> On-demand vs. reserved instances <h2> On-premises <h3> Accelerator selection and sourcing <h3> Lifecycle management <h3> Siting considerations <h2> Deploying on accelerators <h3> Numerical correctness <h3> Improving utilization <h2> Additional resources	24	0	/docs/deployment/accelerator-management/
<h1> Running Llama on Windows <h2> Setup <h2> Getting the weights <h2> Running the model	4	0	/docs/llama-everywhere/running-meta-llama-on-windows/
<h1> Production pipelines for Llama deployments <h2> Overview <h2> High level architecture <h3> Infrastructure requirements by stage <h3> Orchestration platform <h2> Data pipeline patterns <h3> Ingestion strategies <h3> Data validation framework <h3> Preprocessing at scale <h2> Model lifecycle: training, evaluation, and release <h3> Distributed fine-tuning architecture <h3> Evaluation and quality assurance <h3> Deployment strategies and rollouts <h2> Operating in production <h3> Monitoring and observability <h3> Resilience: error handling and recovery <h3> Cost optimization and capacity planning <h2> Additional resources	18	0	/docs/deployment/production-deployment-pipelines/
<h1> Cost projection <h2> Introduction <h3> Purpose of the guide <h3> Scope and assumptions <h2> Important concepts <h3> Input vs. output tokens <h3> KV-caching <h3> Reasoning/thinking tokens <h3> Vision models <h3> Latency considerations <h2> Cost drivers: hosted API <h3> Pricing models <h3> Rate limits and quotas <h3> Cloud costs <h2> Cost drivers: self-hosting <h3> GPU costs <h3> Infrastructure costs <h3> Utilization and forecasting <h3> Scaling strategies <h2> Hidden cost drivers <h3> Security and compliance <h3> Monitoring and observability <h3> Model updates and versioning <h2> Use case examples <h3> Chatbot <h3> Summarization <h3> Code generation <h2> Additional resources	28	0	/docs/deployment/cost-projection/
<h1> Introduction <h2> Introduction <h2> Llama 3.2 Update <h2> Image Support <h2> Use Llama Guard 3 8B for S14 Code Interpreter Abuse <h2> Prompt format <h2> Response	7	0	/docs/model-cards-and-prompt-formats/llama-guard-3/
<h1> Llama Prompt Guard 2 <h2> New Updated Model <h2> Download the Model <h2> Model Card <h2> Prompt Attacks and Llama Prompt Guard 2	5	0	/docs/model-cards-and-prompt-formats/prompt-guard/
<h1> Llama 3.1 <h2> Introduction <h2> Model Card <h2> Prompt Template <h3> Special Tokens <h3> Supported Roles <h3> Pretrained Model Prompt <h3> Instruct Model Prompt <h2> Code Interpreter <h2> Tool Calling (8B/70B/405B) <h3> User and assistant conversation <h3> Built in Python based tool calling <h3> JSON based tool calling <h3> User-defined Custom tool calling	14	0	/docs/model-cards-and-prompt-formats/llama3_1/
<h1> Llama 3 <h2> Model Card <h2> Special Tokens used with Llama 3 <h2> Llama 3 <h2> Llama 3 Instruct	5	0	/docs/model-cards-and-prompt-formats/meta-llama-3/
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External URLs

219 external URL(s)

Found 200 row(s).

