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---
license: apache-2.0
tags:
- merge
- mergekit
- BioMistral/BioMistral-7B-DARE
- NousResearch/Nous-Hermes-2-Mistral-7B-DPO
---
# BioMistral-Hermes-Slerp
BioMistral-Hermes-Slerp is a merge of the following models:
* [BioMistral/BioMistral-7B-DARE](https://huggingface.co/BioMistral/BioMistral-7B-DARE)
* [NousResearch/Nous-Hermes-2-Mistral-7B-DPO](https://huggingface.co/NousResearch/Nous-Hermes-2-Mistral-7B-DPO)
## Evaluations
| Benchmark | BioMistral-Hermes-Slerp | Orca-2-7b | llama-2-7b | meditron-7b | meditron-70b |
| --- | --- | --- | --- | --- | --- |
| MedMCQA | | | | | |
| ClosedPubMedQA | | | | | |
| PubMedQA | | | | | |
| MedQA | | | | | |
| MedQA4 | | | | | |
| MedicationQA | | | | | |
| MMLU Medical | | | | | |
| MMLU | | | | | |
| TruthfulQA | | | | | |
| GSM8K | | | | | |
| ARC | | | | | |
| HellaSwag | | | | | |
| Winogrande | | | | | |
More details on the Open LLM Leaderboard evaluation results can be found here.
## 🧩 Configuration
```yaml
slices:
- sources:
- model: BioMistral/BioMistral-7B-DARE
layer_range: [0, 32]
- model: NousResearch/Nous-Hermes-2-Mistral-7B-DPO
layer_range: [0, 32]
merge_method: slerp
base_model: NousResearch/Nous-Hermes-2-Mistral-7B-DPO
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5 # fallback for rest of tensors
dtype: float16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Technoculture/BioMistral-Hermes-Slerp"
messages = [{"role": "user", "content": "I am feeling sleepy these days"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |