File size: 1,537 Bytes
fda2232 48893bd fda2232 48893bd fda2232 48893bd fda2232 48893bd fda2232 48893bd fda2232 48893bd fda2232 48893bd fda2232 48893bd fda2232 48893bd fda2232 48893bd fda2232 48893bd fda2232 48893bd fda2232 48893bd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 |
---
license: apache-2.0
---
# DrKlaus-7B
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> |