---
license: apache-2.0
tags:
- finetuned
- quantized
- 4-bit
- gptq
- transformers
- safetensors
- mixtral
- text-generation
- moe
- DPO
- RL-TUNED
- license:mit
- autotrain_compatible
- endpoints_compatible
- has_space
- text-generation-inference
- region:us
model_name: Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B-GPTQ
base_model: yunconglong/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B
inference: false
model_creator: yunconglong
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# Description
[MaziyarPanahi/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B-GPTQ](https://huggingface.co./MaziyarPanahi/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B-GPTQ) is a quantized (GPTQ) version of [yunconglong/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B](https://huggingface.co./yunconglong/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B)

## 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/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B-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"])
```