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---
license: apache-2.0
tags:
- merge
- mergekit
- segmed/MedMistral-7B-v0.1
- Guilherme34/Samantha-v2
---
# Dr_Samantha_7b_mistral
<p align="center">
<img src="https://huggingface.co/sethuiyer/Dr_Samantha-7b/resolve/main/dr_samantha_anime_style_reduced_quality.webp" height="256px" alt="SynthIQ">
</p>
This model is a merge of the following models made with mergekit(https://github.com/cg123/mergekit):
* [segmed/MedMistral-7B-v0.1](https://huggingface.co/segmed/MedMistral-7B-v0.1)
* [Guilherme34/Samantha-v2](https://huggingface.co/Guilherme34/Samantha-v2)
Has capabilities of a medical knowledge-focused model (trained on USMLE databases and doctor-patient interactions) with the philosophical, psychological, and relational understanding of the Samantha-7b model.
As both a medical consultant and personal counselor, Dr.Samantha could effectively support both physical and mental wellbeing - important for whole-person care.
## 🧩 Configuration
```yaml
slices:
- sources:
- model: segmed/MedMistral-7B-v0.1
layer_range: [0, 32]
- model: Guilherme34/Samantha-v2
layer_range: [0, 32]
merge_method: slerp
base_model: OpenPipe/mistral-ft-optimized-1218
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
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "sethuiyer/Dr_Samantha_7b_mistral"
messages = [{"role": "user", "content": "What is a large language model?"}]
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"])
``` |