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https://about.fb.com/news/	1	/docs/overview/
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https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai	1	/docs/how-to-guides/responsible-use-guide-resources/
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https://github.com/EleutherAI/lm-evaluation-harness	1	/docs/how-to-guides/responsible-use-guide-resources/
https://github.com/Macaronlin/LLaMA3-Quantization	1	/docs/how-to-guides/quantization/
https://github.com/ModelCloud/GPTQModel	1	/docs/how-to-guides/quantization/
https://github.com/NVIDIA/TensorRT-LLM	1	/docs/deployment/a-b-testing/
https://github.com/Vahe1994/AQLM?tab=readme-ov-file	1	/docs/how-to-guides/quantization/
https://github.com/axolotl-ai-cloud/axolotl	1	/docs/how-to-guides/fine-tuning/
https://github.com/facebookresearch/SpinQuant	1	/docs/how-to-guides/quantization/
https://github.com/facebookresearch/llama	1	/docs/overview/
https://github.com/facebookresearch/llama/	1	/docs/overview/
https://github.com/google/BIG-bench	1	/docs/how-to-guides/validation/
https://github.com/huggingface/alignment-handbook	1	/docs/how-to-guides/responsible-use-guide-resources/
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https://github.com/llamastack/llama-stack	1	/docs/overview/
https://github.com/meta-llama/PurpleLlama	1	/docs/how-to-guides/responsible-use-guide-resources/
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https://github.com/meta-llama/codellama/blob/main/llama/generation.py	1	/docs/model-cards-and-prompt-formats/other-models/
https://github.com/meta-llama/llama-agentic-system	1	/docs/model-cards-and-prompt-formats/
https://github.com/meta-llama/llama-cookbook	1	/docs/overview/
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https://huggingface.co/meta-llama/Meta-Llama-3.1-8B	1	/docs/getting-the-models/hugging-face/
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard	1	/docs/how-to-guides/validation/
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https://llama.meta.com/docs/getting-the-models/hugging-face	1	/docs/getting_the_models/
https://llama.meta.com/docs/getting-the-models/kaggle	1	/docs/getting_the_models/
https://llama.meta.com/docs/getting_the_models/meta	1	/docs/getting_the_models/
https://llama.meta.com/docs/integration-guides/llamaindex	1	/docs/integration-guides/
https://llama.meta.com/docs/integration-guides/meta-code-llama	1	/docs/integration-guides/
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https://mlcommons.org/2024/04/mlc-aisafety-v0-5-poc/	1	/docs/how-to-guides/responsible-use-guide-resources/
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https://owasp.org/www-project-top-10-for-large-language-model-applications/	1	/docs/deployment/security-in-production/
https://paperswithcode.com/dataset/wikitext-2	1	/docs/model-cards-and-prompt-formats/llama3_2/
https://platform.openai.com/docs/assistants/tools/function-calling/quickstart	1	/docs/model-cards-and-prompt-formats/llama3_1/
https://pypi.org/project/llama-cookbook/	1	/docs/how-to-guides/fine-tuning/
https://pytorch.org/blog/quantization-aware-training/	1	/docs/how-to-guides/quantization/
https://pytorch.org/docs/stable/generated/torch.nn.Linear.html	1	/docs/how-to-guides/quantization/
https://pytorch.org/get-started/locally/	1	/docs/llama-everywhere/running-meta-llama-on-windows/
https://pytorch.org/torchtune/stable/install.html	1	/docs/how-to-guides/fine-tuning/
https://pytorch.org/torchtune/stable/tutorials/e2e_flow.html	1	/docs/how-to-guides/fine-tuning/
https://pytorch.org/torchtune/stable/tutorials/llama3.html	1	/docs/how-to-guides/fine-tuning/
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https://scale.com/blog/meta-llama-3-1-launch-partner	1	/docs/getting-the-models/405b-partners/
https://scale.com/genai-platform	1	/docs/getting-the-models/405b-partners/
https://statsig.com/	1	/docs/deployment/a-b-testing/
https://together.ai/llama	1	/docs/deployment/a-b-testing/
https://towardsdatascience.com/importance-of-loss-function-in-machine-learning-eddaaec69519	1	/docs/how-to-guides/validation/
https://towardsdatascience.com/understanding-what-we-lose-b91e114e281b	1	/docs/how-to-guides/validation/
https://towardsdatascience.com/what-is-loss-function-1e2605aeb904	1	/docs/how-to-guides/validation/
https://twitter.com/aiatmeta/	1	/docs/overview/
https://unsplash.com/photos/a-couple-of-houses-on-a-beach-_6auLfoMDHk	1	/docs/how-to-guides/vision-capabilities/
https://unsplash.com/photos/brown-wooden-framed-white-padded-chair-…lants-inside-bedroom-psrloDbaZc8	1	/docs/how-to-guides/vision-capabilities/
https://urldefense.com/v3/__https:/aihub.qualcomm.com/__;!!Bt8RZUm9…v8dhc27zmBDwYvc5C97OvVxd1DoApd0$	1	/docs/getting-the-models/1b3b-partners/
https://urldefense.com/v3/__https:/huggingface.co/Arm__;!!Bt8RZUm9a…g1oj9nZYp2R9GkIhWJWHCSIQV7en9nc$	1	/docs/getting-the-models/1b3b-partners/
https://urldefense.com/v3/__https:/ollama.com/__;!!Bt8RZUm9aw!4qAAf…v8dhc27zmBDwYvc5C97OvVxdnHVjbsY$	1	/docs/getting-the-models/1b3b-partners/
https://urldefense.com/v3/__https:/www.arm.com/developer-hub/server…g1oj9nZYp2R9GkIhWJWHCSIQv5pCFh4$	1	/docs/getting-the-models/1b3b-partners/
https://urldefense.com/v3/__https:/www.arm.com/developer-hub/smartp…g1oj9nZYp2R9GkIhWJWHCSIQrCRq7KE$	1	/docs/getting-the-models/1b3b-partners/
https://urldefense.com/v3/__https:/www.arm.com/markets/artificial-i…g1oj9nZYp2R9GkIhWJWHCSIQKVc0Ij8$	1	/docs/getting-the-models/1b3b-partners/
https://urldefense.com/v3/__https:/www.arm.com/product-filter?famil…g1oj9nZYp2R9GkIhWJWHCSIQ5ZrRMMc$	1	/docs/getting-the-models/1b3b-partners/
https://urldefense.com/v3/__https:/www.arm.com/products/silicon-ip-…g1oj9nZYp2R9GkIhWJWHCSIQLIBbo1s$	1	/docs/getting-the-models/1b3b-partners/
https://wandb.ai/	1	/docs/how-to-guides/fine-tuning/
https://www.anyscale.com/blog/continuous-batching-llm-inference	1	/docs/community-support-and-resources/
https://www.cerebras.ai/	1	/docs/deployment/cost-projection/
https://www.cerebras.net/	1	/docs/how-to-guides/quantization/
https://www.databricks.com/blog/announcing-mosaic-ai-agent-framework-and-agent-evaluation	1	/docs/getting-the-models/405b-partners/
https://www.databricks.com/blog/efficient-fine-tuning-lora-guide-llms	1	/docs/community-support-and-resources/
You have reached the hard limit of 200 rows as a protection against very large output or exhausted memory. You can change this with `--rows-limit`.
No rows found, please edit your search term.

(+) Show entire table

TOP fastest URLs

No fast URLs faster than 1 second(s) found.

TOP slowest URLs

Found 20 row(s).

Time 🔽	Status	Slow URL
5.1 s	200	/docs/deployment/production-deployment-pipelines/
4.4 s	200	/docs/deployment/
3.5 s	200	/docs/model-cards-and-prompt-formats/other-models/
3.5 s	200	/docs/deployment/autoscaling/
3.1 s	200	/docs/community-support-and-resources/
3.1 s	200	/docs/integration-guides/llamaindex/
2.2 s	200	/docs/how-to-guides/validation/
2 s	200	/docs/getting-the-models/hugging-face/
1.9 s	200	/docs/how-to-guides/evaluations/
1.9 s	200	/docs/deployment/infrastructure-migration/
1.9 s	200	/docs/deployment/security-in-production/
1.9 s	200	/docs/deployment/private-cloud-deployment/
1.8 s	200	/docs/model-cards-and-prompt-formats/llama3_2/
1.7 s	200	/docs/deployment/regulated-industry-self-hosting/
1.7 s	200	/docs/how-to-guides/
1.7 s	200	/docs/deployment/cost-comparison/
1.6 s	200	/docs/deployment/cost-projection/
1.6 s	200	/docs/deployment/versioning/
1.5 s	200	/docs/model-cards-and-prompt-formats/llama4/
1.5 s	200	/docs/how-to-guides/distillation/
No rows found, please edit your search term.

