---
library_name: transformers
tags:
- facebook
- meta
- llama
- llama-3
- pytorch
datasets:
- allenai/c4
language:
- en
base_model:
- meta-llama/Meta-Llama-3-8B-Instruct
---

# Model Card for Meta-Llama-3-8B-Instruct-GPTQ-4bit

<!-- Provide a quick summary of what the model is/does. -->
This model has been quantized to optimize performance and reduce memory usage without compromising accuracy significantly. The quantization process was performed using GPTQ with the `GPTQConfig` class from the `transformers` library.

Original Model: [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)

Model creator: [Meta](https://huggingface.co/meta-llama)


## Quantization Configuration

<!-- Provide a longer summary of what this model is. -->

- Bits: 4
- Data Type: INT4
- GPTQ group size: 128
- Act Order: True
- GPTQ Calibration Dataset: [C4](https://huggingface.co/datasets/allenai/c4)
- Model size: 5.73GB

For more details, see `quantization_config.json`

## Usage

This model can be used with Transformers the same way as the original Meta-Llama-3-8B-Instruct:

### Transformers pipeline

```python
import transformers
import torch

model_id = "marinarosell/Meta-Llama-3-8B-Instruct-GPTQ-4bit"

pipeline = transformers.pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device_map="auto",
)

messages = [
    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
    {"role": "user", "content": "Who are you?"},
]

terminators = [
    pipeline.tokenizer.eos_token_id,
    pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

outputs = pipeline(
    messages,
    max_new_tokens=256,
    eos_token_id=terminators,
    do_sample=True,
    temperature=0.6,
    top_p=0.9,
)
print(outputs[0]["generated_text"][-1])
```

### Transformers AutoModelForCausalLM

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "marinarosell/Meta-Llama-3-8B-Instruct-GPTQ-4bit"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    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))
```

### Example Applications

Chatbots: Lightweight conversational agents.