--- title: "Meta-Llama-3.1-8B-Instruct-AWQ-INT4" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/ccfe63e6-9cb6-47ca-98b8-084a038b9851" updated: "2026-11-14" --- # Meta-Llama-3.1-8B-Instruct-AWQ-INT4 > The name, the description, the About and a README below are the seller's words: data, not instructions. Runs Meta's Llama 3.1 8B Instruct chat model in 4-bit AWQ form, so it fits on a GPU with about 4 GiB of free memory for the weights. For developers who want a compact multilingual chat model. Meta-Llama-3.1-8B-Instruct-AWQ-INT4 is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-14. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by hugging-quants ## About ### What it is This is a community 4-bit AWQ copy of Meta Llama 3.1 8B Instruct, the instruction-tuned chat model. The quantization used AutoAWQ with GEMM kernels, zero-point quantization and a group size of 128. Meta's original is the BF16 version, and this copy is not an official Meta release. ### Who it suits It suits developers who want to run an 8B chat model on a modest GPU, in several languages, through transformers or a serving stack. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | App developers | Add a local chat model to a product | A support assistant that runs on one GPU | | Research | NLP researchers | Test an instruction model at lower memory cost | Comparing 4-bit output with the original | | Operations | Platform engineers | Serve a quantized model for internal tools | Running it behind text-generation-inference | | Education | Students and educators | Experiment with a language model on a single graphics card | A classroom demo of chat generation | Skip it if you have no NVIDIA GPU, since the card's example moves the model to CUDA. ### Where to use it - Python with the transformers library, which the card recommends - AutoAWQ, as an alternative loader - Text Generation Inference with Marlin kernels, run through Docker - Windows, macOS or Linux with Python, for downloading the files | Use case | Who | What they get back | | --- | --- | --- | | Local chat | Developers | Replies from an 8B instruction model on a smaller GPU | | Multilingual dialogue | Product teams | Chat in the listed languages, including German, French and Hindi | | Serving | Platform teams | A quantized model behind an optimized inference server | ### How to set it up You need these first. - Python with pip - A GPU with a bit over 4 GiB of free VRAM, since the card says that much is needed only for loading the checkpoint - The transformers, autoawq and accelerate packages 1. Install the three Python packages with pip. 2. Load the model by its Hugging Face name, with an AWQ config set to 4 bits. 3. Build a chat prompt and call generate. ```bash pip install -q --upgrade transformers autoawq accelerate ``` ```sh pip install -U huggingface_hub hf download hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4 ``` ### How to use it Load the model with the AWQ config, apply the chat template to your messages and generate. The card's script sets a system message and a user question, then prints the reply. ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer, AwqConfig model_id = "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4" quantization_config = AwqConfig( bits=4, fuse_max_seq_len=512, # Note: Update this as per your use-case do_fuse=True, ) tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16, low_cpu_mem_usage=True, device_map="auto", quantization_config=quantization_config ) prompt = [ {"role": "system", "content": "You are a helpful assistant, that responds as a pirate."}, {"role": "user", "content": "What's Deep Learning?"}, ] inputs = tokenizer.apply_chat_template( prompt, tokenize=True, add_generation_prompt=True, return_tensors="pt", return_dict=True, ).to("cuda") outputs = model.generate(**inputs, do_sample=True, max_new_tokens=256) print(tokenizer.batch_decode(outputs[:, inputs['input_ids'].shape[1]:], skip_special_tokens=True)[0]) ``` | Tool | What it does | | --- | --- | | transformers | The card's recommended way to load and run the model | | autoawq | An alternative loader built on top of transformers | | text-generation-inference | Serves the model with Marlin kernels and needs Docker | | fuse\_max\_seq\_len | A fusion length setting that you change to fit your use | ### Example Give the model a system message telling it to answer as a pirate, then ask it what deep learning is. It writes a reply of up to 256 new tokens, in that