Content types

Content type	URLs 🔽	Total size	Total time	Avg time	Status 20x	Status 30x	Status 40x
HTML	45	37 MB	78 s	1.8 s	44	0	1
Redirect	21	4 kB	3.5 s	168 ms	0	21	0

Content types (MIME types)

Content type	URLs 🔽	Total size	Total time	Avg time	Status 20x	Status 30x	Status 40x
text/html; charset="utf-8"	45	37 MB	78 s	1.8 s	44	0	1
text / html	21	4 kB	3.5 s	168 ms	0	21	0

Source domains

Domain	Totals	HTML	Redirect
www.llama.com	66 / 37MB / 82s	45 / 37MB / 78s	21 / 4kB / 3.5s

HTTP headers

Found 31 row(s).

Header 🔼	Occurs	Unique	Values preview	Min value	Max value
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Accept-Ch-Lifetime	45	1	4838400
Access-Control-Allow-Credentials	65	1	true
Access-Control-Allow-Methods	65	1	OPTIONS
Access-Control-Allow-Origin	65	1
Access-Control-Expose-Headers	65	1	X-FB-Debug, X-Loader-Length, Error-MID, X-FB-Trace-ID, X-Stack
Alt-Svc	66	1	h3=":443"; ma=86400
Cache-Control	45	1	private, no-cache, no-store, must-revalidate
Content-Length	21	-	[ignored generic values]	0 B	0 B
Content-Security-Policy	45	20+	[see values below]
Content-Type	66	2	text/html; charset="utf-8" (45) / text/html (21)
Cross-Origin-Opener-Policy	45	2	same-origin-allow-popups (39) / same-origin-allow-popups;report-to="coop_report" (6)
Cross-Origin-Resource-Policy	45	1	same-origin
Date	66	-	[ignored generic values]	2026-03-24	2026-03-24
Document-Policy	45	1	force-load-at-top, include-js-call-stacks-in-crash-reports
Expires	45	-	[ignored generic values]	2000-01-01	2000-01-01
Location	21	20+	[see values below]
Origin-Agent-Cluster	45	1	?1
Permissions-Policy	45	1	accelerometer=(), attribution-reporting=(), autoplay=(), bluetooth=(), camera=()…cking=();report-to="permissions_policy"
Pragma	45	1	no-cache
Proxy-Status	1	1	http_request_error; e_fb_vipaddr="AcNPcfFqiUrVOqk-n8VX7-f0w41EnRuXl…JcKFeVoGkoT9wN-FP_fMLVY0sKTsKjQ"
Report-To	45	2	[see values below]
Reporting-Endpoints	45	2	[see values below]
Set-Cookie	45	-	[ignored generic values]
Strict-Transport-Security	66	1	max-age=31536000; preload; includeSubDomains
Vary	65	2	Origin, Accept-Encoding (45) / Origin (20)
X-Content-Type-Options	45	1	nosniff
X-Fb-Connection-Quality	66	20+	[see values below]
X-Fb-Debug	66	20+	[see values below]
X-Frame-Options	45	1	DENY
X-XSS-Protection	45	1	0
No rows found, please edit your search term.

(+) Show entire table

HTTP header values

Found 108 row(s).

Header	Occurs	Value
Accept-Ch	45	viewport-width,dpr,Sec-CH-Prefers-Color-Scheme,Sec-CH-UA-Full-Version-List,Sec-CH-UA-Platform-Version,Sec-CH-UA-Model
Accept-Ch-Lifetime	45	4838400
Access-Control-Allow-Credentials	65	true
Access-Control-Allow-Methods	65	OPTIONS
Access-Control-Allow-Origin	65
Access-Control-Expose-Headers	65	X-FB-Debug, X-Loader-Length, Error-MID, X-FB-Trace-ID, X-Stack
Alt-Svc	66	h3=":443"; ma=86400
Cache-Control	45	private, no-cache, no-store, must-revalidate
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Location	1	/docs/deployment/cost-comparison/
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Location	1	/docs/deployment/private-cloud-deployment/
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(+) Show entire table

HTTP Caching by content type (only from crawlable domains)

Content type	Cache type	URLs 🔽	AVG lifetime	MIN lifetime	MAX lifetime
HTML	Cache-Control	45	-	-	-
Redirect	No cache headers	21	-	-	-

HTTP Caching by domain

Domain	Cache type	URLs 🔽	AVG lifetime	MIN lifetime	MAX lifetime
www.llama.com	Cache-Control	45	-	-	-
www.llama.com	No cache headers	21	-	-	-

HTTP Caching by domain and content type

Domain	Content type	Cache type	URLs 🔽	AVG lifetime	MIN lifetime	MAX lifetime
www.llama.com	HTML	Cache-Control	45	-	-	-
www.llama.com	Redirect	No cache headers	21	-	-	-

DNS info

DNS resolving tree
www.llama.com
llama.com
IPv4: llama.com.
IPv4: 157.240.205.1
IPv6: llama.com.
IPv6: 2a03:2880:f013:0:face:b00c:0:2

