---
base_model:
- NousResearch/Hermes-2-Pro-Mistral-7B
- mistralai/Mistral-7B-Instruct-v0.2
tags:
- merge
- mergekit
- lazymergekit
- NousResearch/Hermes-2-Pro-Mistral-7B
- mistralai/Mistral-7B-Instruct-v0.2
---

# TranscriptAnalyzer-7B

TranscriptAnalyzer-7B is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [NousResearch/Hermes-2-Pro-Mistral-7B](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B)
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)

## 🧩 Configuration

```yaml
base_model: mistralai/Mistral-7B-v0.1
dtype: float16
merge_method: dare_ties
parameters:
  density: 0.6  # Optimal pour 2 modèles

slices:
  - sources:
    - model: NousResearch/Hermes-2-Pro-Mistral-7B
      layer_range: [0, 32]
      parameters:
        weight: 0.65  # 65% Hermes (analyse)
        density: 0.7
    - model: mistralai/Mistral-7B-Instruct-v0.2
      layer_range: [0, 32]
      parameters:
        weight: 0.35  # 35% Mistral (rapidité)
        density: 0.6
```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "alantcoding/TranscriptAnalyzer-7B"
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"])
```