Models › Llama family

Llama-Guard-3-1B

meta-llama Llama 3.2 Community License out 2 years ago

1.50B parameters, text in, text out.

Verified 2026-10-26 07:27 UTC Rank #669 in LLMs · score 72.8 method v1 Sources 3 Downloads 30d 27.2k · #433 of 619 tracked

Changed this week: 4 new quantized builds on the Hub (29 in all).2026-10-24

Can I run it?0.9GB at INT4Estimatefits a 16 GB cardmethod
Can I use it?Llama 3.2 Community LicenseAs 3.1; EU multimodal restriction in the textlicence file2026-10-26
Do people use it?27.2kdownloads, 30 days#433 of 619 we track · below the median+3 likes, downloads −9% this week133 likesHugging Face2026-10-26

Worth reading

Picked by rule from 15 tracked pieces: the lab's own post, the most shared, a field report, the latest.

Most discussed · 1,311 HN pts

Llama.cpp 30B runs with only 6GB of RAM now

llama.cpp introduces a new file format enabling memory mapping for model weights, resulting in loading speeds 10 to 100 times faster.

  • The new format merges multi-file models like LLaMA 13B into a single file to prevent C++ code from reshaping data during loading.
github.com 2023-03-31 1,311 HN pts · 414 comments 989 words about the Llama family family
Latest · 2 years ago

Llama 3.2: Revolutionizing edge AI and vision with open, customizable models

Meta releases Llama 3.2, featuring small and medium-sized vision LLMs (11B and 90B) alongside lightweight text-only models (1B and 3B) designed for edge and mobile devices.

  • The 11B and 90B vision models require an entirely new model architecture that supports image reasoning.
  • Developers can keep all text-only capabilities intact because the adapter training intentionally did not update the language-model parameters.
ai.meta.com 2024-09-25 924 HN pts · 328 comments 2.5k words about the Llama family family

15 best of 15 tracked

The 2 picks above, plus these 13. Top is the radar's score: discussion, source authority and the lab's own posts, fading with age.

  1. ArticleCode Llama, a state-of-the-art large language model for codingai.meta.com · 2023-08-24 · 970 HN pts · official
  2. ArticleMeta Llama 3llama.meta.com · 2024-04-18 · 2,199 HN pts · official
  3. ArticleThe LLama Effect: Leak Sparked a Series of Open Source Alternatives to ChatGPTthesequence.substack.com · 2023-04-09 · 791 HN pts
  4. CodeLlama.cpp: Port of Facebook's LLaMA model in C/C++, with Apple Silicon supportgithub.com · 2023-03-10 · 989 HN pts
  5. CodeGpt4all: A chatbot trained on ~800k GPT-3.5-Turbo Generations based on LLaMagithub.com · 2023-03-28 · 593 HN pts
  6. CodeRun Llama 13B with a 6GB graphics cardgist.github.com · 2023-05-14
  7. CodeLlama2.c: Inference llama 2 in one file of pure Cgithub.com · 2023-07-23 · 707 HN pts
  8. CodeShow HN: I built a free in-browser Llama 3 chatbot powered by WebGPUgithub.com · 2024-05-03 · 547 HN pts
  9. DiscussionFine-tune your own Llama 2 to replace GPT-3.5/4news.ycombinator.com · 2023-09-12 · 955 HN pts
  10. DiscussionMeta AI releases Code Llama 70Btwitter.com · 2024-01-29
  11. ArticleRun Llama 2 uncensored locallyollama.ai · 2023-08-02
  12. ArticleLlama.ttf: A font which is also an LLMfuglede.github.io · 2024-06-23
  13. PaperLlama Guard 3-1B-INT4: Compact and Efficient Safeguard for Human-AI Conversationarxiv.org · 2024-11-29

Where it's used

Hugging Face does not say who downloads a model or with what. These are the proxies: API providers listing it, quantized builds, fine-tunes/adapters/merges, and Spaces that load it.

No API provider serves it, so its downloads are people running it themselves: 29 quantized builds and 13 Spaces.

  • As a service0no API provider listed
  • On own hardware29quantized builds
  • Built on139 fine-tunes · 4 adapters · 0 merges
  • In apps13Hugging Face Spaces

Top Spaces running it: whitecircle/circle-guard-bench ♥ 32 · fdaudens/meta-download-stats ♥ 1 · amoyakd/RAG-Based-Product-Inquiry-ChatBot ♥ 1

Will it fit?

