README.md

---
license: apache-2.0
tags:
- finetuned
- quantized
- 4-bit
- gptq
- transformers
- safetensors
- mistral
- text-generation
- reward model
- RLHF
- RLAIF
- conversational
- en
- dataset:berkeley-nest/Nectar
- arxiv:1909.08593
- license:apache-2.0
- autotrain_compatible
- endpoints_compatible
- has_space
- text-generation-inference
- region:us
model_name: Starling-LM-7B-beta-GPTQ
base_model: Nexusflow/Starling-LM-7B-beta
inference: false
model_creator: Nexusflow
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# Description
[MaziyarPanahi/Starling-LM-7B-beta-GPTQ](https://huggingface.co./MaziyarPanahi/Starling-LM-7B-beta-GPTQ) is a quantized (GPTQ) version of [Nexusflow/Starling-LM-7B-beta](https://huggingface.co./Nexusflow/Starling-LM-7B-beta)

## How to use
### Install the necessary packages

```
pip install --upgrade accelerate auto-gptq transformers
```

### Example Python code


```python
from transformers import AutoTokenizer, pipeline
from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
import torch

model_id = "MaziyarPanahi/Starling-LM-7B-beta-GPTQ"

quantize_config = BaseQuantizeConfig(
        bits=4,
        group_size=128,
        desc_act=False
    )

model = AutoGPTQForCausalLM.from_quantized(
        model_id,
        use_safetensors=True,
        device="cuda:0",
        quantize_config=quantize_config)

tokenizer = AutoTokenizer.from_pretrained(model_id)

pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    max_new_tokens=512,
    temperature=0.7,
    top_p=0.95,
    repetition_penalty=1.1
)

outputs = pipe("What is a large language model?")
print(outputs[0]["generated_text"])
```