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---
tags:
- int4
- vllm
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
pipeline_tag: text-generation
license: llama3.1
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
---

# 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):** Llama3.1
- **Model Developers:** Neural Magic

This model is a quantized version of [Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct).
It was evaluated on a several tasks to assess the its quality in comparison to the unquatized model, including multiple-choice, math reasoning, and open-ended text generation.
Meta-Llama-3.1-8B-Instruct-quantized.w4a16 achieves 93.0% recovery for the Arena-Hard evaluation, 98.9% for OpenLLM v1 (using Meta's prompting when available), 96.1% for OpenLLM v2, 99.7% for HumanEval pass@1, and 97.4% for HumanEval+ pass@1.

### 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-group quantization is applied, in which a linear scaling per group of 128 parameters maps the INT4 and floating point representations of the quantized weights.
[AutoGPTQ](https://github.com/AutoGPTQ/AutoGPTQ) is used for quantization with 10% damping factor and 768 sequences taken from Neural Magic's [LLM compression calibration dataset](https://huggingface.co/datasets/neuralmagic/LLM_compression_calibration).


## 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 applying the [AutoGPTQ](https://github.com/AutoGPTQ/AutoGPTQ) library as presented in the code snipet below.
Although AutoGPTQ was used for this particular model, Neural Magic is transitioning to using [llm-compressor](https://github.com/vllm-project/llm-compressor) which supports several quantization schemes and models not supported by AutoGPTQ.

```python
from transformers import AutoTokenizer
from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
from datasets import load_dataset

model_id = "meta-llama/Meta-Llama-3.1-8B-Instruct"

num_samples = 756
max_seq_len = 4064

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]
    
quantize_config = BaseQuantizeConfig(
  bits=4,
  group_size=128,
  desc_act=True,
  model_file_base_name="model",
  damp_percent=0.1,
)

model = AutoGPTQForCausalLM.from_pretrained(
  model_id,
  quantize_config,
  device_map="auto",
)

model.quantize(examples)
model.save_pretrained("Meta-Llama-3.1-8B-Instruct-quantized.w4a16")
```

## Evaluation

This model was evaluated on the well-known Arena-Hard, OpenLLM v1, OpenLLM v2, HumanEval, and HumanEval+ benchmarks.
In all cases, model outputs were generated with the [vLLM](https://docs.vllm.ai/en/stable/) engine.

Arena-Hard evaluations were conducted using the [Arena-Hard-Auto](https://github.com/lmarena/arena-hard-auto) repository.
The model generated a single answer for each prompt form Arena-Hard, and each answer was judged twice by GPT-4.
We report below the scores obtained in each judgement and the average.

OpenLLM v1 and v2 evaluations were conducted using Neural Magic's fork of [lm-evaluation-harness](https://github.com/neuralmagic/lm-evaluation-harness/tree/llama_3.1_instruct) (branch llama_3.1_instruct).
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) and a few fixes to OpenLLM v2 tasks.

HumanEval and HumanEval+ evaluations were conducted using Neural Magic's fork of the [EvalPlus](https://github.com/neuralmagic/evalplus) repository.

Detailed model outputs are available as HuggingFace datasets for [Arena-Hard](https://huggingface.co/datasets/neuralmagic/quantized-llama-3.1-arena-hard-evals), [OpenLLM v2](https://huggingface.co/datasets/neuralmagic/quantized-llama-3.1-leaderboard-v2-evals), and [HumanEval](https://huggingface.co/datasets/neuralmagic/quantized-llama-3.1-humaneval-evals).