Weights only, an estimate: leave room for the KV cache. Method

Fits a 16 GB card at INT4

About 0.9 GB at INT4, 1.8 GB at FP8, 3.6 GB at BF16: weights only, so leave room for the KV cache.

16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 0.9 GB of weights (estimate; KV cache and activations extra)INT40.9 GBFP8: about 1.8 GB of weights (estimate; KV cache and activations extra)FP81.8 GBBF16: about 3.6 GB of weights (estimate; KV cache and activations extra)BF163.6 GB
16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 0.9 GB of weights (estimate; KV cache and activations extra)INT40.9 GBFP8: about 1.8 GB of weights (estimate; KV cache and activations extra)FP81.8 GBBF16: about 3.6 GB of weights (estimate; KV cache and activations extra)BF163.6 GB

Estimate params × bytes per param × 1.2 · how we estimate

The story so far

Weights, every dated article, and repo commits on one rail.

Most coverage came in the first two weeks

Teal is the weights (2024-09-20), amber triangles are 10 dated articles (each links to it).

What people built on it

42 derivatives on the Hub. Which build to run, by runtime: the most downloaded in each class.

42 derivatives on the Hub, most of them quantized

From the Hugging Face model tree; quantized builds in teal, fine-tunes, merges and adapters in greys.

29 quantized (Hugging Face model tree)9 fine-tunes (Hugging Face model tree)4 adapters (Hugging Face model tree)29 quantized9 fine-tunes4 adapters
29 quantized (Hugging Face model tree)9 fine-tunes (Hugging Face model tree)4 adapters (Hugging Face model tree)29 quantized9 fine-tunes4 adapters

All variants →

Reference

Everything else, folded: the full tables, every source.

Use-case evidence

3 use cases

What it is good at 3 use cases

Labels are evidence, not verdicts: measured a third-party leaderboard row for this model; author-reported a number the model author published in the card or eval files; structural a deterministic signal from the name, config or chat template, or a decision-model estimate. Each chip links to its source. All use cases.

Facts

17 fields

Listed in LLMs

Facts

Pipeline tagtext-generation
Librarytransformers
ArchitecturesLlamaForCausalLM
Model typellama
Params by dtypeBF16: 1,498,482,688
Size bandS
MoEno
Gatedmanual
PapersarXiv:2404.12241, arXiv:2312.06674, arXiv:2204.05862, arXiv:2308.01263, arXiv:2403.03853
LicenseLlama 3.2 Community License (llama3.2)
Hub snapshot2026-10-26

Servability

vLLM · SGLang

Servability

vLLM: supported · LlamaForCausalLM · 4b1c04e76fe9ac3ca8b7e541dd423e7d5f38dc60SGLang: supported · LlamaForCausalLM · 60d941293e3af27a12c6eb64e2c3e2e1a17f100f

vLLM registry (4b1c04e76fe9ac3ca8b7e541dd423e7d5f38dc60) · SGLang registry (60d941293e3af27a12c6eb64e2c3e2e1a17f100f) · Matched by architecture class name against each project's registry on main; 'supported' means the architecture is registered, not that this checkpoint was tested.

Hardware fit

3 estimates

Hardware fit ESTIMATE

Weights-only memory for Llama-Guard-3-1B: params × bytes/param × 1.2 overhead. KV cache and activations are extra. How this is computed.

BF16 (2 bytes/param)3.6 GB · Edge (<4 GB)
FP8 (1 byte/param)1.8 GB · Edge (<4 GB)
INT4 (0.5 bytes/param)0.9 GB · Edge (<4 GB)

Weights only (params × bytes/param × 1.2 overhead); KV cache and activations extra.

All variants

40 variants

Lineage

Original model Family: meta-llama/Llama 42 derivatives on the Hub

finetune: 9quantized: 29adapter: 4

Top variants (40 shown, ranked by downloads)

