Create README.md

README.md

@@ -0,0 +1,238 @@
+---
+license: other
+language:
+- en
+- de
+- fr
+- it
+- pt
+- hi
+- es
+- th
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- llama-3.1
+- meta
+- autoawq
+---
+
+> [!NOTE]
+> Note that this is a community driven quantized version of the original model [`meta-llama/Meta-Llama-3.1-405B-Instruct`](https://huggingface.co/meta-llama/Meta-Llama-3.1-405B-Instruct) which is the FP16 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.
+
+**Model developer**: Meta
+
+**Model Architecture:** Llama 3.1 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. 
+
+<table>
+  <tr>
+   <td>
+   </td>
+   <td><strong>Training Data</strong>
+   </td>
+   <td><strong>Params</strong>
+   </td>
+   <td><strong>Input modalities</strong>
+   </td>
+   <td><strong>Output modalities</strong>
+   </td>
+   <td><strong>Context length</strong>
+   </td>
+   <td><strong>GQA</strong>
+   </td>
+   <td><strong>Token count</strong>
+   </td>
+   <td><strong>Knowledge cutoff</strong>
+   </td>
+  </tr>
+  <tr>
+   <td rowspan="3" >Llama 3.1 (text only)
+   </td>
+   <td rowspan="3" >A new mix of publicly available online data.
+   </td>
+   <td>8B
+   </td>
+   <td>Multilingual Text
+   </td>
+   <td>Multilingual Text and code
+   </td>
+   <td>128k
+   </td>
+   <td>Yes
+   </td>
+   <td rowspan="3" >15T+
+   </td>
+   <td rowspan="3" >December 2023
+   </td>
+  </tr>
+  <tr>
+   <td>70B
+   </td>
+   <td>Multilingual Text
+   </td>
+   <td>Multilingual Text and code
+   </td>
+   <td>128k
+   </td>
+   <td>Yes
+   </td>
+  </tr>
+  <tr>
+   <td>405B
+   </td>
+   <td>Multilingual Text
+   </td>
+   <td>Multilingual Text and code
+   </td>
+   <td>128k
+   </td>
+   <td>Yes
+   </td>
+  </tr>
+</table>
+
+**Supported languages:** English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.
+
+**Llama 3.1 family of models**. Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability.
+
+**Model Release Date:** July 23, 2024.
+
+**Status:** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
+
+**License:** A custom commercial license, the Llama 3.1 Community License, is available at: [https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE)
+
+Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3.1 in applications, please go [here](https://github.com/meta-llama/llama-recipes). 
+
+For more information please refer to the original model card [`meta-llama/Meta-Llama-3.1-405B-Instruct`](https://huggingface.co/meta-llama/Meta-Llama-3.1-405B-Instruct).
+
+## Quantized Model Information
+
+Llama 3.1 405B Instruct has been 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.
+
+In order to quantize Llama 3.1 405B Instruct, we had to 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 we install the latest version of `transformers` as follows:
+
+```bash
+pip install "transformers>=4.43.0" --upgrade
+```
+
+And then we can 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-3.1-405B-Instruct"
+quant_path = "hugging-quants/Meta-Llama-3.1-405B-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}"')
+```
+
+## Quantized Model Usage
+
+In order to use the current quantized model, we offer support for different alternatives:
+
+### 🤗 transformers
+
+To run the inference on top of Llama 3.1 405B Instruct AWQ in INT4 precision, we can instantiate the AWQ model 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-3.1-405B-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)
+tokenizer.pad_token_id = tokenizer.eos_token_id
+tokenizer.padding_side = "left"
+
+terminators = [
+  tokenizer.eos_token_id,
+  tokenizer.convert_tokens_to_ids("<|eot_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, eos_token_id=terminators)
+print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
+```
+
+### AutoAWQ
+
+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-3.1-405B-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)
+tokenizer.pad_token_id = tokenizer.eos_token_id
+tokenizer.padding_side = "left"
+
+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",
+  fuse_layers=True,
+)
+
+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!