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---
library_name: transformers
tags:
- pytorch
datasets:
- allenai/c4
language:
- en
base_model:
- mistralai/Mistral-7B-Instruct-v0.3
---
# Model Card for Mistral-7B-Instruct-v0.3-GPTQ-4bit-gs128
<!-- 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: [Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3)
Model creator: [mistralai](https://huggingface.co/mistralai)
## 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: 4.17GB
For more details, see `quantization_config.json`
## Usage
This model can be used with Transformers:
### Transformers pipeline
```python
import transformers
import torch
model_id = "marinarosell/Mistral-7B-Instruct-v0.3-GPTQ-4bit-gs128"
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?"},
]
outputs = pipeline(
messages,
max_new_tokens=256,
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/Mistral-7B-Instruct-v0.3-GPTQ-4bit-gs128"
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)
outputs = model.generate(
input_ids,
max_new_tokens=256,
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. |