---
license: apache-2.0
language:
- en
library_name: transformers
tags:
- 4-bit
- AWQ
- text-generation
- autotrain_compatible
- endpoints_compatible
- medical
datasets:
- Open-Orca/OpenOrca
- pubmed
- medmcqa
- maximegmd/medqa_alpaca_format
base_model: mistralai/Mistral-7B-v0.1
metrics:
- accuracy
pipeline_tag: text-generation
inference: false
quantized_by: Suparious
---
# internistai/base-7b-v0.2 AWQ

- Model creator: [internistai](https://huggingface.co/internistai)
- Original model: [base-7b-v0.2](https://huggingface.co/internistai/base-7b-v0.2)

<img width=30% src="assets_logo.png" alt="logo" title="logo">

## Model Summary

Internist.ai 7b is a medical domain large language model trained by medical doctors to demonstrate the benefits of a **physician-in-the-loop** approach. The training data was carefully curated by medical doctors to ensure clinical relevance and required quality for clinical practice.

**With this 7b model we release the first 7b model to score above the 60% pass threshold on MedQA (USMLE) and outperfoms models of similar size accross most medical evaluations.**

This model serves as a proof of concept and larger models trained on a larger corpus of medical literature are planned. Do not hesitate to reach out to us if you would like to sponsor some compute to speed up this training.

## How to use

### Install the necessary packages

```bash
pip install --upgrade autoawq autoawq-kernels
```

### Example Python code

```python
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer, TextStreamer

model_path = "solidrust/base-7b-v0.2-AWQ"
system_message = "You are base-7b-v0.2, incarnated as a powerful AI. You were created by internistai."

# Load model
model = AutoAWQForCausalLM.from_quantized(model_path,
                                          fuse_layers=True)
tokenizer = AutoTokenizer.from_pretrained(model_path,
                                          trust_remote_code=True)
streamer = TextStreamer(tokenizer,
                        skip_prompt=True,
                        skip_special_tokens=True)

# Convert prompt to tokens
prompt_template = """\
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant"""

prompt = "You're standing on the surface of the Earth. "\
        "You walk one mile south, one mile west and one mile north. "\
        "You end up exactly where you started. Where are you?"

tokens = tokenizer(prompt_template.format(system_message=system_message,prompt=prompt),
                  return_tensors='pt').input_ids.cuda()

# Generate output
generation_output = model.generate(tokens,
                                  streamer=streamer,
                                  max_new_tokens=512)
```

### About AWQ

AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.

AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.

It is supported by:

- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code