DNS server: 127.0.0.53

SSL/TLS info

Info	Text
Issuer	C = US, O = DigiCert Inc, CN = DigiCert Global G2 TLS RSA SHA256 2020 CA1
Subject	C = US, ST = California, L = Menlo Park, O = Meta Platforms, Inc., CN = llama.com
Valid from	Jan 9 00:00:00 2026 GMT (VALID already 74.7 day(s))
Valid to	Apr 1 23:59:59 2026 GMT (VALID still for 8.3 day(s))
Supported protocols	TLSv1.2, TLSv1.3
RAW certificate output	Certificate: Data: Version: 3 (0x2) Serial Number: 0c:1b:af:ea:12:66:f0:e8:5a:2b:6f:58:ba:a2:96:47 Signature Algorithm: sha256WithRSAEncryption Issuer: C = US, O = DigiCert Inc, CN = DigiCert Global G2 TLS RSA SHA256 2020 CA1 Validity Not Before: Jan 1 00:00:00 2026 GMT Not After : Apr 1 23:59:59 2026 GMT Subject: C = US, ST = California, L = Menlo Park, O = "Meta Platforms, Inc.", CN = llama.com Subject Public Key Info: Public Key Algorithm: rsaEncryption Public-Key: (2048 bit) Modulus: 00:d9:de:37:f5:45:76:90:1a:37:0b:f4:e3:f1:3e: d5:60:04:33:6e:3a:cc:81:aa:f6:20:6e:02:71:b8: 5e:02:b4:01:c3:8b:2b:f1:e8:f9:86:3f:03:df:28: 1e:44:46:8c:96:de:d2:fd:4f:77:3e:7a:71:0a:83: 5c:7b:71:3c:71:51:8d:2d:4b:34:a0:51:36:c8:29: f6:04:ef:ed:3d:ff:a5:73:a7:9e:b4:30:a6:88:28: 74:74:c4:88:64:9f:6f:94:55:07:ce:e5:54:49:26: 7e:f7:23:e2:60:58:60:44:c8:6f:87:47:51:ff:1b: ee:cf:f3:e8:c3:14:2e:95:52:98:7c:cd:62:67:ff: 21:5a:a4:68:89:c8:41:16:71:55:6b:1a:3f:ce:fb: b8:be:49:e4:1f:e4:58:09:57:28:62:d3:94:fd:34: 5f:27:99:09:0b:fa:6f:b6:ed:88:0c:93:dd:67:4d: 16:e3:08:88:d5:3c:f3:04:33:b5:1d:cc:a0:3b:ba: 40:a1:ff:0d:de:47:08:0b:1f:b6:27:6b:c2:87:5f: 57:ef:ad:98:43:d4:57:8f:48:77:fe:4e:5b:d7:f7: e5:e2:34:89:34:52:a1:0d:7d:0a:5a:af:47:1e:0e: be:cf:2d:76:b6:2d:77:fa:75:55:e3:61:d1:fb:b5: ba:61 Exponent: 65537 (0x10001) X509v3 extensions: X509v3 Authority Key Identifier: 74:85:80:C0:66:C7:DF:37:DE:CF:BD:29:37:AA:03:1D:BE:ED:CD:17 X509v3 Subject Key Identifier: CF:61:88:D2:A9:9E:67:EE:AF:60:2E:52:BA:5B:9F:0E:FD:DA:99:83 X509v3 Subject Alternative Name: DNS:llama.com, DNS:*.llama.com, DNS:www.llama.com X509v3 Certificate Policies: Policy: 2.23.140.1.2.2 CPS: http://www.digicert.com/CPS X509v3 Key Usage: critical Digital Signature, Key Encipherment X509v3 Extended Key Usage: TLS Web Server Authentication, TLS Web Client Authentication X509v3 CRL Distribution Points: Full Name: URI:http://crl3.digicert.com/DigiCertGlobalG2TLSRSASHA2562020CA1-1.crl Full Name: URI:http://crl4.digicert.com/DigiCertGlobalG2TLSRSASHA2562020CA1-1.crl Authority Information Access: OCSP - URI:http://ocsp.digicert.com CA Issuers - URI:http://cacerts.digicert.com/DigiCertGlobalG2TLSRSASHA2562020CA1-1.crt X509v3 Basic Constraints: critical CA:FALSE CT Precertificate SCTs: Signed Certificate Timestamp: Version : v1 (0x0) Log ID : 0E:57:94:BC:F3:AE:A9:3E:33:1B:2C:99:07:B3:F7:90: DF:9B:C2:3D:71:32:25:DD:21:A9:25:AC:61:C5:4E:21 Timestamp : Jan 1 00:58:39.219 2026 GMT Extensions: none Signature : ecdsa-with-SHA256 30:44:02:20:18:AA:B1:05:6E:83:DC:AC:54:9C:29:72: 36:4E:C7:F4:D4:3F:F2:C1:BC:38:00:FF:D1:20:F2:3C: FE:3B:61:D7:02:20:5F:03:8C:DA:97:2E:08:C4:CA:7C: 1E:9A:3A:7E:A8:6F:77:38:D0:AE:E4:66:07:98:AF:E4: 09:65:15:7F:4F:DC Signed Certificate Timestamp: Version : v1 (0x0) Log ID : 16:83:2D:AB:F0:A9:25:0F:0F:F0:3A:A5:45:FF:C8:BF: C8:23:D0:87:4B:F6:04:29:27:F8:E7:1F:33:13:F5:FA Timestamp : Jan 1 00:58:39.704 2026 GMT Extensions: none Signature : ecdsa-with-SHA256 30:44:02:20:68:6F:39:CE:0A:08:7D:A1:29:D9:FD:75: E4:3E:71:AC:51:86:60:2D:6A:4E:04:2A:36:3C:09:14: 84:5D:86:BA:02:20:74:87:CE:0C:8C:CC:F6:91:AE:27: B3:FF:16:97:66:C1:92:C9:10:84:E3:F0:EA:17:38:2E: 83:98:EC:90:D5:43 Signed Certificate Timestamp: Version : v1 (0x0) Log ID : 96:97:64:BF:55:58:97:AD:F7:43:87:68:37:08:42:77: E9:F0:3A:D5:F6:A4:F3:36:6E:46:A4:3F:0F:CA:A9:C6 Timestamp : Jan 1 00:58:39.250 2026 GMT Extensions: none Signature : ecdsa-with-SHA256 30:44:02:20:5C:EB:77:8C:33:A0:A0:A8:DB:60:D7:F5: 3C:BC:3D:6D:30:F2:52:51:7F:1A:F9:8F:E6:05:0D:C9: F1:D8:9B:36:02:20:2E:7A:E4:27:3F:50:6A:A9:9D:F1: 07:D9:B9:61:67:E9:F7:1C:97:92:3E:79:D0:09:2F:03: CF:2F:82:A5:C7:77 Signature Algorithm: sha256WithRSAEncryption Signature Value: 45:c0:5e:67:fd:77:e3:65:97:17:1d:7e:df:3d:3f:d6:bb:9f: ea:8d:d6:f7:fc:16:96:2b:7f:1d:35:b7:d6:b8:2e:3b:1c:f6: 77:4f:28:82:85:9c:53:96:bc:e5:2c:f3:71:86:0b:21:ab:13: 6c:2c:7a:56:bf:15:8d:f0:8c:2e:40:6b:7a:c8:65:c4:c9:00: 87:4c:8f:4b:31:fa:57:0f:7e:e7:93:d9:ec:e5:cf:05:af:ff: 6e:ed:c8:b4:eb:1b:6f:8a:6f:1f:eb:53:63:7f:e3:b6:0d:0f: 48:c7:80:59:44:08:65:97:d2:3f:c1:b6:9b:1e:54:f4:c2:de: 97:32:a3:66:4c:6a:03:95:5d:16:11:2c:7c:95:c8:dd:d8:aa: c8:82:a3:bf:10:2c:64:1f:e6:05:98:5d:cc:d1:6d:18:0c:60: 2c:c5:8b:67:9f:e8:55:93:ad:33:c7:a0:d7:40:32:b6:26:fa: fb:0e:1f:e2:01:55:0c:7a:23:d7:ae:f8:9d:a3:61:53:5c:bb: c5:05:e0:4d:b3:cb:ba:eb:a2:f3:2a:0e:d0:05:8c:28:34:cb: 9a:7b:a3:4c:3d:ed:0a:42:fc:5e:67:3d:d3:e9:21:e4:81:61: 0f:d0:d7:d3:1b:a9:59:90:7f:34:e3:a0:f0:c5:44:6f:e3:54: 94:00:85:1e
RAW protocols output	=== ssl2 === s_client: Unknown option: -ssl2 s_client: Use -help for summary. === ssl3 === s_client: Unknown option: -ssl3 s_client: Use -help for summary. === tls1 === 4027778D92730000:error:0A0000BF:SSL routines:tls_setup_handshake:no protocols available:../ssl/statem/statem_lib.c:104: CONNECTED(00000003) --- no peer certificate available --- No client certificate CA names sent --- SSL handshake has read 0 bytes and written 7 bytes Verification: OK --- New, (NONE), Cipher is (NONE) Secure Renegotiation IS NOT supported Compression: NONE Expansion: NONE No ALPN negotiated Early data was not sent Verify return code: 0 (ok) --- === tls1_1 === 4057821A33750000:error:0A0000BF:SSL routines:tls_setup_handshake:no protocols available:../ssl/statem/statem_lib.c:104: CONNECTED(00000003) --- no peer certificate available --- No client certificate CA names sent --- SSL handshake has read 0 bytes and written 7 bytes Verification: OK --- New, (NONE), Cipher is (NONE) Secure Renegotiation IS NOT supported Compression: NONE Expansion: NONE No ALPN negotiated Early data was not sent Verify return code: 0 (ok) --- === tls1_2 === depth=2 C = US, O = DigiCert Inc, OU = www.digicert.com, CN = DigiCert Global Root G2 verify return:1 depth=1 C = US, O = DigiCert Inc, CN = DigiCert Global G2 TLS RSA SHA256 2020 CA1 verify return:1 depth=0 C = US, ST = California, L = Menlo Park, O = "Meta Platforms, Inc.", CN = llama.com verify return:1 CONNECTED(00000003) --- Certificate chain 0 s:C = US, ST = California, L = Menlo Park, O = "Meta Platforms, Inc.", CN = llama.com i:C = US, O = DigiCert Inc, CN = DigiCert Global G2 TLS RSA SHA256 2020 CA1 a:PKEY: rsaEncryption, 2048 (bit); sigalg: RSA-SHA256 v:NotBefore: Jan 9 00:00:00 2026 GMT; NotAfter: Apr 1 23:59:59 2026 GMT 1 s:C = US, O = DigiCert Inc, CN = DigiCert Global G2 TLS RSA SHA256 2020 CA1 i:C = US, O = DigiCert Inc, OU = www.digicert.com, CN = DigiCert Global Root G2 a:PKEY: rsaEncryption, 2048 (bit); sigalg: RSA-SHA256 v:NotBefore: Mar 30 00:00:00 2021 GMT; NotAfter: Mar 29 23:59:59 2031 GMT 2 s:C = US, O = DigiCert Inc, OU = www.digicert.com, CN = DigiCert Global Root G2 i:C = US, O = DigiCert Inc, OU = www.digicert.com, CN = DigiCert High Assurance EV Root CA a:PKEY: rsaEncryption, 2048 (bit); sigalg: RSA-SHA256 v:NotBefore: Oct 29 00:00:00 2024 GMT; NotAfter: Nov 8 23:59:59 2031 GMT --- Server certificate -----BEGIN CERTIFICATE----- MIIG5jCCBc6gAwIBAgIQCzbiXdt1vmWJiIQ2+4/F9DANBgkqhkiG9w0BAQsFADBZ MQswCQYDVQQGEwJVUzEVMBMGA1UEChMMRGlnaUNlcnQgSW5jMTMwMQYDVQQDEypE aWdpQ2VydCBHbG9iYWwgRzIgVExTIFJTQSBTSEEyNTYgMjAyMCBDQTEwHhcNMjYw MTA5MDAwMDAwWhcNMjYwNDAxMjM1OTU5WjBqMQswCQYDVQQGEwJVUzETMBEGA1UE CBMKQ2FsaWZvcm5pYTETMBEGA1UEBxMKTWVubG8gUGFyazEdMBsGA1UEChMUTWV0 YSBQbGF0Zm9ybXMsIEluYy4xEjAQBgNVBAMTCWxsYW1hLmNvbTCCASIwDQYJKoZI hvcNAQEBBQADggEPADCCAQoCggEBALy5II/Do6LHuKZu1Xe2vvAx5VV4HQ14BMr9 LVflq4zaJ8iqCqvdQ5xEdGoySawwnplwGbIQc5B73XvFAX7uJjmk77ob8ZOuraaB Dx2sqTMQF0gs1WHMyLklMKeKaoC6ep6xhdSRiC3ml6YWWpNtgZY2geJJDxaFYjDC gfdRtNa21rJXKpWU07q4qEZin4G3vkPXEo86azOXYTDeGeHZWts/StbTpJAuinfZ BFkdwWNsgCOy3sMRWpJ0PJwr09yMdWHMGRa0UVBZw01rzULCcee+3sciXu7cofIu SyHs3xcZvKHnDPB2rI0ZzNFXtxWOs3YO7ebbnzmppFUR7x50PfMCAwEAAaOCA5cw ggOTMB8GA1UdIwQYMBaAFHSFgMBmx9833s+9KTeqAx2+7c0XMB0GA1UdDgQWBBR3 Tq38eQn5xMnzh0POCLpZzI0FczAwBgNVHREEKTAngglsbGFtYS5jb22CCyoubGxh bWEuY29tgg13d3cubGxhbWEuY29tMD4GA1UdIAQ3MDUwMwYGZ4EMAQICMCkwJwYI KwYBBQUHAgEWG2h0dHA6Ly93d3cuZGlnaWNlcnQuY29tL0NQUzAOBgNVHQ8BAf8E BAMCBaAwEwYDVR0lBAwwCgYIKwYBBQUHAwEwgZ8GA1UdHwSBlzCBlDBIoEagRIZC aHR0cDovL2NybDMuZGlnaWNlcnQuY29tL0RpZ2lDZXJ0R2xvYmFsRzJUTFNSU0FT SEEyNTYyMDIwQ0ExLTEuY3JsMEigRqBEhkJodHRwOi8vY3JsNC5kaWdpY2VydC5j b20vRGlnaUNlcnRHbG9iYWxHMlRMU1JTQVNIQTI1NjIwMjBDQTEtMS5jcmwwgYcG CCsGAQUFBwEBBHsweTAkBggrBgEFBQcwAYYYaHR0cDovL29jc3AuZGlnaWNlcnQu Y29tMFEGCCsGAQUFBzAChkVodHRwOi8vY2FjZXJ0cy5kaWdpY2VydC5jb20vRGln aUNlcnRHbG9iYWxHMlRMU1JTQVNIQTI1NjIwMjBDQTEtMS5jcnQwDAYDVR0TAQH/ BAIwADCCAX4GCisGAQQB1nkCBAIEggFuBIIBagFoAHYADleUvPOuqT4zGyyZB7P3 kN+bwj1xMiXdIaklrGHFTiEAAAGbo/Vl/QAABAMARzBFAiAp5KaGkrd7mmclmjnl OPSO+DaZrwwwpgbiEOnazvMTSgIhAOkmtUytEBUcrQ8SSBpxnm6g/r/eML4Y0v8w J/reYiHBAHUA0W6ppWgHfmY1oD83pd28A6U8QRIU1IgY9ekxsyPLlQQAAAGbo/Vm 1QAABAMARjBEAiB2jFDeXqAqVCTaNpIT2g7ygnFnyzRMSzW6XShcEUQAhAIgELnR ksh6JPFpZzWS3zh0uAf8nL9o7qY1eWFBJbKuwa0AdwBkEcRspBLsp4kcogIuALyr TygH1B41J6vq/tUDyX3N8AAAAZuj9WYmAAAEAwBIMEYCIQCFVsuZxzb5t/Y/cpX3 GkY2duJTeZnGajmLRVuVsrKuAgIhAIgIqsbUHtdhZnqufXwngiyGAcbFcywNUVjL GibcbQQYMA0GCSqGSIb3DQEBCwUAA4IBAQB+IZ3RxkiUzRUO2HfIEAsxASGi6RQE Oevz87CZZhM9Vb+WiBtAnXzaG8b/5NxqBRZnDXt0qSGrUL7Gxyg9jRPI8aGGQngM Bpej8/ZnY17vSQ/tWLnVJn3+0jQbiBwlM/wO49aJB/jbEvjMvJxMB9g1sv1fRQmi QFV1lTVVU3VMQ4fD8E9q6ZI2T9MYZOr5ceLWUWMxMVKbcL/t/LW2G+axpWdYqgLJ Go0Ip8xyChSuE86SJCd4U24Lc6rGj4Wt1YdrAnBX2Owrptc7PsARKkbiq974wqf3 093mSNM9k7KV2pjQq1OFYsBc7Y7YObJXcAetuCNJXmhl4QR7lqkKEx8I -----END CERTIFICATE----- subject=C = US, ST = California, L = Menlo Park, O = "Meta Platforms, Inc.", CN = llama.com issuer=C = US, O = DigiCert Inc, CN = DigiCert Global G2 TLS RSA SHA256 2020 CA1 --- No client certificate CA names sent Peer signing digest: SHA256 Peer signature type: RSA-PSS Server Temp Key: ECDH, prime256v1, 256 bits --- SSL handshake has read 4698 bytes and written 336 bytes Verification: OK --- New, TLSv1.2, Cipher is ECDHE-RSA-AES128-GCM-SHA256 Server public key is 2048 bit Secure Renegotiation IS supported Compression: NONE Expansion: NONE No ALPN negotiated SSL-Session: Protocol : TLSv1.2 Cipher : ECDHE-RSA-AES128-GCM-SHA256 Session-ID: 38774B17B45052D11D23CF4B44A86BE870298C78E4CF3AE8A3682CE9A52A2D12 Session-ID-ctx: Master-Key: B7ADB0D2B7C79CF63003D0E48709CD61B148D4F2B677CD67BCBB68BD65138203CF78C2952261B2FA1834A6917324076F PSK identity: None PSK identity hint: None SRP username: None Start Time: 1774373943 Timeout : 7200 (sec) Verify return code: 0 (ok) Extended master secret: yes --- DONE === tls1_3 === depth=2 C = US, O = DigiCert Inc, OU = www.digicert.com, CN = DigiCert Global Root G2 verify return:1 depth=1 C = US, O = DigiCert Inc, CN = DigiCert Global G2 TLS RSA SHA256 2020 CA1 verify return:1 depth=0 C = US, ST = California, L = Menlo Park, O = "Meta Platforms, Inc.", CN = llama.com verify return:1 CONNECTED(00000003) --- Certificate