README.md

---
base_model:
- OpenPipe/mistral-ft-optimized-1227
- mlabonne/NeuralHermes-2.5-Mistral-7B
tags:
- merge
- mergekit
- lazymergekit
- OpenPipe/mistral-ft-optimized-1227
- mlabonne/NeuralHermes-2.5-Mistral-7B
license: apache-2.0
---

# llambses-1

llambses-1 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [OpenPipe/mistral-ft-optimized-1227](https://huggingface.co/OpenPipe/mistral-ft-optimized-1227)
* [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B)

## 🧩 Configuration

```yaml
models:
  - model: Kukedlc/NeuralSynthesis-7b-v0.4-slerp
  - model: OpenPipe/mistral-ft-optimized-1227
    parameters:
      density: 0.5
      weight: 0.6
  - model: mlabonne/NeuralHermes-2.5-Mistral-7B
    parameters:
      density: 0.5
      weight: 0.4
merge_method: ties
base_model: Kukedlc/NeuralSynthesis-7b-v0.4-slerp
parameters:
  normalize: true
dtype: float16
```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "bfuzzy1/llambses-1"

chat_template = """{% for message in messages %}
    {% if message['role'] == 'user' %}
        {{ bos_token + '[INST] ' + message['content'] + ' [/INST]' }}
    {% elif message['role'] == 'assistant' %}
        {{ message['content'] + eos_token }}
    {% elif message['role'] == 'system' %}
        {{ '<<SYS>>\\n' + message['content'] + '\\n<</SYS>>\\n\\n' }}
    {% endif %}
{% endfor %}
"""

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "What is a large language model?"}
]


tokenizer = AutoTokenizer.from_pretrained(model)
template = tokenizer.chat_template = chat_template
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=100, do_sample=True, temperature=0.7, top_k=3, top_p=0.95)
print(outputs[0]["generated_text"])
```