README.md

---
library_name: transformers
tags:
- '-medical'
datasets:
- Mohammed-Altaf/medical-instruction-120k
---

## Model Details

### Model Description

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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

- **Developed by:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]

### Model Sources [optional]

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## Uses

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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model



```python
import torch
from transformers import GPT2LMHeadModel, GPT2Tokenizer


path = "Vidyuth/GPT2-finetuned-medical-instructions"
device = "cuda" if torch.cuda.is_available() else "cpu"
tokenizer = GPT2Tokenizer.from_pretrained(path)
model = GPT2LMHeadModel.from_pretrained(path).to(device)

prompt_input = (
    "The conversation between human and AI assistant.\n"
    "[|Human|] {input}\n"
    "[|AI|]"
)
sentence = prompt_input.format_map({'input': "what are the symptoms of covid?"})
inputs = tokenizer(sentence, return_tensors="pt").to(device)

with torch.no_grad():
    beam_output = model.generate(**inputs,
                                min_new_tokens=1, 
                                max_length=512,
                                num_beams=3,
                                repetition_penalty=1.2,
                                early_stopping=True,
                                eos_token_id=198 
                                )
    print(tokenizer.decode(beam_output[0], skip_special_tokens=True))
```


## Training Details

### Training Data

<!https://huggingface.co/datasets/Mohammed-Altaf/medical-instruction-120k>

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### Training Procedure 

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

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## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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