chain 0 s:C = US, ST = California, L = Menlo Park, O = "Meta Platforms, Inc.", CN = llama.com i:C = US, O = DigiCert Inc, CN = DigiCert Global G2 TLS RSA SHA256 2020 CA1 a:PKEY: rsaEncryption, 2048 (bit); sigalg: RSA-SHA256 v:NotBefore: Jan 1 00:00:00 2026 GMT; NotAfter: Apr 1 23:59:59 2026 GMT 1 s:C = US, O = DigiCert Inc, CN = DigiCert Global G2 TLS RSA SHA256 2020 CA1 i:C = US, O = DigiCert Inc, OU = www.digicert.com, CN = DigiCert Global Root G2 a:PKEY: rsaEncryption, 2048 (bit); sigalg: RSA-SHA256 v:NotBefore: Mar 30 00:00:00 2021 GMT; NotAfter: Mar 29 23:59:59 2031 GMT --- Server certificate -----BEGIN CERTIFICATE----- MIIG7TCCBdWgAwIBAgIQDBuv6hJm8OhaK29YuqKWRzANBgkqhkiG9w0BAQsFADBZ MQswCQYDVQQGEwJVUzEVMBMGA1UEChMMRGlnaUNlcnQgSW5jMTMwMQYDVQQDEypE aWdpQ2VydCBHbG9iYWwgRzIgVExTIFJTQSBTSEEyNTYgMjAyMCBDQTEwHhcNMjYw MTAxMDAwMDAwWhcNMjYwNDAxMjM1OTU5WjBqMQswCQYDVQQGEwJVUzETMBEGA1UE CBMKQ2FsaWZvcm5pYTETMBEGA1UEBxMKTWVubG8gUGFyazEdMBsGA1UEChMUTWV0 YSBQbGF0Zm9ybXMsIEluYy4xEjAQBgNVBAMTCWxsYW1hLmNvbTCCASIwDQYJKoZI hvcNAQEBBQADggEPADCCAQoCggEBANneN/VFdpAaNwv04/E+1WAEM246zIGq9iBu AnG4XgK0AcOLK/Ho+YY/A98oHkRGjJbe0v1Pdz56cQqDXHtxPHFRjS1LNKBRNsgp 9gTv7T3/pXOnnrQwpogodHTEiGSfb5RVB87lVEkmfvcj4mBYYETIb4dHUf8b7s/z 6MMULpVSmHzNYmf/IVqkaInIQRZxVWsaP877uL5J5B/kWAlXKGLTlP00XyeZCQv6 b7btiAyT3WdNFuMIiNU88wQztR3MoDu6QKH/Dd5HCAsftidrwodfV++tmEPUV49I d/5OW9f35eI0iTRSoQ19ClqvRx4Ovs8tdrYtd/p1VeNh0fu1umECAwEAAaOCA54w ggOaMB8GA1UdIwQYMBaAFHSFgMBmx9833s+9KTeqAx2+7c0XMB0GA1UdDgQWBBTP YYjSqZ5n7q9gLlK6W58O/dqZgzAwBgNVHREEKTAngglsbGFtYS5jb22CCyoubGxh bWEuY29tgg13d3cubGxhbWEuY29tMD4GA1UdIAQ3MDUwMwYGZ4EMAQICMCkwJwYI KwYBBQUHAgEWG2h0dHA6Ly93d3cuZGlnaWNlcnQuY29tL0NQUzAOBgNVHQ8BAf8E BAMCBaAwHQYDVR0lBBYwFAYIKwYBBQUHAwEGCCsGAQUFBwMCMIGfBgNVHR8EgZcw gZQwSKBGoESGQmh0dHA6Ly9jcmwzLmRpZ2ljZXJ0LmNvbS9EaWdpQ2VydEdsb2Jh bEcyVExTUlNBU0hBMjU2MjAyMENBMS0xLmNybDBIoEagRIZCaHR0cDovL2NybDQu ZGlnaWNlcnQuY29tL0RpZ2lDZXJ0R2xvYmFsRzJUTFNSU0FTSEEyNTYyMDIwQ0Ex LTEuY3JsMIGHBggrBgEFBQcBAQR7MHkwJAYIKwYBBQUHMAGGGGh0dHA6Ly9vY3Nw LmRpZ2ljZXJ0LmNvbTBRBggrBgEFBQcwAoZFaHR0cDovL2NhY2VydHMuZGlnaWNl cnQuY29tL0RpZ2lDZXJ0R2xvYmFsRzJUTFNSU0FTSEEyNTYyMDIwQ0ExLTEuY3J0 MAwGA1UdEwEB/wQCMAAwggF7BgorBgEEAdZ5AgQCBIIBawSCAWcBZQB1AA5XlLzz rqk+MxssmQez95Dfm8I9cTIl3SGpJaxhxU4hAAABm3cQWvMAAAQDAEYwRAIgGKqx BW6D3KxUnClyNk7H9NQ/8sG8OAD/0SDyPP47YdcCIF8DjNqXLgjEynwemjp+qG93 ONCu5GYHmK/kCWUVf0/cAHUAFoMtq/CpJQ8P8DqlRf/Iv8gj0IdL9gQpJ/jnHzMT 9foAAAGbdxBc2AAABAMARjBEAiBobznOCgh9oSnZ/XXkPnGsUYZgLWpOBCo2PAkU hF2GugIgdIfODIzM9pGuJ7P/FpdmwZLJEITj8OoXOC6DmOyQ1UMAdQCWl2S/VViX rfdDh2g3CEJ36fA61fak8zZuRqQ/D8qpxgAAAZt3EFsSAAAEAwBGMEQCIFzrd4wz oKCo22DX9Ty8PW0w8lJRfxr5j+YFDcnx2Js2AiAueuQnP1BqqZ3xB9m5YWfp9xyX kj550AkvA88vgqXHdzANBgkqhkiG9w0BAQsFAAOCAQEARcBeZ/1342WXFx1+3z0/ 1ruf6o3W9/wWlit/HTW31rguOxz2d08ogoWcU5a85SzzcYYLIasTbCx6Vr8VjfCM LkBreshlxMkAh0yPSzH6Vw9+55PZ7OXPBa//bu3ItOsbb4pvH+tTY3/jtg0PSMeA WUQIZZfSP8G2mx5U9MLelzKjZkxqA5VdFhEsfJXI3diqyIKjvxAsZB/mBZhdzNFt GAxgLMWLZ5/oVZOtM8eg10Aytib6+w4f4gFVDHoj1674naNhU1y7xQXgTbPLuuui 8yoO0AWMKDTLmnujTD3tCkL8Xmc90+kh5IFhD9DX0xupWZB/NOOg8MVEb+NUlACF Hg== -----END CERTIFICATE----- subject=C = US, ST = California, L = Menlo Park, O = "Meta Platforms, Inc.", CN = llama.com issuer=C = US, O = DigiCert Inc, CN = DigiCert Global G2 TLS RSA SHA256 2020 CA1 --- No client certificate CA names sent Peer signing digest: SHA256 Peer signature type: RSA-PSS Server Temp Key: X25519, 253 bits --- SSL handshake has read 3528 bytes and written 311 bytes Verification: OK --- New, TLSv1.3, Cipher is TLS_CHACHA20_POLY1305_SHA256 Server public key is 2048 bit Secure Renegotiation IS NOT supported Compression: NONE Expansion: NONE No ALPN negotiated Early data was not sent Verify return code: 0 (ok) --- DONE

