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---
language:
- en
pipeline_tag: text-generation
license: llama3
license_link: https://llama.meta.com/llama3/license/
---
# Meta-Llama-3-8B-Instruct-quantized.w8a8
## Model Overview
- **Model Architecture:** Meta-Llama-3
- **Input:** Text
- **Output:** Text
- **Model Optimizations:**
- **Activation quantization:** INT8
- **Weight quantization:** INT8
- **Intended Use Cases:** Intended for commercial and research use in English. Similarly to [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-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:** 7/11/2024
- **Version:** 1.0
- **License(s):** [Llama3](https://llama.meta.com/llama3/license/)
- **Model Developers:** Neural Magic
Quantized version of [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct).
It achieves an average score of 68.66 on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark (version 1), whereas the unquantized model achieves 68.54.
### Model Optimizations
This model was obtained by quantizing the weights of [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) to INT8 data type.
This optimization reduces the number of bits used to represent weights and activations from 16 to 8, reducing GPU memory requirements (by approximately 50%) and increasing matrix-multiply compute throughput (by approximately 2x).
Weight quantization also reduces disk size requirements by approximately 50%.
Only weights and activations of the linear operators within transformers blocks are quantized.
Weights are quantized with a symmetric static per-channel scheme, where a fixed linear scaling factor is applied between INT8 and floating point representations for each output channel dimension.
Activations are quantized with a symmetric dynamic per-token scheme, computing a linear scaling factor at runtime for each token between INT8 and floating point representations.
The [GPTQ](https://arxiv.org/abs/2210.17323) algorithm is applied for quantization, as implemented in the [llm-compressor](https://github.com/vllm-project/llm-compressor) library.
GPTQ used a 1% damping factor and 256 sequences of 8,192 random tokens.
## Deployment
### Use with vLLM
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-3-8B-Instruct-quantized.w8a8"
number_gpus = 1
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)
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.
### Use with transformers
The following example contemplates how the model can be deployed in Transformers using the `generate()` function.
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "neuralmagic/Meta-Llama-3-8B-Instruct-quantized.w8a8"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
)
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
input_ids = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = model.generate(
input_ids,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))
```
## Creation
This model was created by using the [llm-compressor](https://github.com/vllm-project/llm-compressor) library as presented in the code snipet below.
```python
from transformers import AutoTokenizer
from datasets import Dataset
from llmcompressor.transformers import SparseAutoModelForCausalLM, oneshot
from llmcompressor.modifiers.quantization import GPTQModifier
import random
model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
num_samples = 256
max_seq_len = 8192
tokenizer = AutoTokenizer.from_pretrained(model_id)
max_token_id = len(tokenizer.get_vocab()) - 1
input_ids = [[random.randint(0, max_token_id) for _ in range(max_seq_len)] for _ in range(num_samples)]
attention_mask = num_samples * [max_seq_len * [1]]
ds = Dataset.from_dict({"input_ids": input_ids, "attention_mask": attention_mask})
recipe = GPTQModifier(
targets="Linear",
scheme="W8A8",
ignore=["lm_head"],
dampening_frac=0.01,
)
model = SparseAutoModelForCausalLM.from_pretrained(
model_id,
device_map="auto",
trust_remote_code=True,
)
oneshot(
model=model,
dataset=ds,
recipe=recipe,
max_seq_length=max_seq_len,
num_calibration_samples=num_samples,
)
model.save_pretrained("Meta-Llama-3-8B-Instruct-quantized.w8a8")
```
## Evaluation
The model was evaluated on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) leaderboard tasks (version 1) with the [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/tree/383bbd54bc621086e05aa1b030d8d4d5635b25e6) (commit 383bbd54bc621086e05aa1b030d8d4d5635b25e6) and the [vLLM](https://docs.vllm.ai/en/stable/) engine, using the following command:
```
lm_eval \
--model vllm \
--model_args pretrained="neuralmagic/Meta-Llama-3-8B-Instruct-quantized.w8a8",dtype=auto,gpu_memory_utilization=0.4,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \
--tasks openllm \
--batch_size auto
```
### Accuracy
#### Open LLM Leaderboard evaluation scores
<table>
<tr>
<td><strong>Benchmark</strong>
</td>
<td><strong>Meta-Llama-3-8B-Instruct </strong>
</td>
<td><strong>Meta-Llama-3-8B-Instruct-quantized.w8a8 (this model)</strong>
</td>
<td><strong>Recovery</strong>
</td>
</tr>
<tr>
<td>MMLU (5-shot)
</td>
<td>66.54
</td>
<td>66.13
</td>
<td>99.4%
</td>
</tr>
<tr>
<td>ARC Challenge (25-shot)
</td>
<td>62.63
</td>
<td>62.20
</td>
<td>99.3%
</td>
</tr>
<tr>
<td>GSM-8K (5-shot, strict-match)
</td>
<td>75.21
</td>
<td>76.27
</td>
<td>101.4%
</td>
</tr>
<tr>
<td>Hellaswag (10-shot)
</td>
<td>78.81
</td>
<td>78.41
</td>
<td>99.5%
</td>
</tr>
<tr>
<td>Winogrande (5-shot)
</td>
<td>76.48
</td>
<td>76.48
</td>
<td>100.0%
</td>
</tr>
<tr>
<td>TruthfulQA (0-shot)
</td>
<td>52.49
</td>
<td>52.49
</td>
<td>100.0%
</td>
</tr>
<tr>
<td><strong>Average</strong>
</td>
<td><strong>68.69</strong>
</td>
<td><strong>68.66</strong>
</td>
<td><strong>100.0%</strong>
</td>
</tr>
</table>

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"registry_requires_subclass": false,
"sparsity_structure": "unstructured"
}
}
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}

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quant_stage:
quant_modifiers:
GPTQModifier:
sequential_update: false
dampening_frac: 0.01
ignore: [lm_head]
config_groups:
group_0:
targets: [Linear]
weights: {num_bits: 8, type: int, symmetric: true, strategy: channel}
input_activations: {num_bits: 8, type: int, symmetric: true, dynamic: true, strategy: token}

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{
"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|>"
}

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