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Model: neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a16 Source: Original Platform
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README.md
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README.md
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---
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tags:
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- int8
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- vllm
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language:
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- en
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- de
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- fr
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- it
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- pt
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- hi
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- es
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- th
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pipeline_tag: text-generation
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license: llama3.1
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base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
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---
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# Meta-Llama-3.1-8B-Instruct-quantized.w8a16
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## Model Overview
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- **Model Architecture:** Meta-Llama-3
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- **Input:** Text
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- **Output:** Text
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- **Model Optimizations:**
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- **Weight quantization:** INT8
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- **Intended Use Cases:** Intended for commercial and research use multiple languages. 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.
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- **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws).
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- **Release Date:** 7/23/2024
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- **Version:** 1.0
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- **License(s):** Llama3.1
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- **Model Developers:** Neural Magic
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Quantized version of [Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct).
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It achieves scores within 1% of the scores of the unquantized model for MMLU, ARC-Challenge, GSM-8k, Hellaswag, Winogrande and TruthfulQA.
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### Model Optimizations
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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 INT8 data type.
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This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%.
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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 INT8 and floating point representations of the quantized weights.
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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.
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## Deployment
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This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below.
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```python
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from vllm import LLM, SamplingParams
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from transformers import AutoTokenizer
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model_id = "neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a16"
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number_gpus = 1
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max_model_len = 8192
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sampling_params = SamplingParams(temperature=0.6, top_p=0.9, max_tokens=256)
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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messages = [
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{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
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{"role": "user", "content": "Who are you?"},
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]
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prompts = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
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llm = LLM(model=model_id, tensor_parallel_size=number_gpus, max_model_len=max_model_len)
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outputs = llm.generate(prompts, sampling_params)
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generated_text = outputs[0].outputs[0].text
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print(generated_text)
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```
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vLLM aslo supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details.
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## Creation
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This model was created by using the [llm-compressor](https://github.com/vllm-project/llm-compressor) library as presented in the code snipet below.
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```python
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from transformers import AutoTokenizer
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from datasets import Dataset
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from llmcompressor.transformers import SparseAutoModelForCausalLM, oneshot
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from llmcompressor.modifiers.quantization import GPTQModifier
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import random
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model_id = "meta-llama/Meta-Llama-3.1-8B-Instruct"
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num_samples = 256
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max_seq_len = 8192
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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max_token_id = len(tokenizer.get_vocab()) - 1
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input_ids = [[random.randint(0, max_token_id) for _ in range(max_seq_len)] for _ in range(num_samples)]
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attention_mask = num_samples * [max_seq_len * [1]]
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ds = Dataset.from_dict({"input_ids": input_ids, "attention_mask": attention_mask})
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recipe = GPTQModifier(
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targets="Linear",
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scheme="W8A16",
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ignore=["lm_head"],
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dampening_frac=0.01,
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)
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model = SparseAutoModelForCausalLM.from_pretrained(
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model_id,
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device_map="auto",
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trust_remote_code=True,
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)
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oneshot(
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model=model,
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dataset=ds,
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recipe=recipe,
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max_seq_length=max_seq_len,
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num_calibration_samples=num_samples,
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)
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model.save_pretrained("Meta-Llama-3.1-8B-Instruct-quantized.w8a16")
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```
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## Evaluation
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The model was evaluated on MMLU, ARC-Challenge, GSM-8K, Hellaswag, Winogrande and TruthfulQA.
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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.
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This version of the lm-evaluation-harness includes versions of MMLU, 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-3.1-8B-Instruct-evals).
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**Note:** Results have been updated after Meta modified the chat template.
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### Accuracy
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<table>
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<tr>
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<td><strong>Category</strong>
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</td>
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<td><strong>Benchmark</strong>
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</td>
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<td><strong>Meta-Llama-3.1-8B-Instruct </strong>
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</td>
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<td><strong>Meta-Llama-3.1-8B-Instruct-quantized.w8a16 (this model)</strong>
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</td>
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<td><strong>Recovery</strong>
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</td>
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</tr>
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<tr>
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<td rowspan="5" ><strong>OpenLLM v1</strong>
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</td>
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</tr>
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<tr>
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<td>Hellaswag (10-shot)
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</td>
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<td>80.47
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</td>
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<td>80.48
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</td>
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<td>100.0%
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</td>
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</tr>
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<tr>
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<td>Winogrande (5-shot)
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</td>
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<td>78.06
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</td>
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<td>77.51
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</td>
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<td>99.3%
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</td>
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</tr>
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<tr>
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<td>TruthfulQA (0-shot, mc2)
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</td>
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<td>54.48
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</td>
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<td>54.41
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</td>
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<td>99.9%
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</td>
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</tr>
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<tr>
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<td><strong>Average</strong>
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</td>
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<td><strong>74.05</strong>
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</td>
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<td><strong>74.12</strong>
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</td>
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<td><strong>100.1%</strong>
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</td>
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</tr>
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<tr>
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<td rowspan="9" ><strong>Multilingual</strong>
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</td>
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<td>Portuguese MMLU (5-shot)
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</td>
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<td>59.96
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</td>
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<td>59.79
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</td>
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<td>99.8%
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</td>
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</tr>
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<tr>
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<td>Spanish MMLU (5-shot)
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</td>
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<td>60.25
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</td>
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<td>59.92
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</td>
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<td>99.4%
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</td>
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</tr>
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<tr>
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<td>Italian MMLU (5-shot)
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</td>
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<td>59.23
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</td>
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<td>59.25