Crawler stats

Basic stats
Total execution time	30 s
Total URLs	66
Total size	37 MB
Requests - total time	82 s
Requests - avg time	1.2 s
Requests - min time	122 ms
Requests - max time	5.1 s
Requests by status	200: 44 301: 20 302: 1 404: 1

Analysis stats

Found 21 row(s).

Class::method	Exec time 🔽	Exec count
BestPracticeAnalyzer::checkNonClickablePhoneNumbers	338 ms	45
SslTlsAnalyzer::getTLSandSSLCertificateInfo	312 ms	1
BestPracticeAnalyzer::checkHeadingStructure	296 ms	45
AccessibilityAnalyzer::checkMissingLabels	279 ms	44
AccessibilityAnalyzer::checkMissingAriaLabels	269 ms	44
AccessibilityAnalyzer::checkMissingRoles	215 ms	44
AccessibilityAnalyzer::checkMissingLang	171 ms	44
BestPracticeAnalyzer::checkMaxDOMDepth	136 ms	45
BestPracticeAnalyzer::checkInlineSvg	65 ms	45
BestPracticeAnalyzer::checkMissingQuotesOnAttributes	24 ms	45
AccessibilityAnalyzer::checkImageAltAttributes	7 ms	44
SeoAndOpenGraphAnalyzer::analyzeHeadings	5 ms	1
SecurityAnalyzer::checkHtmlSecurity	4 ms	45
SecurityAnalyzer::checkHeaders	1 ms	45
SeoAndOpenGraphAnalyzer::analyzeSeo	0 ms	1
SeoAndOpenGraphAnalyzer::analyzeOpenGraph	0 ms	1
BestPracticeAnalyzer::checkMetaDescriptionUniqueness	0 ms	1
BestPracticeAnalyzer::checkTitleUniqueness	0 ms	1
BestPracticeAnalyzer::checkBrotliSupport	0 ms	1
BestPracticeAnalyzer::checkWebpSupport	0 ms	1
BestPracticeAnalyzer::checkAvifSupport	0 ms	1
No rows found, please edit your search term.

(+) Show entire table

Content processor stats

Found 12 row(s).

Class::method	Exec time 🔽	Exec count
HtmlProcessor::findUrls	110 ms	66
NextJsProcessor::applyContentChangesBeforeUrlParsing	98 ms	45
JavaScriptProcessor::findUrls	86 ms	45
CssProcessor::findUrls	7 ms	45
AstroProcessor::findUrls	4 ms	45
AstroProcessor::applyContentChangesBeforeUrlParsing	0 ms	45
HtmlProcessor::applyContentChangesBeforeUrlParsing	0 ms	66
NextJsProcessor::findUrls	0 ms	45
JavaScriptProcessor::applyContentChangesBeforeUrlParsing	0 ms	45
SvelteProcessor::applyContentChangesBeforeUrlParsing	0 ms	45
SvelteProcessor::findUrls	0 ms	45
CssProcessor::applyContentChangesBeforeUrlParsing	0 ms	45
No rows found, please edit your search term.

Crawler info

Version	2.1.0.20260317
Executed At	2026-03-24 17:38:32
Command	siteone-crawler --url=https://www.llama.com/docs --markdown-export-dir=/tmp/siteone-meta_llama --markdown-exclude-selector=header,footer,nav,.sidebar,.menu,.breadcrumb,script,style --timeout=30 --workers=3 --disable-javascript --disable-styles --disable-fonts --disable-images --disable-files --no-color --hide-progress-bar --output=text --allowed-domain-for-crawling=www.llama.com --include-regex=/docs/
Hostname	ubuntu-8gb-hel1-1
User-Agent	Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/26.0.0.0 Safari/537.36 siteone-crawler/2.1.0.20260317