**Note:** Results have been updated after Meta modified the chat template.

### Accuracy

<table>
  <tr>
   <td><strong>Category</strong>
   </td>
   <td><strong>Benchmark</strong>
   </td>
   <td><strong>Meta-Llama-3.1-8B-Instruct </strong>
   </td>
   <td><strong>Meta-Llama-3.1-8B-Instruct-quantized.w4a16 (this model)</strong>
   </td>
   <td><strong>Recovery</strong>
   </td>
  </tr>
  <tr>
   <td rowspan="1" ><strong>LLM as a judge</strong>
   </td>    
   <td>Arena Hard
   </td>
   <td>25.8 (25.1 / 26.5)
   </td>
   <td>27.2 (27.6 / 26.7)
   </td>
   <td>105.4%
   </td>
  </tr>
  <tr>
   <td rowspan="8" ><strong>OpenLLM v1</strong>
   </td>
   <td>MMLU (5-shot)
   </td>
   <td>68.3
   </td>
   <td>66.9
   </td>
   <td>97.9%
   </td>
  </tr>
  <tr>
   <td>MMLU (CoT, 0-shot)
   </td>
   <td>72.8
   </td>
   <td>71.1
   </td>
   <td>97.6%
   </td>
  </tr>
  <tr>
   <td>ARC Challenge (0-shot)
   </td>
   <td>81.4
   </td>
   <td>80.2
   </td>
   <td>98.0%
   </td>
  </tr>
  <tr>
   <td>GSM-8K (CoT, 8-shot, strict-match)
   </td>
   <td>82.8
   </td>
   <td>82.9
   </td>
   <td>100.2%
   </td>
  </tr>
  <tr>
   <td>Hellaswag (10-shot)
   </td>
   <td>80.5
   </td>
   <td>79.9
   </td>
   <td>99.3%
   </td>
  </tr>
  <tr>
   <td>Winogrande (5-shot)
   </td>
   <td>78.1
   </td>
   <td>78.0
   </td>
   <td>99.9%
   </td>
  </tr>
  <tr>
   <td>TruthfulQA (0-shot, mc2)
   </td>
   <td>54.5
   </td>
   <td>52.8
   </td>
   <td>96.9%
   </td>
  </tr>
  <tr>
   <td><strong>Average</strong>
   </td>
   <td><strong>74.3</strong>
   </td>
   <td><strong>73.5</strong>
   </td>
   <td><strong>98.9%</strong>
   </td>
  </tr>
  <tr>
   <td rowspan="7" ><strong>OpenLLM v2</strong>
   </td>
   <td>MMLU-Pro (5-shot)
   </td>
   <td>30.8
   </td>
   <td>28.8
   </td>
   <td>93.6%
   </td>
  </tr>
  <tr>
   <td>IFEval (0-shot)
   </td>
   <td>77.9
   </td>
   <td>76.3
   </td>
   <td>98.0%
   </td>
  </tr>
  <tr>
   <td>BBH (3-shot)
   </td>
   <td>30.1
   </td>
   <td>28.9
   </td>
   <td>96.1%
   </td>
  </tr>
  <tr>
   <td>Math-lvl-5 (4-shot)
   </td>
   <td>15.7
   </td>
   <td>14.8
   </td>
   <td>94.4%
   </td>
  </tr>
  <tr>
   <td>GPQA (0-shot)
   </td>
   <td>3.7
   </td>
   <td>4.0
   </td>
   <td>109.8%
   </td>
  </tr>
  <tr>
   <td>MuSR (0-shot)
   </td>
   <td>7.6
   </td>
   <td>6.3
   </td>
   <td>83.2%
   </td>
  </tr>
  <tr>
   <td><strong>Average</strong>
   </td>
   <td><strong>27.6</strong>
   </td>
   <td><strong>26.5</strong>
   </td>
   <td><strong>96.1%</strong>
   </td>
  </tr>
  <tr>
   <td rowspan="2" ><strong>Coding</strong>
   </td>
   <td>HumanEval pass@1
   </td>
   <td>67.3
   </td>
   <td>67.1
   </td>
   <td>99.7%
   </td>
  </tr>
  <tr>
   <td>HumanEval+ pass@1
   </td>
   <td>60.7
   </td>
   <td>59.1
   </td>
   <td>97.4%
   </td>
  </tr>
  <tr>
   <td rowspan="9" ><strong>Multilingual</strong>
   </td>
   <td>Portuguese MMLU (5-shot)
   </td>
   <td>59.96
   </td>
   <td>58.69
   </td>
   <td>97.9%
   </td>
  </tr>
  <tr>
   <td>Spanish MMLU (5-shot)
   </td>
   <td>60.25
   </td>
   <td>58.39
   </td>
   <td>96.9%
   </td>
  </tr>
  <tr>
   <td>Italian MMLU (5-shot)
   </td>
   <td>59.23
   </td>
   <td>57.82
   </td>
   <td>97.6%
   </td>
  </tr>
  <tr>
   <td>German MMLU (5-shot)
   </td>
   <td>58.63
   </td>
   <td>56.22
   </td>
   <td>95.9%
   </td>
  </tr>
  <tr>
   <td>French MMLU (5-shot)
   </td>
   <td>59.65
   </td>
   <td>57.58
   </td>
   <td>96.5%
   </td>
  </tr>
  <tr>
   <td>Hindi MMLU (5-shot)
   </td>
   <td>50.10
   </td>
   <td>47.14
   </td>
   <td>94.1%
   </td>
  </tr>
  <tr>
   <td>Thai MMLU (5-shot)
   </td>
   <td>49.12
   </td>
   <td>46.72
   </td>
   <td>95.1%
   </td>
  </tr>
</table>


### Reproduction

The results were obtained using the following commands:

#### MMLU
```
lm_eval \
  --model vllm \
  --model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16",dtype=auto,max_model_len=3850,max_gen_toks=10,tensor_parallel_size=1 \
  --tasks mmlu_llama_3.1_instruct \
  --fewshot_as_multiturn \
  --apply_chat_template \
  --num_fewshot 5 \
  --batch_size auto
```

#### MMLU-CoT
```
lm_eval \
  --model vllm \
  --model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16",dtype=auto,max_model_len=4064,max_gen_toks=1024,tensor_parallel_size=1 \
  --tasks mmlu_cot_0shot_llama_3.1_instruct \
  --apply_chat_template \
  --num_fewshot 0 \
  --batch_size auto
```