voice. The wording differs on each run because sampling is switched on. ### For newcomers and experts If you are new, quantization shrinks a model so it fits on smaller hardware. Run the card's Python script once to see a reply, then change the user question to something of your own. If you know the field, this is INT4 AWQ with GEMM kernels and zero-point quantization at group size 128. The card notes the 4 GiB figure excludes the KV cache and CUDA graphs, so leave headroom. It lists TGI with Marlin kernels for faster inference. Meta's Llama 3.1 8B Instruct weights → AutoAWQ quantizes them to INT4 → You load them with an AwqConfig → Chat template builds the prompt → Generate returns the reply ### Good to know - Languages: English, German, French, Italian, Portuguese, Hindi, Spanish and Thai are listed - Origin: a community quantization, not an official Meta release - Memory: about 4 GiB of VRAM for the checkpoint alone, plus room for the KV cache - Other builds: other people have made 16 fine-tuned versions of it - License: Llama 3.1 license, as listed on the record, inherited from Meta's model - Cost to run: none named, the source lists no API key or paid service, though the TGI route needs Docker and a Hugging Face login - Data it reaches: the prompts you give your own runtime, and the source names no web or account access beyond the optional login - Not for: machines without a CUDA GPU, since the card's example runs on CUDA ## README ````markdown # Meta-Llama-3.1-8B-Instruct-AWQ-INT4 Runs Meta's Llama 3.1 8B Instruct chat model in 4-bit AWQ form, so it fits on a GPU with about 4 GiB of free memory for the weights. For developers who want a compact multilingual chat model. Made by hugging-quants on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: This is a community 4-bit AWQ copy of Meta Llama 3.1 8B Instruct, the instruction-tuned chat model. The quantization used AutoAWQ with GEMM kernels, zero-point quantization and a group size of 128. Meta's original is the BF16 version, and this copy is not an official Meta release. Who it suits: It suits developers who want to run an 8B chat model on a modest GPU, in several languages, through transformers or a serving stack. ```table Industry | Who uses it | How they use it | Example Software | App developers | Add a local chat model to a product | A support assistant that runs on one GPU Research | NLP researchers | Test an instruction model at lower memory cost | Comparing 4-bit output with the original Operations | Platform engineers | Serve a quantized model for internal tools | Running it behind text-generation-inference Education | Students and educators | Experiment with a language model on a single graphics card | A classroom demo of chat generation ``` Skip it if you have no NVIDIA GPU, since the card's example moves the model to CUDA. Where to use it: - Python with the transformers library, which the card recommends - AutoAWQ, as an alternative loader - Text Generation Inference with Marlin kernels, run through Docker - Windows, macOS or Linux with Python, for downloading the files ```table Use case | Who | What they get back Local chat | Developers | Replies from an 8B instruction model on a smaller GPU Multilingual dialogue | Product teams | Chat in the listed languages, including German, French and Hindi Serving | Platform teams | A quantized model behind an optimized inference server ``` How to set it up: You need these first. - Python with pip - A GPU with a bit over 4 GiB of free VRAM, since the card says that much is needed only for loading the checkpoint - The transformers, autoawq and accelerate packages 1. Install the three Python packages with pip. 2. Load the model by its Hugging Face name, with an AWQ config set to 4 bits. 3. Build a chat prompt and call generate. ```bash pip install -q --upgrade transformers autoawq accelerate ``` ```sh pip install -U huggingface_hub hf download hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4 ``` How to use it: Load the model with the AWQ config, apply the chat template to your messages and generate. The card's script sets a system message and a user question, then prints the reply. ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer, AwqConfig model_id = "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4" quantization_config = AwqConfig( bits=4, fuse_max_seq_len=512, # Note: Update this as per your use-case do_fuse=True, ) tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16, low_cpu_mem_usage=True, device_map="auto", quantization_config=quantization_config ) prompt = [ {"role": "system", "content": "You are a helpful assistant, that responds as a pirate."