Create README.md (#1)

- Create README.md (625581a2a3dad81f6bc966fd3b0a51eacbfbee93)

README.md

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+---
+license: other
+language:
+- en
+- de
+- fr
+- it
+- pt
+- hi
+- es
+- th
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- llama-3.1
+- meta
+- autoawq
+---
+
+> [!IMPORTANT]
+> This repository is a community-driven quantized version of the original model [`meta-llama/Meta-Llama-3.1-8B-Instruct`](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) which is the BF16 half-precision official version released by Meta AI.
+
+## Model Information
+
+The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks.
+
+This repository contains [`meta-llama/Meta-Llama-3.1-8B-Instruct`](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) quantized using [AutoAWQ](https://github.com/casperhansen/AutoAWQ) from FP16 down to INT4 using the GEMM kernels performing zero-point quantization with a group size of 128.
+
+## Model Usage
+
+> [!NOTE]
+> In order to run the inference with Llama 3.1 8B Instruct AWQ in INT4, around 4 GiB of VRAM are needed only for loading the model checkpoint, without including the KV cache or the CUDA graphs, meaning that there should be a bit over that VRAM available.
+
+In order to use the current quantized model, support is offered for different solutions as `transformers`, `autoawq`, or `text-generation-inference`.
+
+### 🤗 transformers
+
+In order to run the inference with Llama 3.1 8B Instruct AWQ in INT4, both `torch` and `autoawq` need to be installed as:
+
+```bash
+pip install "torch>=2.2.0,<2.3.0" autoawq --upgrade
+```
+
+Then, the latest version of `transformers` need to be installed, being 4.43.0 or higher, as:
+
+```bash
+pip install "transformers[accelerate]>=4.43.0" --upgrade
+```
+
+To run the inference on top of Llama 3.1 8B Instruct AWQ in INT4 precision, the AWQ model can be instantiated as any other causal language modeling model via `AutoModelForCausalLM` and run the inference normally.
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4"
+prompt = [
+  {"role": "system", "content": "You are a helpful assistant, that responds as a pirate."},
+  {"role": "user", "content": "What's Deep Learning?"},
+]
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+inputs = tokenizer.apply_chat_template(prompt, tokenize=True, add_generation_prompt=True, return_tensors="pt").cuda()
+
+model = AutoModelForCausalLM.from_pretrained(
+  model_id,
+  torch_dtype=torch.float16,
+  low_cpu_mem_usage=True,
+  device_map="auto",
+)
+
+outputs = model.generate(inputs, do_sample=True, max_new_tokens=256)
+print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
+```
+
+### AutoAWQ
+
+In order to run the inference with Llama 3.1 8B Instruct AWQ in INT4, both `torch` and `autoawq` need to be installed as:
+
+```bash
+pip install "torch>=2.2.0,<2.3.0" autoawq --upgrade
+```
+
+Then, the latest version of `transformers` need to be installed, being 4.43.0 or higher, as:
+
+```bash
+pip install "transformers[accelerate]>=4.43.0" --upgrade
+```
+
+Alternatively, one may want to run that via `AutoAWQ` even though it's built on top of 🤗 `transformers`, which is the recommended approach instead as described above.
+
+```python
+import torch
+from autoawq import AutoAWQForCausalLM
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4"
+prompt = [
+  {"role": "system", "content": "You are a helpful assistant, that responds as a pirate."},
+  {"role": "user", "content": "What's Deep Learning?"},
+]
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+inputs = tokenizer.apply_chat_template(prompt, tokenize=True, add_generation_prompt=True, return_tensors="pt").cuda()
+
+model = AutoAWQForCausalLM.from_pretrained(
+  model_id,
+  torch_dtype=torch.float16,
+  low_cpu_mem_usage=True,
+  device_map="auto",
+)
+
+outputs = model.generate(inputs, do_sample=True, max_new_tokens=256)
+print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
+```
+
+The AutoAWQ script has been adapted from [AutoAWQ/examples/generate.py](https://github.com/casper-hansen/AutoAWQ/blob/main/examples/generate.py).
+
+### 🤗 Text Generation Inference (TGI)
+
+Coming soon!
+
+## Quantization Reproduction
+
+> [!NOTE]
+> In order to quantize Llama 3.1 8B Instruct using AutoAWQ, you will need to use an instance with at least enough CPU RAM to fit the whole model i.e. ~8GiB, and an NVIDIA GPU with 24GiB of VRAM to quantize it.
+
+In order to quantize Llama 3.1 8B Instruct, first install `torch` and `autoawq` as follows:
+
+```bash
+pip install "torch>=2.2.0,<2.3.0" autoawq --upgrade
+```
+
+Otherwise the quantization may fail, since the AutoAWQ kernels are built with PyTorch 2.2.1, meaning that those will break with PyTorch 2.3.0.
+
+Then install the latest version of `transformers` as follows:
+
+```bash
+pip install "transformers>=4.43.0" --upgrade
+```
+
+And then, run the following script, adapted from [`AutoAWQ/examples/quantize.py`](https://github.com/casper-hansen/AutoAWQ/blob/main/examples/quantize.py) as follows:
+
+```python
+from awq import AutoAWQForCausalLM
+from transformers import AutoTokenizer
+
+model_path = "meta-llama/Meta-Llama-3.1-8B-Instruct"
+quant_path = "hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4"
+quant_config = {
+  "zero_point": True,
+  "q_group_size": 128,
+  "w_bit": 4,
+  "version": "GEMM",
+}
+
+# Load model
+model = AutoAWQForCausalLM.from_pretrained(
+  model_path, **{"low_cpu_mem_usage": True, "use_cache": False}
+)
+tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+
+# Quantize
+model.quantize(tokenizer, quant_config=quant_config)
+
+# Save quantized model
+model.save_quantized(quant_path)
+tokenizer.save_pretrained(quant_path)
+
+print(f'Model is quantized and saved at "{quant_path}"')
+```