Relation
Quant
40 rows
Variant Relation Quant Params Band License Author Likes Downloads
AcuteShrewdSecurity/Llama-Phishsense-1B finetune — 1.24B S llama3.2 AcuteShrewdSecurity 10 54k
tensorblock/Llama-Guard-3-1B-GGUF quantized GGUF — — llama3.2 tensorblock 1 2.5k
MaziyarPanahi/Llama-Guard-3-1B-GGUF quantized GGUF — — — MaziyarPanahi 0 1.9k
DevQuasar-2/Llama-Guard-3-1B-GGUF quantized GGUF — — — DevQuasar-2 0 1.1k
itlwas/Llama-Guard-3-1B-Q4_K_M-GGUF quantized GGUF — — llama3.2 itlwas 0 775
onnx-community/Llama-Guard-3-1B quantized — — — llama3.2 onnx-community 2 660
DevQuasar/meta-llama.Llama-Guard-3-1B-GGUF quantized GGUF — — — DevQuasar 0 695
SirSahOl/Llama-Guard-3-1B-chat-mlx-16bit finetune mlx 1.50B S llama3.2 SirSahOl 0 633
smdesai/llama-guard-3-1b quantized — 1.50B S llama3.2 smdesai 0 508
Fabio2000/Llama-Guard-3-1B-Q4_K_M-GGUF quantized GGUF — — llama3.2 Fabio2000 0 269
AIFunOver/Llama-Guard-3-1B-openvino-8bit quantized -8bit — — llama3.2 AIFunOver 0 242
samuellimabraz/Llama-Guard-3-1B-Q4_K_M-GGUF quantized GGUF — — llama3.2 samuellimabraz 0 234
Thorge-AI/llama-guard-3-1b-q8_0.gguf quantized q8_0 — — llama3.2 Thorge-AI 1 183
mlx-community/Llama-Guard-3-1B-chat-mlx-16bit finetune mlx 1.50B S llama3.2 mlx-community 0 219
jquirion/Llama-Guard-3-1B-Q4_K_M quantized GGUF — — llama3.2 jquirion 0 211
sheldonrobinson/Llama-Guard-3-1B-Q8_0-GGUF quantized Q8_0 — — llama3.2 sheldonrobinson 0 155
mlx-community/Llama-Guard-3-1B-chat-mlx-8bit quantized mlx 1.50B S llama3.2 mlx-community 0 151
wolfCuanhamaRWS/GuardReasoner-1B_q5_k_m_gguf quantized gguf — — llama3.2 wolfCuanhamaRWS 0 150
GuardwallAI/Llama-Guard-3-1B-Q8_0-GGUF quantized Q8_0 — — llama3.2 GuardwallAI 0 149
tomalamb/Llama-Guard-3-1B-lora-r1-beavertails adapter — — — — tomalamb 0 144
mlx-community/Llama-Guard-3-1B-chat-mlx-4bit quantized mlx 1.50B S llama3.2 mlx-community 0 141
marcusnogueira/llama-guard-3-1b-awq-4bit quantized awq 1.50B S — marcusnogueira 0 139
Tibi29/Llama-Guard-3-1B-Phishing-Fine-Tune finetune — 1.24B S llama3.2 Tibi29 0 132
Anthonyg5005/Llama-Guard-3-1B-exl2 quantized exl2 — — llama3.2 Anthonyg5005 0 125
sheldonrobinson/Llama-Guard-3-1B-Q4_0-GGUF quantized Q4_0 — — llama3.2 sheldonrobinson 0 111
bricknerdev/Llama-Guard-3-1B-Q8_0-GGUF quantized Q8_0 — — llama3.2 bricknerdev 0 103
AIFunOver/Llama-Guard-3-1B-openvino-4bit quantized -4bit — — llama3.2 AIFunOver 0 79
marcusnogueira/llama-guard-3-1b-gptq-4bit quantized gptq 1.50B S — marcusnogueira 0 78
marcusnogueira/llama-guard-3-1b-gptq-8bit quantized gptq 1.50B S — marcusnogueira 0 70
SirSahOl/Llama-Guard-3-1B-chat-mlx-4bit quantized mlx 1.50B S llama3.2 SirSahOl 0 67
AIFunOver/Llama-Guard-3-1B-openvino-fp16 finetune -fp16 — — llama3.2 AIFunOver 0 64
wolfCuanhamaRWS/Llama-Phishsense-1B_q4_k_m_gguf quantized gguf — — llama3.2 wolfCuanhamaRWS 0 64
SirSahOl/Llama-Guard-3-1B-chat-mlx-8bit quantized mlx 1.50B S llama3.2 SirSahOl 0 61
jainsatyam26/nemotron-llamaguard-final adapter — — — — jainsatyam26 0 54
2084Collective/sft_output finetune — 1.50B S — 2084Collective 0 50
Timios74/aegisguard-llama-guard finetune — 1.24B S mit Timios74 0 40
aascott/Llama-Guard-3-1B-Confusable-Guard-LoRA adapter — — — llama3.2 aascott 0 29
mitulr/outputs adapter — — — llama3.2 mitulr 0 26
marcusnogueira/llama-guard-3-1b-smoothquant-w8a8 quantized -w8a8 1.50B S — marcusnogueira 0 17
marcusnogueira/llama-guard-3-1b-llm-int8 quantized int8 1.50B S — marcusnogueira 0 16

Repository changelog

1 change
changed in the last 7 daysOther: 2026-10-24all changes

Articles and coverage

15 pieces

Ranked by source signals (HN, Lobsters, authority domains, official posts) and recency. Summaries are generated from a stored copy of the article text; every sentence cites a span of that text, and nothing is shown that failed the citation check.

