transformers pytorch llama text-generation medical AI text2text-generation en dataset:abneraigc/wiki_medical_terms_llama base_model:NousResearch/Llama-2-7b-chat-hf base_model:finetune:NousResearch/Llama-2-7b-chat-hf license:mit autotrain_compatible text-generation-inference endpoints_compatible region:us
---
base_model:
- NousResearch/Llama-2-7b-chat-hf
datasets:
- abneraigc/wiki_medical_terms_llama
language:
- en
library_name: transformers
license: mit
metrics:
- perplexity
- accuracy
- rouge
pipeline_tag: text2text-generation
tags:
- medical
- AI
---

# Model Card for Llama-2-7b-chat-finetune

This model is a fine-tuned version of Llama-2-7b for chat-based applications, trained on medical data to answer various queries with detailed medical knowledge.

## Model Details

### Model Description

This model is fine-tuned from Llama-2-7b for answering medical-related queries and tasks using a large corpus of medical data. It is suitable for generating text based on a given prompt in a conversational style.

- **Developed by:** SURESHBEEKHANI
- **License:** MIT
- **Model type:** Causal Language Model
- **Language(s):** English
- **Finetuned from model:** Llama-2-7b

### Model Sources

- **Repository:** [SURESHBEEKHANI/Llama-2-7b-chat-finetune](https://huggingface.co/SURESHBEEKHANI/Llama-2-7b-chat-finetune)
- **Code Notebook:** [Fine-tune Llama-2-7b](https://github.com/SURESHBEEKHANI/Advanced-LLM-Fine-Tuning/blob/main/fine-tune-llama-2-7b-ipynb.ipynb)

## Use Cases

### Direct Use

This model can be used directly for generating text responses to prompts related to medical topics. It is designed to assist in answering medical queries with detailed information.

### Out-of-Scope Use

This model is not suitable for generating answers related to non-medical domains, and should not be used in contexts where the data might be sensitive, harmful, or biased.

## Bias, Risks, and Limitations

The model might inherit biases from its training data and might not always provide accurate medical information. It is recommended to use the model as a supplementary tool and consult medical professionals for critical use cases.

## How to Get Started with the Model

Use the code below to get started with the model.

```python
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer

model_name = "SURESHBEEKHANI/Llama-2-7b-chat-finetune"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

prompt = " What is Superficial vein thrombosis and explain in detail? ?"
pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=200)
result = pipe(f"<s>[INST] {prompt} [/INST]")
print(result[0]['generated_text'])