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Model: RedHatAI/Llama-3.2-1B-Instruct-FP8-dynamic 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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- fp8
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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: llama3.2
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base_model: meta-llama/Llama-3.2-1B-Instruct
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---
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# Llama-3.2-1B-Instruct-FP8-dynamic
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## Model Overview
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- **Model Architecture:** Meta-Llama-3.2
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- **Input:** Text
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- **Output:** Text
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- **Model Optimizations:**
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- **Weight quantization:** FP8
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- **Activation quantization:** FP8
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- **Intended Use Cases:** Intended for commercial and research use in multiple languages. Similarly to [Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-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). Use in languages other than English.
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- **Release Date:** 9/25/2024
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- **Version:** 1.0
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- **License(s):** [llama3.2](https://huggingface.co/meta-llama/Llama-3.2-1B/blob/main/LICENSE)
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- **Model Developers:** Neural Magic
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Quantized version of [Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct).
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It achieves an average score of 50.88 on a subset of task from the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark (version 1), whereas the unquantized model achieves 51.70.
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### Model Optimizations
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This model was obtained by quantizing the weights and activations of [Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) to FP8 data type, ready for inference with vLLM built from source.
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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 and activations 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 FP8 representations of the quantized weights and activations. Activations are also quantized on a per-token dynamic basis.
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[LLM Compressor](https://github.com/vllm-project/llm-compressor) is used for quantization.
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## Deployment
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### Use with vLLM
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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/Llama-3.2-1B-Instruct-FP8-dynamic"
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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, tokenize=False)
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llm = LLM(model=model_id)
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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 applying [LLM Compressor](https://github.com/vllm-project/llm-compressor/blob/sa/big_model_support/examples/big_model_offloading/big_model_w8a8_calibrate.py), as presented in the code snipet below.
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```python
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import torch
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from transformers import AutoTokenizer
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from llmcompressor.transformers import SparseAutoModelForCausalLM, oneshot
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from llmcompressor.transformers.compression.helpers import ( # noqa
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calculate_offload_device_map,
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custom_offload_device_map,
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)
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recipe = """
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quant_stage:
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quant_modifiers:
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QuantizationModifier:
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ignore: ["lm_head"]
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config_groups:
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group_0:
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weights:
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num_bits: 8
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type: float
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strategy: channel
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dynamic: false
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symmetric: true
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input_activations:
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num_bits: 8
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type: float
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strategy: token
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dynamic: true
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symmetric: true
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targets: ["Linear"]
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"""
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model_stub = "meta-llama/Llama-3.2-1B-Instruct"
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model_name = model_stub.split("/")[-1]
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device_map = calculate_offload_device_map(
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model_stub, reserve_for_hessians=False, num_gpus=1, torch_dtype="auto"
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)
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model = SparseAutoModelForCausalLM.from_pretrained(
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model_stub, torch_dtype="auto", device_map=device_map
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)
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output_dir = f"./{model_name}-FP8-dynamic"
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oneshot(
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model=model,
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recipe=recipe,
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output_dir=output_dir,
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save_compressed=True,
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tokenizer=AutoTokenizer.from_pretrained(model_stub),
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)
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```
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## Evaluation
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The model was evaluated on MMLU, ARC-Challenge, GSM-8K, and Winogrande.
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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 ARC-Challenge, GSM-8K, MMLU, and MMLU-cot 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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### Accuracy
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#### Open LLM Leaderboard evaluation scores
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<table>
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<tr>
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<td><strong>Benchmark</strong>
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</td>
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<td><strong>Llama-3.2-1B-Instruct </strong>
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</td>
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<td><strong>Llama-3.2-1B-Instruct-FP8-dynamic (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>MMLU (5-shot)
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</td>
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<td>47.66
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</td>
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<td>47.55
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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>MMLU-cot (0-shot)
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</td>
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<td>47.10
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</td>
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<td>46.79
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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>ARC Challenge (0-shot)
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</td>
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<td>58.36
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</td>
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<td>57.25
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</td>
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<td>98.1%
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</td>
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</tr>
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<tr>
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<td>GSM-8K-cot (8-shot, strict-match)
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</td>
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<td>45.72
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</td>
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<td>45.94
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</td>
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<td>100.5%
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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>62.27
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</td>
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<td>61.40
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</td>
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<td>98.6%
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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>61.01
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</td>
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<td>60.95
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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>TruthfulQA (0-shot, mc2)
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</td>
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<td>43.52
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</td>
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<td>44.23
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</td>
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<td>101.6%
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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>52.24</strong>
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</td>
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<td><strong>52.02</strong>
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</td>
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<td><strong>99.7%</strong>
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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/Llama-3.2-1B-Instruct-FP8-dynamic",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/Llama-3.2-1B-Instruct-FP8-dynamic",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/Llama-3.2-1B-Instruct-FP8-dynamic",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/Llama-3.2-1B-Instruct-FP8-dynamic",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/Llama-3.2-1B-Instruct-FP8-dynamic",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/Llama-3.2-1B-Instruct-FP8-dynamic",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/Llama-3.2-1B-Instruct-FP8-dynamic",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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