README.md

---
license: mit
base_model:
- mistralai/Mistral-7B-Instruct-v0.3
- uukuguy/speechless-code-mistral-7b-v2.0
- Nondzu/Mistral-7B-Instruct-v0.2-code-ft
- teknium/OpenHermes-2.5-Mistral-7B
- meta-math/MetaMath-Mistral-7B
tags:
- merge
- mergekit
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.3
- uukuguy/speechless-code-mistral-7b-v2.0
- Nondzu/Mistral-7B-Instruct-v0.2-code-ft
- teknium/OpenHermes-2.5-Mistral-7B
- meta-math/MetaMath-Mistral-7B
---

# Axon26-Coder

Axon26-Coder is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3)
* [uukuguy/speechless-code-mistral-7b-v2.0](https://huggingface.co/uukuguy/speechless-code-mistral-7b-v2.0)
* [Nondzu/Mistral-7B-Instruct-v0.2-code-ft](https://huggingface.co/Nondzu/Mistral-7B-Instruct-v0.2-code-ft)
* [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B)
* [meta-math/MetaMath-Mistral-7B](https://huggingface.co/meta-math/MetaMath-Mistral-7B)

## 🧩 Configuration
```yaml
merge_method: dare_ties
base_model: mistralai/Mistral-7B-Instruct-v0.3
models:
  - model: mistralai/Mistral-7B-Instruct-v0.3
    parameters:
      weight: 0.15
      density: 0.5
  - model: uukuguy/speechless-code-mistral-7b-v2.0
    parameters:
      weight: 0.25
      density: 0.7
  - model: Nondzu/Mistral-7B-Instruct-v0.2-code-ft
    parameters:
      weight: 0.2
      density: 0.6
  - model: teknium/OpenHermes-2.5-Mistral-7B
    parameters:
      weight: 0.2
      density: 0.6
  - model: meta-math/MetaMath-Mistral-7B
    parameters:
      weight: 0.2
      density: 0.6
parameters:
  int8_mask: true
  normalize: true
dtype: bfloat16
tokenizer_source: mistralai/Mistral-7B-Instruct-v0.3
```

## 💻 Usage
```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "AIencoder/Axon26-Coder"
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"])
```