Create README.md

README.md

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+---
+language:
+- en
+- de
+- fr
+- it
+- pt
+- hi
+- es
+- th
+pipeline_tag: text-generation
+license: llama3.1
+---
+
+# Meta-Llama-3.1-8B-Instruct-quantized.w4a16
+
+## Model Overview
+- **Model Architecture:** Meta-Llama-3
+  - **Input:** Text
+  - **Output:** Text
+- **Model Optimizations:**
+  - **Weight quantization:** INT4
+- **Intended Use Cases:** Intended for commercial and research use in English. Similarly to [Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct), this models is intended for assistant-like chat.
+- **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English.
+- **Release Date:** 7/26/2024
+- **Version:** 1.0
+- **License(s):** [Llama3](https://llama.meta.com/llama3/license/)
+- **Model Developers:** Neural Magic
+
+Quantized version of [Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct).
+It achieves an average score of 67.57 on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark (version 1), whereas the unquantized model achieves 69.32.
+
+### Model Optimizations
+
+This model was obtained by quantizing the weights of [Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) to INT4 data type.
+This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 75%.
+
+Only the weights of the linear operators within transformers blocks are quantized. Symmetric per-channel quantization is applied, in which a linear scaling per output dimension maps the INT4 and floating point representations of the quantized weights.
+The [GPTQ](https://arxiv.org/abs/2210.17323) algorithm is applied for quantization, as implemented in the [llm-compressor](https://github.com/vllm-project/llm-compressor) library. GPTQ used a 1% damping factor and 512 sequences of 8,192 random tokens.
+
+
+## Deployment
+
+This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below.
+
+```python
+from vllm import LLM, SamplingParams
+from transformers import AutoTokenizer
+
+model_id = "neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16"
+number_gpus = 1
+max_model_len = 8192
+
+sampling_params = SamplingParams(temperature=0.6, top_p=0.9, max_tokens=256)
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+messages = [
+    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
+    {"role": "user", "content": "Who are you?"},
+]
+
+prompts = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
+
+llm = LLM(model=model_id, tensor_parallel_size=number_gpus, max_model_len=max_model_len)
+
+outputs = llm.generate(prompts, sampling_params)
+
+generated_text = outputs[0].outputs[0].text
+print(generated_text)
+```
+
+vLLM aslo supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details.
+
+
+## Creation
+
+This model was created by using the [llm-compressor](https://github.com/vllm-project/llm-compressor) library as presented in the code snipet below.
+
+```python
+from transformers import AutoTokenizer
+from datasets import Dataset
+from llmcompressor.transformers import SparseAutoModelForCausalLM, oneshot
+from llmcompressor.modifiers.quantization import GPTQModifier
+import random
+
+model_id = "meta-llama/Meta-Llama-3.1-8B-Instruct"
+
+num_samples = 512
+max_seq_len = 8192
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+preprocess_fn = lambda example: {"text": "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n{text}".format_map(example)}
+
+dataset_name = "neuralmagic/LLM_compression_calibration"
+dataset = load_dataset(dataset_name, split="train")
+ds = dataset.shuffle().select(range(num_samples))
+ds = ds.map(preprocess_fn)
+
+recipe = GPTQModifier(
+  targets="Linear",
+  scheme="W4A16",
+  ignore=["lm_head"],
+  dampening_frac=0.01,
+)
+
+model = SparseAutoModelForCausalLM.from_pretrained(
+  model_id,
+  device_map="auto",
+  trust_remote_code=True,
+)
+
+oneshot(
+  model=model,
+  dataset=ds,
+  recipe=recipe,
+  max_seq_length=max_seq_len,
+  num_calibration_samples=num_samples,
+)
+model.save_pretrained("Meta-Llama-3.1-8B-Instruct-quantized.w4a16")
+```
+
+
+
+## Evaluation
+
+The model was evaluated on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) leaderboard tasks (version 1) with the [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/tree/383bbd54bc621086e05aa1b030d8d4d5635b25e6) (commit 383bbd54bc621086e05aa1b030d8d4d5635b25e6) and the [vLLM](https://docs.vllm.ai/en/stable/) engine, using the following command:
+```
+lm_eval \
+  --model vllm \
+  --model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16",dtype=auto,gpu_memory_utilization=0.4,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \
+  --tasks openllm \
+  --batch_size auto
+```
+
+### Accuracy
+
+#### Open LLM Leaderboard evaluation scores
+<table>
+  <tr>
+   <td><strong>Benchmark</strong>
+   </td>
+   <td><strong>Meta-Llama-3.1-8B-Instruct </strong>
+   </td>
+    <td><strong>hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4</strong>
+   </td>
+   <td><strong>Meta-Llama-3.1-8B-Instruct-quantized.w4a16 (this model)</strong>
+   </td>
+   <td><strong>Recovery (this model) </strong>
+   </td>
+  </tr>
+  <tr>
+   <td>MMLU (5-shot)
+   </td>
+   <td>67.94
+   </td>
+   <td>66.33
+   </td>
+   <td>65.38
+   </td>
+   <td>96.23%
+   </td>
+  </tr>
+  <tr>
+   <td>ARC Challenge (25-shot)
+   </td>
+   <td>60.41
+   </td>
+   <td>58.36
+   </td>
+   <td>59.30
+   </td>
+   <td>98.16%
+   </td>
+  </tr>
+  <tr>
+   <td>GSM-8K (5-shot, strict-match)
+   </td>
+   <td>75.66
+   </td>
+   <td>74.07
+   </td>
+   <td>75.43
+   </td>
+   <td>99.69%
+   </td>
+  </tr>
+  <tr>
+   <td>Hellaswag (10-shot)
+   </td>
+   <td>80.01
+   </td>
+   <td>79.18
+   </td>
+   <td>79.05
+   </td>
+   <td>98.80%
+   </td>
+  </tr>
+  <tr>
+   <td>Winogrande (5-shot)
+   </td>
+   <td>77.90
+   </td>
+   <td>76.00
+   </td>
+   <td>76.08
+   </td>
+   <td>97.66%
+   </td>
+  </tr>
+  <tr>
+   <td>TruthfulQA (0-shot)
+   </td>
+   <td>54.04
+   </td>
+   <td>51.91
+   </td>
+   <td>50.19
+   </td>
+   <td>92.8%
+   </td>
+  </tr>
+  <tr>
+   <td><strong>Average</strong>
+   </td>
+   <td><strong>69.33</strong>
+   </td>
+   <td><strong>67.64</strong>
+   </td>
+   <td><strong>67.57</strong>
+   </td>
+   <td><strong>97.47%</strong>
+   </td>
+  </tr>
+</table>