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</td>
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<td>100.0%
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</td>
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</tr>
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<tr>
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<td>German MMLU (5-shot)
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</td>
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<td>58.63
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</td>
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<td>58.31
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</td>
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<td>99.5%
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</td>
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</tr>
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<tr>
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<td>French MMLU (5-shot)
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</td>
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<td>59.65
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</td>
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<td>59.57
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</td>
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<td>99.9%
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</td>
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</tr>
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<tr>
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<td>Hindi MMLU (5-shot)
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</td>
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<td>50.10
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</td>
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<td>49.97
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</td>
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<td>99.7%
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</td>
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</tr>
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<tr>
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<td>Thai MMLU (5-shot)
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</td>
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<td>49.12
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</td>
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<td>49.09
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</td>
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<td>99.9%
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</td>
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</tr>
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</table>
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### Reproduction
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The results were obtained using the following commands:
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#### MMLU
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```
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lm_eval \
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--model vllm \
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--model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a16",dtype=auto,max_model_len=3850,max_gen_toks=10,tensor_parallel_size=1 \
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--tasks mmlu_llama_3.1_instruct \
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--fewshot_as_multiturn \
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--apply_chat_template \
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--num_fewshot 5 \
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--batch_size auto
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```
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#### MMLU-CoT
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```
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lm_eval \
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--model vllm \
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--model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a16",dtype=auto,max_model_len=4064,max_gen_toks=1024,tensor_parallel_size=1 \
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--tasks mmlu_cot_0shot_llama_3.1_instruct \
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--apply_chat_template \
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--num_fewshot 0 \
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--batch_size auto
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```
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#### ARC-Challenge
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```
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lm_eval \
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--model vllm \
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--model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a16",dtype=auto,max_model_len=3940,max_gen_toks=100,tensor_parallel_size=1 \
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--tasks arc_challenge_llama_3.1_instruct \
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--apply_chat_template \
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--num_fewshot 0 \
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--batch_size auto
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```
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#### GSM-8K
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```
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lm_eval \
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--model vllm \
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--model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a16",dtype=auto,max_model_len=4096,max_gen_toks=1024,tensor_parallel_size=1 \
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--tasks gsm8k_cot_llama_3.1_instruct \
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--fewshot_as_multiturn \
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--apply_chat_template \
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--num_fewshot 8 \
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--batch_size auto
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```
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#### Hellaswag
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```
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lm_eval \
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--model vllm \
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--model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a16",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \
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--tasks hellaswag \
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--num_fewshot 10 \
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--batch_size auto
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```
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#### Winogrande
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```
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lm_eval \
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--model vllm \
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--model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a16",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \
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--tasks winogrande \
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--num_fewshot 5 \
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--batch_size auto
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```
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#### TruthfulQA
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```
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lm_eval \
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--model vllm \
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--model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a16",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \
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--tasks truthfulqa \
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--num_fewshot 0 \
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--batch_size auto
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```
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#### MMLU Portuguese
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```
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lm_eval \
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--model vllm \
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--model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a16",dtype=auto,max_model_len=3850,max_gen_toks=10,tensor_parallel_size=1 \
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--tasks mmlu_pt_llama_3.1_instruct \
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--fewshot_as_multiturn \
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--apply_chat_template \
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--num_fewshot 5 \
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--batch_size auto
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```
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#### MMLU Spanish
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```
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lm_eval \
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--model vllm \
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--model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a16",dtype=auto,max_model_len=3850,max_gen_toks=10,tensor_parallel_size=1 \
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--tasks mmlu_es_llama_3.1_instruct \
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--fewshot_as_multiturn \
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--apply_chat_template \
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--num_fewshot 5 \
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--batch_size auto
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```
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#### MMLU Italian
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```
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lm_eval \
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--model vllm \
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--model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a16",dtype=auto,max_model_len=3850,max_gen_toks=10,tensor_parallel_size=1 \
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--tasks mmlu_it_llama_3.1_instruct \
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--fewshot_as_multiturn \
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--apply_chat_template \
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--num_fewshot 5 \
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--batch_size auto
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```
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#### MMLU German
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```
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lm_eval \
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--model vllm \
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--model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a16",dtype=auto,max_model_len=3850,max_gen_toks=10,tensor_parallel_size=1 \
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--tasks mmlu_de_llama_3.1_instruct \
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--fewshot_as_multiturn \
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--apply_chat_template \
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--num_fewshot 5 \
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--batch_size auto
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```
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#### MMLU French
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```
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lm_eval \
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--model vllm \
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--model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a16",dtype=auto,max_model_len=3850,max_gen_toks=10,tensor_parallel_size=1 \
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--tasks mmlu_fr_llama_3.1_instruct \
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--fewshot_as_multiturn \
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--apply_chat_template \
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--num_fewshot 5 \
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--batch_size auto
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```
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#### MMLU Hindi
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```
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lm_eval \
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--model vllm \
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--model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a16",dtype=auto,max_model_len=3850,max_gen_toks=10,tensor_parallel_size=1 \
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--tasks mmlu_hi_llama_3.1_instruct \
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--fewshot_as_multiturn \
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--apply_chat_template \
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--num_fewshot 5 \
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--batch_size auto
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```
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#### MMLU Thai
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```
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lm_eval \
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--model vllm \
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--model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a16",dtype=auto,max_model_len=3850,max_gen_toks=10,tensor_parallel_size=1 \
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--tasks mmlu_th_llama_3.1_instruct \
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--fewshot_as_multiturn \
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--apply_chat_template \
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--num_fewshot 5 \
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--batch_size auto
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```
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