README.md

---
tags:
- merge
- mergekit
- lazymergekit
- openchat/openchat-3.5-0106
- teknium/OpenHermes-2.5-Mistral-7B
base_model:
- openchat/openchat-3.5-0106
- teknium/OpenHermes-2.5-Mistral-7B
---

# djinn

djinn is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [openchat/openchat-3.5-0106](https://huggingface.co/openchat/openchat-3.5-0106)
* [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B)

## 🧩 Configuration

```yaml
merge_method: linear # use linear so we can include multiple models, albeit at a zero weight
parameters:
  weight: 1.0 # weight everything as 1 unless specified otherwise - linear with one model weighted at 1 is a no-op like passthrough
slices:
  - sources:
      - model: openchat/openchat-3.5-0106
        layer_range: [0, 1]
      - model: teknium/OpenHermes-2.5-Mistral-7B 
        layer_range: [0, 1]
        parameters:
          weight: 0
  - sources:
      - model: bardsai/jaskier-7b-dpo-v6.1
        layer_range: [1, 10]
  - sources:
      - model: senseable/WestLake-7B-v2
        layer_range: [10, 20]
  - sources:
      - model: NousResearch/Nous-Hermes-2-Mistral-7B-DPO
        layer_range: [20, 30]
  - sources:
      - model: paulml/OGNO-7B
        layer_range: [15, 25]
  - sources:
      - model: paulml/DPOB-INMTOB-7B
        layer_range: [22, 32]
  - sources:
      - model: mlabonne/AlphaMonarch-7B
        layer_range: [5, 15]
  - sources: 
      - model: openchat/openchat-3.5-0106
        layer_range: [31, 32]
      - model: teknium/OpenHermes-2.5-Mistral-7B
        layer_range: [31, 32]
        parameters:
          weight: 0
dtype: float16
tokenizer_source: model:openchat/openchat-3.5-0106
```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mayacinka/djinn"
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"])
```