Official3

ai.meta.com2023-08-24officialauthorityHN front pageabout the Llama familyHN 970 pts · 501 comments1,460 words
Meta released Code Llama, a code-specialized large language model built on Llama 2 that is free for research and commercial use. The family includes four sizes with 7B, 13B, 34B, and 70B parameters, trained on 500B tokens of code data (1T for the 70B model) and supporting up to 100,000 tokens of context. Benchmark testing showed Code Llama 34B scored 53.7% on HumanEval and 56.2% on MBPP, outperforming other open-source solutions.written, reviewed, cited
  • Code Llama - Python is a language-specialized variation of Code Llama, further fine-tuned on 100B tokens of Python code.
ai.meta.com2024-09-25officialauthorityHN front pageabout the Llama familyHN 924 pts · 328 comments2,460 words
Meta releases Llama 3.2, featuring small and medium-sized vision LLMs (11B and 90B) alongside lightweight text-only models (1B and 3B) designed for edge and mobile devices. The 11B and 90B models serve as drop-in replacements for their corresponding text model equivalents while the 1B and 3B models support a context length of 128K tokens and are enabled on day one for Qualcomm and MediaTek hardware. Both pre-trained and aligned vision models are available for fine-tuning using torchtune and local deployment via torchchat, with weights based on BFloat16 numerics.written, reviewed, cited
  • The 11B and 90B vision models require an entirely new model architecture that supports image reasoning.
  • Developers can keep all text-only capabilities intact because the adapter training intentionally did not update the language-model parameters.
  • The 1B and 3B models are created by using structured pruning in a single shot manner from the Llama 3.1 8B.
llama.meta.com2024-04-18officialHN front pageabout the Llama familyHN 2,199 pts · 923 comments42 words

Analysis5

thesequence.substack.com2023-04-09authorityHN front pageabout the Llama familyHN 791 pts · 504 comments837 words
Meta released Llama in 7B, 13B, 33B, and 65B parameter versions, which were leaked on 4chan and subsequently fine-tuned by researchers into models like Vicuna that match GPT-4 performance. Stanford University released Alpaca, an instruction-following model based on the Llama 7B variant, while UC Berkeley, CMU, Stanford, and UC San Diego open-sourced Vicuna. Additional projects include Koala from Berkeley AI Research, ChatLLama from Nebuly, and ColossalChat from the Colossal-AI project, all leveraging the leaked base model to create open-source conversational agents.written, reviewed, cited
  • Open source LLM alternatives previously lacked the ability to follow human instructions compared to API models.
  • BloombergGPT is a 50 billion LLM fine tuned in financial data based on the BLOOM architecture.
  • The Segment Anything Model was released with the largest computer vision segmentation dataset ever, containing 1-Billion masks.
github.com2023-03-10authorityHN front pageabout the Llama familyHN 989 pts · 284 comments494 words
llama.cpp enables LLM and VLM inference in C/C++ with minimal setup across various hardware. It supports 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization for faster inference and reduced memory use. Apple silicon is a first-class citizen optimized via ARM NEON, Accelerate and Metal frameworks, while CPU+GPU hybrid inference partially accelerates models larger than the total VRAM capacity.written, reviewed, cited
github.com2023-03-28authorityHN front pageabout the Llama familyHN 593 pts · 301 comments466 words
github.com2023-07-23authorityHN front pageabout the Llama familyHN 707 pts · 165 comments4,774 words
The llama2.c repository offers a 700-line pure C file for training and inferring Llama 2 models, currently supporting fp32 inference for models up to 7B parameters. Work on model quantization is currently ongoing, but the project includes a 110M parameter model trained on the TinyStories dataset.written, reviewed, cited
  • Models larger than 7B are unusably slow because the repo performs float32 inference.
  • The 13B+ model variant fails due to integer flow errors in pointer arithmetic.
github.com2024-05-03authorityHN front pageabout the Llama familyHN 547 pts · 139 comments233 words
Secret-llama is an entirely-in-browser LLM chatbot powered by the webllm inference engine that runs fully private without needing a server. It supports models like Llama 3 and Mistral, ranging in size from 600MB to 4.3GB, requiring WebGPU support. Users can try it directly online, while developers compile the React code using yarn build-and-preview.written, reviewed, cited

