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Model: yejingfu/nmagic-Meta-Llama-3.1-8B-Instruct-FP8 Source: Original Platform
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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: llama3.1
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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-FP8
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## Model Overview
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- **Model Architecture:** Meta-Llama-3.1
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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 [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). Use in languages other than English.
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- **Release Date:** 7/23/2024
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- **Version:** 1.0
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- **License(s):** [llama3.1](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B/blob/main/LICENSE)
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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 an average score of 73.44 on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark (version 1), whereas the unquantized model achieves 73.79.
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### Model Optimizations
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This model was obtained by quantizing the weights and activations of [Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-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-tensor quantization is applied, in which a single linear scaling maps the FP8 representations of the quantized weights and activations.
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[LLM Compressor](https://github.com/vllm-project/llm-compressor) is used for quantization with 512 sequences of UltraChat.
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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/Meta-Llama-3.1-8B-Instruct-FP8"
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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 with calibration samples from UltraChat](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 datasets import load_dataset
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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 (
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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: tensor
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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: tensor
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dynamic: false
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symmetric: true
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targets: ["Linear"]
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"""
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model_stub = "meta-llama/Meta-Llama-3.1-8B-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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tokenizer = AutoTokenizer.from_pretrained(model_stub)
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output_dir = f"./{model_name}-FP8"
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DATASET_ID = "HuggingFaceH4/ultrachat_200k"
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DATASET_SPLIT = "train_sft"
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NUM_CALIBRATION_SAMPLES = 512
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MAX_SEQUENCE_LENGTH = 4096
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ds = load_dataset(DATASET_ID, split=DATASET_SPLIT)
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ds = ds.shuffle(seed=42).select(range(NUM_CALIBRATION_SAMPLES))
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def preprocess(example):
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return {
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"text": tokenizer.apply_chat_template(
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example["messages"],
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tokenize=False,
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)
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}
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ds = ds.map(preprocess)
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def tokenize(sample):
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return tokenizer(
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sample["text"],
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padding=False,
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max_length=MAX_SEQUENCE_LENGTH,
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truncation=True,
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add_special_tokens=False,
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)
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ds = ds.map(tokenize, remove_columns=ds.column_names)
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oneshot(
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model=model,
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output_dir=output_dir,
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dataset=ds,
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recipe=recipe,
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max_seq_length=MAX_SEQUENCE_LENGTH,
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num_calibration_samples=NUM_CALIBRATION_SAMPLES,
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save_compressed=True,
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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, 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 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>Meta-Llama-3.1-8B-Instruct </strong>
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</td>
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<td><strong>Meta-Llama-3.1-8B-Instruct-FP8(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>67.95
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</td>
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<td>67.97
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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>MMLU-cot (0-shot)
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</td>
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<td>71.24
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</td>
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<td>71.12
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</td>
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<td>99.83%
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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>82.00
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</td>
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<td>81.66
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</td>
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<td>99.59%
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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>81.96
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</td>
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<td>81.12
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</td>
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<td>98.98%
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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>80.46
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</td>
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<td>80.4
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</td>
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<td>99.93%
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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>78.45
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</td>
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<td>77.90
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</td>
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<td>99.30%
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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>54.50
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</td>
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<td>53.92
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</td>
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<td>98.94%
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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>73.79</strong>
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</td>
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<td><strong>73.44</strong>
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</td>
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<td><strong>99.52%</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/Meta-Llama-3.1-8B-Instruct-FP8",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \
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--tasks mmlu \
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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-FP8",dtype=auto,add_bos_token=True,max_model_len=4096,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-FP8",dtype=auto,add_bos_token=True,max_model_len=4096,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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lm_eval \
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--model vllm \
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||||
--model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-FP8",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \
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||||
--tasks gsm8k_cot_llama_3.1_instruct \
|
||||
--apply_chat_template \
|
||||
--fewshot_as_multiturn \
|
||||
--num_fewshot 8 \
|
||||
--batch_size auto
|
||||
```
|
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|
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#### Hellaswag
|
||||
```
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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-FP8",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \
|
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--tasks hellaswag \
|
||||
--num_fewshot 10 \
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--batch_size auto
|
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```
|
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|
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#### Winogrande
|
||||
```
|
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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-FP8",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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|
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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-FP8",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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79
config.json
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79
config.json
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{
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"_name_or_path": "/home/linghao/.cache/huggingface/hub/models--meta-llama--Meta-Llama-3.1-8B-Instruct/snapshots/5206a32e0bd3067aef1ce90f5528ade7d866253f",
|
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"architectures": [
|
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"LlamaForCausalLM"
|
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],
|
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"attention_bias": false,
|
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"attention_dropout": 0.0,
|
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"bos_token_id": 128000,
|
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"eos_token_id": [
|
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128001,
|
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128008,
|
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128009
|
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],
|
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 131072,
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"mlp_bias": false,
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"model_type": "llama",
|
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
|
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"num_key_value_heads": 8,
|
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
|
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"rope_scaling": {
|
||||
"factor": 8.0,
|
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"high_freq_factor": 4.0,
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"low_freq_factor": 1.0,
|
||||
"original_max_position_embeddings": 8192,
|
||||
"rope_type": "llama3"
|
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},
|
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"rope_theta": 500000.0,
|
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"tie_word_embeddings": false,
|
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"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.44.0",
|
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"use_cache": true,
|
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"vocab_size": 128256,
|
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"quantization_config": {
|
||||
"config_groups": {
|
||||
"group_0": {
|
||||
"input_activations": {
|
||||
"block_structure": null,
|
||||
"dynamic": false,
|
||||
"group_size": null,
|
||||
"num_bits": 8,
|
||||
"observer": "minmax",
|
||||
"observer_kwargs": {},
|
||||
"strategy": "tensor",
|
||||
"symmetric": true,
|
||||
"type": "float"
|
||||
},
|
||||
"output_activations": null,
|
||||
"targets": [
|
||||
"Linear"
|
||||
],
|
||||
"weights": {
|
||||
"block_structure": null,
|
||||
"dynamic": false,
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||||
}
|
||||
10
recipe.yaml
Normal file
10
recipe.yaml
Normal file
@@ -0,0 +1,10 @@
|
||||
quant_stage:
|
||||
quant_modifiers:
|
||||
QuantizationModifier:
|
||||
ignore: [lm_head]
|
||||
config_groups:
|
||||
group_0:
|
||||
weights: {num_bits: 8, type: float, strategy: tensor, dynamic: false, symmetric: true}
|
||||
input_activations: {num_bits: 8, type: float, strategy: tensor, dynamic: false,
|
||||
symmetric: true}
|
||||
targets: [Linear]
|
||||
17
special_tokens_map.json
Normal file
17
special_tokens_map.json
Normal file
@@ -0,0 +1,17 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<|begin_of_text|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|eot_id|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": "<|eot_id|>"
|
||||
}
|
||||
410563
tokenizer.json
Normal file
410563
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
2063
tokenizer_config.json
Normal file
2063
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user