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Model: M4-ai/TinyMistral-6x248M Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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tags:
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- moe
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- frankenmoe
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- merge
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- mergekit
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- lazymergekit
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- Locutusque/TinyMistral-248M-v2
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- Locutusque/TinyMistral-248M-v2.5
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- Locutusque/TinyMistral-248M-v2.5-Instruct
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- jtatman/tinymistral-v2-pycoder-instruct-248m
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- Felladrin/TinyMistral-248M-SFT-v4
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- Locutusque/TinyMistral-248M-v2-Instruct
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base_model:
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- Locutusque/TinyMistral-248M-v2
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- Locutusque/TinyMistral-248M-v2.5
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- Locutusque/TinyMistral-248M-v2.5-Instruct
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- jtatman/tinymistral-v2-pycoder-instruct-248m
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- Felladrin/TinyMistral-248M-SFT-v4
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- Locutusque/TinyMistral-248M-v2-Instruct
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inference:
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parameters:
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do_sample: true
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temperature: 0.2
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top_p: 0.14
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top_k: 12
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max_new_tokens: 250
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repetition_penalty: 1.15
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widget:
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- text: |
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<|im_start|>user
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Write me a Python program that calculates the factorial of n. <|im_end|>
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<|im_start|>assistant
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- text: >-
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An emerging clinical approach to treat substance abuse disorders involves a
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form of cognitive-behavioral therapy whereby addicts learn to reduce their
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reactivity to drug-paired stimuli through cue-exposure or extinction
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training. It is, however,
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datasets:
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- nampdn-ai/mini-peS2o
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---
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# TinyMistral-6x248M
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TinyMistral-6x248M is a Mixure of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
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* [Locutusque/TinyMistral-248M-v2](https://huggingface.co/Locutusque/TinyMistral-248M-v2)
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* [Locutusque/TinyMistral-248M-v2.5](https://huggingface.co/Locutusque/TinyMistral-248M-v2.5)
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* [Locutusque/TinyMistral-248M-v2.5-Instruct](https://huggingface.co/Locutusque/TinyMistral-248M-v2.5-Instruct)
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* [jtatman/tinymistral-v2-pycoder-instruct-248m](https://huggingface.co/jtatman/tinymistral-v2-pycoder-instruct-248m)
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* [Felladrin/TinyMistral-248M-SFT-v4](https://huggingface.co/Felladrin/TinyMistral-248M-SFT-v4)
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* [Locutusque/TinyMistral-248M-v2-Instruct](https://huggingface.co/Locutusque/TinyMistral-248M-v2-Instruct)
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The resulting model is then pre-trained on 600,000 examples of nampdn-ai/mini-peS2o.
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We don't recommend using the Inference API as the model has serious performance degradation.
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### Recommended inference parameters
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```
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do_sample: true
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temperature: 0.2
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top_p: 0.14
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top_k: 12
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repetition_penalty: 1.15
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```
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## 🧩 Configuration
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```yaml
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base_model: Locutusque/TinyMistral-248M-v2.5
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experts:
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- source_model: Locutusque/TinyMistral-248M-v2
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positive_prompts:
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- "An emerging trend in global economics is"
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- "TITLE: The Next Generation of Internet Connectivity"
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- "begin a comprehensive analysis on the sociopolitical effects of"
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negative_prompts:
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- "Code a simple"
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- "Explain the Krebs cycle in detail"
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- "Compose a sonnet about"
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- source_model: Locutusque/TinyMistral-248M-v2.5
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positive_prompts:
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- "Advanced C++ memory management techniques"
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- "C# asynchronous programming best practices"
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- "AI's role in predictive analytics"
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- "textbook review on machine learning algorithms"
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- "## Exercise: Design a C# interface for a CRM system"
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- "## Solution: Optimize an AI-powered recommendation engine"
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negative_prompts:
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- "Narrate the story of"
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- "The ethical considerations in"
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- "Review the latest art exhibition by"
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- source_model: Locutusque/TinyMistral-248M-v2.5-Instruct
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positive_prompts:
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- "What is the chemical formula for photosynthesis?"
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- "Identification of a new mineral found on Mars"
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- "physics: Explaining the concept of relativity"
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- "Solve for x using differential equations:"
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- "history: Analyze the causes of the French Revolution"
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negative_prompts:
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- "Devise a business plan for"
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- "The evolution of culinary arts"
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- "Orchestrate a piece for a string quartet"
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- source_model: jtatman/tinymistral-v2-pycoder-instruct-248m
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positive_prompts:
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- "Write a Python program for facial recognition"
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- "Explain dynamic typing in programming languages"
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- "algorithm development for efficient data sorting"
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negative_prompts:
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- "Who was the first Emperor of Rome?"
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- "Discuss the political dynamics in"
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- "Provide a proof for Fermat's Last Theorem"
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- "physics: The principles of thermodynamics"
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- source_model: Felladrin/TinyMistral-248M-SFT-v4
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positive_prompts:
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- "Escreba sobre a influência da música no Brasil"
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- "Voici un guide pour les voyageurs en France"
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- "Para entender la política de México, se debe considerar"
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- "Cuales son los efectos de la globalización en Argentina"
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- "Welche gesellschaftlichen Veränderungen gibt es in Deutschland"
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- "If you had to imagine a utopian city, what would be its core values?"
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negative_prompts:
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- "Calculate the integral of"
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- "Describe the process of cell division"
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- "Review the latest advancements in quantum computing"
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- source_model: Locutusque/TinyMistral-248M-v2-Instruct
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positive_prompts:
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- "Write an essay on the evolution of international trade laws"
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- "What are the key components of a sustainable urban ecosystem?"
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- "instruct on effective negotiation techniques in diplomacy"
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- "How does cognitive bias affect decision making in high-pressure environments?"
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- "Identify the architectural significance of the Sydney Opera House"
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negative_prompts:
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- "Develop a script to automate"
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- "Understanding inheritance in object-oriented programming"
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- "philosophy of existentialism in contemporary society"
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```
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## 💻 Usage
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```python
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!pip install -qU transformers bitsandbytes accelerate
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from transformers import AutoTokenizer
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import transformers
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import torch
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model = "M4-ai/TinyMistral-6x248M"
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tokenizer = AutoTokenizer.from_pretrained(model)
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pipeline = transformers.pipeline(
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"text-generation",
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model=model,
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model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
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)
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messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
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prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
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print(outputs[0]["generated_text"])
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```
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