transformers safetensors mistral text-generation conversational license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
---
license: apache-2.0
---
# DrKlaus-7B

![image/webp](https://cdn-uploads.huggingface.co/production/uploads/6455cc8d679315e4ef16fbec/E0UeNsU-zKRAwySfeCWf8.webp)

DrKlaus-7B is a SFT model made with [AutoSloth](https://colab.research.google.com/drive/1Zo0sVEb2lqdsUm9dy2PTzGySxdF9CNkc#scrollTo=MmLkhAjzYyJ4) by [macadeliccc](https://huggingface.co/macadeliccc)

## Process

- Original Model: [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
- Datatset: [medalpaca/medical_meadow_wikidoc_patient_information](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc_patient_information)

- Learning Rate: 3e-05
- Steps: 80
- Warmup Steps: 8
- Per Device Train Batch Size: 24
- Gradient Accumulation Steps 12
- Optimizer: adamw_8bit
- Max Sequence Length: 1024
- Max Prompt Length: 512
- Max Length: 1024

## 💻 Usage

```python
!pip install -qU transformers

from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

model = "macadeliccc/DrKlaus-7B"
tokenizer = AutoTokenizer.from_pretrained(model)

# Example prompt
prompt = "Your example prompt here"

# Generate a response
model = AutoModelForCausalLM.from_pretrained(model)
pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer)
outputs = pipeline(prompt, max_length=50, num_return_sequences=1)
print(outputs[0]["generated_text"])
```

<div align="center">
<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/made%20with%20unsloth.png" height="50" align="center" />
</div>