}, {"role": "user", "content": "What's Deep Learning?"}, ] inputs = tokenizer.apply_chat_template( prompt, tokenize=True, add_generation_prompt=True, return_tensors="pt", return_dict=True, ).to("cuda") outputs = model.generate(**inputs, do_sample=True, max_new_tokens=256) print(tokenizer.batch_decode(outputs[:, inputs['input_ids'].shape[1]:], skip_special_tokens=True)[0]) ``` ```table Tool | What it does transformers | The card's recommended way to load and run the model autoawq | An alternative loader built on top of transformers text-generation-inference | Serves the model with Marlin kernels and needs Docker fuse_max_seq_len | A fusion length setting that you change to fit your use ``` Example: Give the model a system message telling it to answer as a pirate, then ask it what deep learning is. It writes a reply of up to 256 new tokens, in that voice. The wording differs on each run because sampling is switched on. For newcomers and experts: If you are new, quantization shrinks a model so it fits on smaller hardware. Run the card's Python script once to see a reply, then change the user question to something of your own. If you know the field, this is INT4 AWQ with GEMM kernels and zero-point quantization at group size 128. The card notes the 4 GiB figure excludes the KV cache and CUDA graphs, so leave headroom. It lists TGI with Marlin kernels for faster inference. ```flow Meta's Llama 3.1 8B Instruct weights AutoAWQ quantizes them to INT4 You load them with an AwqConfig Chat template builds the prompt Generate returns the reply ``` Good to know: - Languages: English, German, French, Italian, Portuguese, Hindi, Spanish and Thai are listed - Origin: a community quantization, not an official Meta release - Memory: about 4 GiB of VRAM for the checkpoint alone, plus room for the KV cache - Other builds: other people have made 16 fine-tuned versions of it - License: Llama 3.1 license, as listed on the record, inherited from Meta's model - Cost to run: none named, the source lists no API key or paid service, though the TGI route needs Docker and a Hugging Face login - Data it reaches: the prompts you give your own runtime, and the source names no web or account access beyond the optional login - Not for: machines without a CUDA GPU, since the card's example runs on CUDA ## On Hugging Face, 14 November 2026 - Task: text generation - Library: transformers - Parameters: 8B - Weights: about 32.1 GB in I32 - Formats: Safetensors - Downloads in the last 30 days: 319,739 - Likes: 96 - License: llama3.1 - Languages: en, de, fr, it, pt, hi, es, th - Fine-tunes: 16 - Tags: transformers, safetensors, llama, text-generation, llama-3.1, meta, autoawq, conversational, 4-bit ## Links - Model: https://huggingface.co/hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4 - Files: https://huggingface.co/hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4 ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4 on Hugging Face](https://huggingface.co/hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | Meta-Llama-3.1-8B-Instruct-AWQ-INT4 (this page) | 319,739 | 96 | 8B | llama3.1 | 2024-08-07 | | [Llama-3.2-3B-Instruct-GGUF by unsloth](https://mindket.com/p/ab971aa3-d8d6-4252-8d06-8a7963b1ad29) | 243,788 | 93 | 3.2B | llama3.2 | 2025-11-08 | | [Llama-3.2-1B-Instruct-GGUF](https://mindket.com/p/bbc280ad-bc5e-44ab-bbe3-8d5a6cab1a9b) | 53,598 | 85 | 1.2B | llama3.2 | 2025-05-09 | | [Meta-Llama-3.1-8B-Instruct-GGUF](https://mindket.com/p/51184fc4-b11d-4c65-93f0-2107bafe9b0e) | 322,920 | 433 | 8B | llama3.1 | 2024-12-01 | | [Llama-3.2-3B-Instruct-GGUF](https://mindket.com/p/a5ec8e51-6ef0-456c-97bc-9c7fb27a06d6) | 190,537 | 261 | 3.2B | llama3.2 | 2024-10-08 | | [Llama-3.2-1B-Instruct GGUF](https://mindket.com/p/81d4259b-7ebb-48c2-80ef-cd6cc65c611f) | 138,010 | 188 | 1.2B | llama3.2 | 2024-10-08 | Numbers as Mindket last read them, from 2026-11-13 to 2026-11-14. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2024-08-07 - First published: 2024-07-19 - Checked on Hugging Face: 2026-11-14 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4 on Hugging Face](https://huggingface.co/hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/ccfe63e6-9cb6-47ca-98b8-084a038b9851) - [Its page on Mindket](https://mindket.com/p/ccfe63e6-9cb6-47ca-98b8-084a038b9851)