#### ARC-Challenge
```
lm_eval \
  --model vllm \
  --model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16",dtype=auto,max_model_len=3940,max_gen_toks=100,tensor_parallel_size=1 \
  --tasks arc_challenge_llama_3.1_instruct \
  --apply_chat_template \
  --num_fewshot 0 \
  --batch_size auto
```

#### GSM-8K
```
lm_eval \
  --model vllm \
  --model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16",dtype=auto,max_model_len=4096,max_gen_toks=1024,tensor_parallel_size=1 \
  --tasks gsm8k_cot_llama_3.1_instruct \
  --fewshot_as_multiturn \
  --apply_chat_template \
  --num_fewshot 8 \
  --batch_size auto
```

#### Hellaswag
```
lm_eval \
  --model vllm \
  --model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \
  --tasks hellaswag \
  --num_fewshot 10 \
  --batch_size auto
```

#### Winogrande
```
lm_eval \
  --model vllm \
  --model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \
  --tasks winogrande \
  --num_fewshot 5 \
  --batch_size auto
```

#### TruthfulQA
```
lm_eval \
  --model vllm \
  --model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \
  --tasks truthfulqa \
  --num_fewshot 0 \
  --batch_size auto
```

#### OpenLLM v2
```
lm_eval \
  --model vllm \
  --model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16",dtype=auto,max_model_len=4096,tensor_parallel_size=1,enable_chunked_prefill=True \
  --apply_chat_template \
  --fewshot_as_multiturn \
  --tasks leaderboard \
  --batch_size auto
```

#### MMLU Portuguese
```
lm_eval \
  --model vllm \
  --model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16",dtype=auto,max_model_len=3850,max_gen_toks=10,tensor_parallel_size=1 \
  --tasks mmlu_pt_llama_3.1_instruct \
  --fewshot_as_multiturn \
  --apply_chat_template \
  --num_fewshot 5 \
  --batch_size auto
```

#### MMLU Spanish
```
lm_eval \
  --model vllm \
  --model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16",dtype=auto,max_model_len=3850,max_gen_toks=10,tensor_parallel_size=1 \
  --tasks mmlu_es_llama_3.1_instruct \
  --fewshot_as_multiturn \
  --apply_chat_template \
  --num_fewshot 5 \
  --batch_size auto
```

#### MMLU Italian
```
lm_eval \
  --model vllm \
  --model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16",dtype=auto,max_model_len=3850,max_gen_toks=10,tensor_parallel_size=1 \
  --tasks mmlu_it_llama_3.1_instruct \
  --fewshot_as_multiturn \
  --apply_chat_template \
  --num_fewshot 5 \
  --batch_size auto
```

#### MMLU German
```
lm_eval \
  --model vllm \
  --model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16",dtype=auto,max_model_len=3850,max_gen_toks=10,tensor_parallel_size=1 \
  --tasks mmlu_de_llama_3.1_instruct \
  --fewshot_as_multiturn \
  --apply_chat_template \
  --num_fewshot 5 \
  --batch_size auto
```

#### MMLU French
```
lm_eval \
  --model vllm \
  --model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16",dtype=auto,max_model_len=3850,max_gen_toks=10,tensor_parallel_size=1 \
  --tasks mmlu_fr_llama_3.1_instruct \
  --fewshot_as_multiturn \
  --apply_chat_template \
  --num_fewshot 5 \
  --batch_size auto
```

#### MMLU Hindi
```
lm_eval \
  --model vllm \
  --model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16",dtype=auto,max_model_len=3850,max_gen_toks=10,tensor_parallel_size=1 \
  --tasks mmlu_hi_llama_3.1_instruct \
  --fewshot_as_multiturn \
  --apply_chat_template \
  --num_fewshot 5 \
  --batch_size auto
```

#### MMLU Thai
```
lm_eval \
  --model vllm \
  --model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16",dtype=auto,max_model_len=3850,max_gen_toks=10,tensor_parallel_size=1 \
  --tasks mmlu_th_llama_3.1_instruct \
  --fewshot_as_multiturn \
  --apply_chat_template \
  --num_fewshot 5 \
  --batch_size auto
```

#### HumanEval and HumanEval+
##### Generation
```
python3 codegen/generate.py \
  --model neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16 \
  --bs 16 \
  --temperature 0.2 \
  --n_samples 50 \
  --root "." \
  --dataset humaneval
```
##### Sanitization
```
python3 evalplus/sanitize.py \
  humaneval/neuralmagic--Meta-Llama-3.1-8B-Instruct-quantized.w4a16_vllm_temp_0.2
```
##### Evaluation
```
evalplus.evaluate \
  --dataset humaneval \
  --samples humaneval/neuralmagic--Meta-Llama-3.1-8B-Instruct-quantized.w4a16_vllm_temp_0.2-sanitized
```