

![image/png](https://cdn-uploads.huggingface.co/production/uploads/63732ebbbd81fae2b3aaf3fb/ap2c-DdMn4i6xemiV_adx.png)

This is a 2-bit quantization of @pankajmathur orca_mini_v3_70b using quip# (https://cornell-relaxml.github.io/quip-sharp/) with hessian context lenght 4k. 


Inference with the model is a bit slow, but this should be the best 70b model you can use without offloading on a 3090. 


# Prompt Format:

```
### System:
You are an AI assistant that follows instruction extremely well. Help as much as you can.

### User:
Tell me about Orcas.

### Assistant:

```


I have included the quip library I have used in this repo. 
I am able to use this model in the widely known textgen-webui. For installation I suggest to follow these steps: 

1. Download the quip folder from this repo and place it inside the repositories folder of the textgen-webui folder.
2. install the requirements of quip#
3. compile and install the quiptools cuda lib:

```
pip install fast-hadamard-transform glog==0.3.1 primefac==2.0.12
cd repositories/quip-sharp/quiptools
python setup.py install --force

```

4. reinstall the requirements of textgen-webui
5. load the model with the quip# integration of textgen-webui

You can use the library of this repo also for scripts. Within the quip# folder, after installing the library, use this command: 

```
python interactive_gen.py --hf_path path_to_the_2bitmodel --max_length 500 text
```



{"license": "llama2"}
transformers safetensors llama text-generation license:llama2 autotrain_compatible endpoints_compatible text-generation-inference region:us