Community2

github.com2023-03-31authorityHN front pageabout the Llama familyHN 1,311 pts · 414 comments989 words
llama.cpp introduces a new file format enabling memory mapping for model weights, resulting in loading speeds 10 to 100 times faster. This change allows safely loading models up to 2x larger, supports running many concurrent inference processes, and ensures tensors are aligned properly on a 32-byte boundary. Users who deleted old Meta LLaMA .pth files can use the migrate-ggml-2023-03-30-pr613.py script to convert old ggml files into the new mmap()'able format.written, reviewed, cited
  • The new format merges multi-file models like LLaMA 13B into a single file to prevent C++ code from reshaping data during loading.
news.ycombinator.com2023-09-12HN front pageabout the Llama familyHN 955 pts · 181 comments10,023 words
Fine-tuning open-source Llama 2 models can replace GPT-3.5 and GPT-4 for specific tasks, with a fine-tuned Llama 7B matching GPT-4 labels 95% of the time on a test set. While fine-tuning requires 1000+ examples and is less flexible than prompting, a fine-tuned Llama 7B is 50x cheaper than GPT-3.5 per token, costing just $19 to classify 2M recipes versus $23k for GPT-4.written, reviewed, cited
All coverage tracked for this model (15) — append-only, nothing is ever removed
TitleSourcePublishedFirst seenSignals
Llama Guard 3-1B-INT4: Compact and Efficient Safeguard for Human-AI Conversationarxiv.org2024-11-292026-10-07hn
Llama 3.2: Revolutionizing edge AI and vision with open, customizable modelsai.meta.com official2024-09-252026-10-07hn
Llama.ttf: A font which is also an LLMfuglede.github.io2024-06-232026-10-07hn
Show HN: I built a free in-browser Llama 3 chatbot powered by WebGPUgithub.com2024-05-032026-10-07hn
Meta Llama 3llama.meta.com official2024-04-182026-10-07hn
Meta AI releases Code Llama 70Btwitter.com2024-01-292026-10-07hn
Fine-tune your own Llama 2 to replace GPT-3.5/4news.ycombinator.com2023-09-122026-10-07hn
Code Llama, a state-of-the-art large language model for codingai.meta.com official2023-08-242026-10-07hn
Run Llama 2 uncensored locallyollama.ai2023-08-022026-10-07hn
Llama2.c: Inference llama 2 in one file of pure Cgithub.com2023-07-232026-10-07hn
Run Llama 13B with a 6GB graphics cardgist.github.com2023-05-142026-10-07hn
The LLama Effect: Leak Sparked a Series of Open Source Alternatives to ChatGPTthesequence.substack.com2023-04-092026-10-07hn
Llama.cpp 30B runs with only 6GB of RAM nowgithub.com2023-03-312026-10-07hn
Gpt4all: A chatbot trained on ~800k GPT-3.5-Turbo Generations based on LLaMagithub.com2023-03-282026-10-07hn
Llama.cpp: Port of Facebook's LLaMA model in C/C++, with Apple Silicon supportgithub.com2023-03-102026-10-07hn

Papers

5 papers

Papers 5

arXiv links found in the model card and its tags. "Peer-reviewed" is shown only with evidence: a published venue in OpenAlex (measured) or the authors' own journal reference on arXiv (reported).

Similar models

3 models

Similar models

  • Qwen2.5-1.5B-Instruct · 1.54B
    same size band (S), same task (text-generation), more providers, permissive license
  • Qwen2.5-1.5B · 1.54B
    same size band (S), same task (text-generation), more providers, permissive license
  • Qwen2.5-Coder-1.5B-Instruct · 1.54B
    same size band (S), same task (text-generation), more providers, permissive license

Sources

3 sources

Sources

Hub snapshot: 2026-10-26 · HF API: 2026-10-26 07:23 UTC · Providers: 2026-10-26 07:26 UTC · Articles: 2026-10-26 07:27 UTC · Summary: 2026-10-07 15:13 UTC · Last verified 2026-10-26 07:27 UTC

Follow Llama-Guard-3-1B

Watch this model

Email alerts are not open yet; the feeds above are the same data. See feeds for the item schema.