Text Generation
Transformers
Safetensors
mixtral
Mixture of Experts
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
text-generation-inference
Inference Endpoints
| 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"]) | |
| ``` |