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-3.1-70B-Instruct-quantized.w8a8
+
+## Model Overview
+- **Model Architecture:** Meta-Llama-3
+  - **Input:** Text
+  - **Output:** Text
+- **Model Optimizations:**
+  - **Activation quantization:** INT8
+  - **Weight quantization:** INT8
+- **Intended Use Cases:** Intended for commercial and research use multiple languages. Similarly to [Meta-Llama-3.1-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-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).
+- **Release Date:** 7/29/2024
+- **Version:** 1.0
+- **License(s):** [Llama3.1]
+- **Model Developers:** Neural Magic
+
+Quantized version of [Meta-Llama-3.1-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-Instruct).
+It achieves scores within 3% of the scores of the unquantized model for MMLU, ARC-Challenge, GSM-8k, Hellaswag, Winogrande and TruthfulQA.
+
+### Model Optimizations
+
+This model was obtained by quantizing the weights of [Meta-Llama-3.1-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-Instruct) to INT8 data type.
+This optimization reduces the number of bits used to represent weights and activations from 16 to 8, reducing GPU memory requirements (by approximately 50%) and increasing matrix-multiply compute throughput (by approximately 2x).
+Weight quantization also reduces disk size requirements by approximately 50%.
+
+Only weights and activations of the linear operators within transformers blocks are quantized.
+Weights are quantized with a symmetric static per-channel scheme, where a fixed linear scaling factor is applied between INT8 and floating point representations for each output channel dimension.
+Activations are quantized with a symmetric dynamic per-token scheme, computing a linear scaling factor at runtime for each token between INT8 and floating point representations.
+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 256 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-3.1-70B-Instruct-quantized.w8a8"
+number_gpus = 2
+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-3.1-70B-Instruct"
+
+num_samples = 256
+max_seq_len = 8192
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+def preprocess_fn(example):
+  return {"text": tokenizer.apply_chat_template(example["messages"], add_generation_prompt=False, tokenize=False)}
+
+ds = load_dataset("neuralmagic/LLM_compression_calibration", split="train")
+ds = ds.shuffle().select(range(num_samples))
+ds = ds.map(preprocess_fn)
+
+examples = [tokenizer(example["text"], padding=False, max_length=max_seq_len, truncation=True) for example in ds]
+
+recipe = GPTQModifier(
+  targets="Linear",
+  scheme="W8A8",
+  ignore=["lm_head"],
+  dampening_frac=0.1,
+)
+
+model = SparseAutoModelForCausalLM.from_pretrained(
+  model_id,
+  device_map="auto",
+)
+
+oneshot(
+  model=model,
+  dataset=ds,
+  recipe=recipe,
+  max_seq_length=max_seq_len,
+  num_calibration_samples=num_samples,
+)
+
+model.save_pretrained("Meta-Llama-3.1-70B-Instruct-quantized.w8a8")
+```
+
+
+## Evaluation
+
+The model was evaluated on MMLU, ARC-Challenge, GSM-8K, Hellaswag, Winogrande and TruthfulQA.
+Evaluation was conducted using the Neural Magic fork of [lm-evaluation-harness](https://github.com/neuralmagic/lm-evaluation-harness/tree/llama_3.1_instruct) (branch llama_3.1_instruct) and the [vLLM](https://docs.vllm.ai/en/stable/) engine.
+This version of the lm-evaluation-harness includes versions of ARC-Challenge and GSM-8K that match the prompting style of [Meta-Llama-3.1-Instruct-evals](https://huggingface.co/datasets/meta-llama/Meta-Llama-3.1-70B-Instruct-evals).
+
+### Accuracy
+
+#### Open LLM Leaderboard evaluation scores
+<table>
+  <tr>
+   <td><strong>Benchmark</strong>
+   </td>
+   <td><strong>Meta-Llama-3.1-70B-Instruct </strong>
+   </td>
+   <td><strong>Meta-Llama-3.1-70B-Instruct-quantized.w8a8 (this model)</strong>
+   </td>
+   <td><strong>Recovery</strong>
+   </td>
+  </tr>
+  <tr>
+   <td>MMLU (5-shot)
+   </td>
+   <td>82.21
+   </td>
+   <td>79.91
+   </td>
+   <td>97.2%
+   </td>
+  </tr>
+  <tr>
+   <td>ARC Challenge (0-shot)
+   </td>
+   <td>95.05
+   </td>
+   <td>93.09
+   </td>
+   <td>97.9%
+   </td>
+  </tr>
+  <tr>
+   <td>GSM-8K (CoT, 8-shot, strict-match)
+   </td>
+   <td>93.10
+   </td>
+   <td>93.18
+   </td>
+   <td>100.1%
+   </td>
+  </tr>
+  <tr>
+   <td>Hellaswag (10-shot)
+   </td>
+   <td>86.40
+   </td>
+   <td>85.46
+   </td>
+   <td>98.9%
+   </td>
+  </tr>
+  <tr>
+   <td>Winogrande (5-shot)
+   </td>
+   <td>85.00
+   </td>
+   <td>85.24
+   </td>
+   <td>100.3%
+   </td>
+  </tr>
+  <tr>
+   <td>TruthfulQA (0-shot)
+   </td>
+   <td>59.83
+   </td>
+   <td>58.55
+   </td>
+   <td>97.9%
+   </td>
+  </tr>
+  <tr>
+   <td><strong>Average</strong>
+   </td>
+   <td><strong>83.60</strong>
+   </td>
+   <td><strong>82.57</strong>
+   </td>
+   <td><strong>98.8%</strong>
+   </td>
+  </tr>
+</table>