README.md

---
license: apache-2.0
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- Locutusque/TinyMistral-248M-v2
- Locutusque/TinyMistral-248M-v2.5
- Locutusque/TinyMistral-248M-v2.5-Instruct
- jtatman/tinymistral-v2-pycoder-instruct-248m
- Felladrin/TinyMistral-248M-SFT-v4
- Locutusque/TinyMistral-248M-v2-Instruct
base_model:
- Locutusque/TinyMistral-248M-v2
- Locutusque/TinyMistral-248M-v2.5
- Locutusque/TinyMistral-248M-v2.5-Instruct
- jtatman/tinymistral-v2-pycoder-instruct-248m
- Felladrin/TinyMistral-248M-SFT-v4
- Locutusque/TinyMistral-248M-v2-Instruct
inference:
  parameters:
    do_sample: true
    temperature: 0.2
    top_p: 0.14
    top_k: 12
    max_new_tokens: 250
    repetition_penalty: 1.15
widget:
- text: |
    <|im_start|>user
    Write me a Python program that calculates the factorial of n. <|im_end|>
    <|im_start|>assistant
- text: >-
    An emerging clinical approach to treat substance abuse disorders involves a
    form of cognitive-behavioral therapy whereby addicts learn to reduce their
    reactivity to drug-paired stimuli through cue-exposure or extinction
    training. It is, however,
datasets:
- nampdn-ai/mini-peS2o
---

# TinyMistral-6x248M

TinyMistral-6x248M is a Mixure of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [Locutusque/TinyMistral-248M-v2](https://huggingface.co/Locutusque/TinyMistral-248M-v2)
* [Locutusque/TinyMistral-248M-v2.5](https://huggingface.co/Locutusque/TinyMistral-248M-v2.5)
* [Locutusque/TinyMistral-248M-v2.5-Instruct](https://huggingface.co/Locutusque/TinyMistral-248M-v2.5-Instruct)
* [jtatman/tinymistral-v2-pycoder-instruct-248m](https://huggingface.co/jtatman/tinymistral-v2-pycoder-instruct-248m)
* [Felladrin/TinyMistral-248M-SFT-v4](https://huggingface.co/Felladrin/TinyMistral-248M-SFT-v4)
* [Locutusque/TinyMistral-248M-v2-Instruct](https://huggingface.co/Locutusque/TinyMistral-248M-v2-Instruct)

The resulting model is then pre-trained on 600,000 examples of nampdn-ai/mini-peS2o.

We don't recommend using the Inference API as the model has serious performance degradation.

### Recommended inference parameters

```
do_sample: true
temperature: 0.2
top_p: 0.14
top_k: 12
repetition_penalty: 1.15
```

## 🧩 Configuration

```yaml
base_model: Locutusque/TinyMistral-248M-v2.5
experts:
  - source_model: Locutusque/TinyMistral-248M-v2
    positive_prompts:
      - "An emerging trend in global economics is"
      - "TITLE: The Next Generation of Internet Connectivity"
      - "begin a comprehensive analysis on the sociopolitical effects of"
    negative_prompts:
      - "Code a simple"
      - "Explain the Krebs cycle in detail"
      - "Compose a sonnet about"

  - source_model: Locutusque/TinyMistral-248M-v2.5
    positive_prompts:
      - "Advanced C++ memory management techniques"
      - "C# asynchronous programming best practices"
      - "AI's role in predictive analytics"
      - "textbook review on machine learning algorithms"
      - "## Exercise: Design a C# interface for a CRM system"
      - "## Solution: Optimize an AI-powered recommendation engine"
    negative_prompts:
      - "Narrate the story of"
      - "The ethical considerations in"
      - "Review the latest art exhibition by"
  
  - source_model: Locutusque/TinyMistral-248M-v2.5-Instruct
    positive_prompts:
      - "What is the chemical formula for photosynthesis?"
      - "Identification of a new mineral found on Mars"
      - "physics: Explaining the concept of relativity"
      - "Solve for x using differential equations:"
      - "history: Analyze the causes of the French Revolution"
    negative_prompts:
      - "Devise a business plan for"
      - "The evolution of culinary arts"
      - "Orchestrate a piece for a string quartet"
  
  - source_model: jtatman/tinymistral-v2-pycoder-instruct-248m
    positive_prompts:
      - "Write a Python program for facial recognition"
      - "Explain dynamic typing in programming languages"
      - "algorithm development for efficient data sorting"
    negative_prompts:
      - "Who was the first Emperor of Rome?"
      - "Discuss the political dynamics in"
      - "Provide a proof for Fermat's Last Theorem"
      - "physics: The principles of thermodynamics"
  
  - source_model: Felladrin/TinyMistral-248M-SFT-v4
    positive_prompts:
      - "Escreba sobre a influência da música no Brasil"
      - "Voici un guide pour les voyageurs en France"
      - "Para entender la política de México, se debe considerar"
      - "Cuales son los efectos de la globalización en Argentina"
      - "Welche gesellschaftlichen Veränderungen gibt es in Deutschland"
      - "If you had to imagine a utopian city, what would be its core values?"
    negative_prompts:
      - "Calculate the integral of"
      - "Describe the process of cell division"
      - "Review the latest advancements in quantum computing"

  - source_model: Locutusque/TinyMistral-248M-v2-Instruct
    positive_prompts:
      - "Write an essay on the evolution of international trade laws"
      - "What are the key components of a sustainable urban ecosystem?"
      - "instruct on effective negotiation techniques in diplomacy"
      - "How does cognitive bias affect decision making in high-pressure environments?"
      - "Identify the architectural significance of the Sydney Opera House"
    negative_prompts:
      - "Develop a script to automate"
      - "Understanding inheritance in object-oriented programming"
      - "philosophy of existentialism in contemporary society"
```

## 💻 Usage

```python
!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "M4-ai/TinyMistral-6x248M"

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)

messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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"])
```