--- 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"]) ```