--- language: - en license: apache-2.0 datasets: - databricks/databricks-dolly-15k - Felladrin/ChatML-databricks-dolly-15k - euclaise/reddit-instruct-curated - Felladrin/ChatML-reddit-instruct-curated - THUDM/webglm-qa - Felladrin/ChatML-WebGLM-QA - starfishmedical/webGPT_x_dolly - Felladrin/ChatML-webGPT_x_dolly - LDJnr/Capybara - Felladrin/ChatML-Capybara - Open-Orca/SlimOrca-Dedup - Felladrin/ChatML-SlimOrca-Dedup - HuggingFaceH4/ultrachat_200k - Felladrin/ChatML-ultrachat_200k - nvidia/HelpSteer - Felladrin/ChatML-HelpSteer - sablo/oasst2_curated - Felladrin/ChatML-oasst2_curated - CohereForAI/aya_dataset - Felladrin/ChatML-aya_dataset - argilla/distilabel-capybara-dpo-7k-binarized - Felladrin/ChatML-distilabel-capybara-dpo-7k-binarized - argilla/distilabel-intel-orca-dpo-pairs - Felladrin/ChatML-distilabel-intel-orca-dpo-pairs - argilla/ultrafeedback-binarized-preferences - Felladrin/ChatML-ultrafeedback-binarized-preferences - sablo/oasst2_dpo_pairs_en - Felladrin/ChatML-oasst2_dpo_pairs_en - NeuralNovel/Neural-DPO - Felladrin/ChatML-Neural-DPO base_model: Felladrin/Minueza-32M-Base pipeline_tag: text-generation widget: - messages: - role: system content: You are a career counselor. The user will provide you with an individual looking for guidance in their professional life, and your task is to assist them in determining what careers they are most suited for based on their skills, interests, and experience. You should also conduct research into the various options available, explain the job market trends in different industries, and advice on which qualifications would be beneficial for pursuing particular fields. - role: user content: Heya! - role: assistant content: Hi! How may I help you? - role: user content: I am interested in developing a career in software engineering. What would you recommend me to do? - messages: - role: system content: You are a highly knowledgeable assistant. Help the user as much as you can. - role: user content: How can I become a healthier person? - messages: - role: system content: You are a helpful assistant who gives creative responses. - role: user content: Write the specs of a game about mages in a fantasy world. - messages: - role: system content: You are a helpful assistant who answers user's questions with details. - role: user content: Tell me about the pros and cons of social media. - messages: - role: system content: You are a helpful assistant who answers user's questions with details and curiosity. - role: user content: What are some potential applications for quantum computing? inference: parameters: max_new_tokens: 250 do_sample: true temperature: 0.65 top_p: 0.55 top_k: 35 repetition_penalty: 1.176 model-index: - name: Minueza-32M-Chat results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 20.39 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Minueza-32M-Chat name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 26.54 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Minueza-32M-Chat name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 25.75 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Minueza-32M-Chat name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 47.27 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Minueza-32M-Chat name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 50.99 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Minueza-32M-Chat name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 0.0 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Minueza-32M-Chat name: Open LLM Leaderboard --- # Minueza-32M-Chat: A chat model with 32 million parameters - Base model: [Felladrin/Minueza-32M-Base](https://huggingface.co/Felladrin/Minueza-32M-Base) - Datasets used during SFT: - [[ChatML](https://huggingface.co/datasets/Felladrin/ChatML-databricks-dolly-15k)] [databricks/databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) - [[ChatML](https://huggingface.co/datasets/Felladrin/ChatML-reddit-instruct-curated)] [euclaise/reddit-instruct-curated](https://huggingface.co/datasets/euclaise/reddit-instruct-curated) - [[ChatML](https://huggingface.co/datasets/Felladrin/ChatML-WebGLM-QA)] [THUDM/webglm-qa](https://huggingface.co/datasets/THUDM/webglm-qa) - [[ChatML](https://huggingface.co/datasets/Felladrin/ChatML-webGPT_x_dolly)] [starfishmedical/webGPT_x_dolly](https://huggingface.co/datasets/starfishmedical/webGPT_x_dolly) - [[ChatML](https://huggingface.co/datasets/Felladrin/ChatML-Capybara)] [LDJnr/Capybara](https://huggingface.co/datasets/LDJnr/Capybara) - [[ChatML](https://huggingface.co/datasets/Felladrin/ChatML-SlimOrca-Dedup)] [Open-Orca/SlimOrca-Dedup](https://huggingface.co/datasets/Open-Orca/SlimOrca-Dedup) - [[ChatML](https://huggingface.co/datasets/Felladrin/ChatML-ultrachat_200k)] [HuggingFaceH4/ultrachat_200k](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k) - [[ChatML](https://huggingface.co/datasets/Felladrin/ChatML-HelpSteer)] [nvidia/HelpSteer](https://huggingface.co/datasets/nvidia/HelpSteer) - [[ChatML](https://huggingface.co/datasets/Felladrin/ChatML-oasst2_curated)] [sablo/oasst2_curated](https://huggingface.co/datasets/sablo/oasst2_curated) - [[ChatML](https://huggingface.co/datasets/Felladrin/ChatML-aya_dataset)] [CohereForAI/aya_dataset](https://huggingface.co/datasets/CohereForAI/aya_dataset) - Datasets used during DPO: - [[ChatML](https://huggingface.co/datasets/Felladrin/ChatML-distilabel-capybara-dpo-7k-binarized)] [argilla/distilabel-capybara-dpo-7k-binarized](https://huggingface.co/datasets/argilla/distilabel-capybara-dpo-7k-binarized) - [[ChatML](https://huggingface.co/datasets/Felladrin/ChatML-distilabel-intel-orca-dpo-pairs)] [argilla/distilabel-intel-orca-dpo-pairs](https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs) - [[ChatML](https://huggingface.co/datasets/Felladrin/ChatML-ultrafeedback-binarized-preferences)] [argilla/ultrafeedback-binarized-preferences](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences) - [[ChatML](https://huggingface.co/datasets/Felladrin/ChatML-oasst2_dpo_pairs_en)] [sablo/oasst2_dpo_pairs_en](https://huggingface.co/datasets/sablo/oasst2_dpo_pairs_en) - [[ChatML](https://huggingface.co/datasets/Felladrin/ChatML-Neural-DPO)] [NeuralNovel/Neural-DPO](https://huggingface.co/datasets/NeuralNovel/Neural-DPO) - License: [Apache License 2.0](https://huggingface.co/Felladrin/Minueza-32M-Chat/resolve/main/license.txt) - Availability in other ML formats: - GGUF: [Felladrin/gguf-Minueza-32M-Chat](https://huggingface.co/Felladrin/gguf-Minueza-32M-Chat) - ONNX: [Felladrin/onnx-Minueza-32M-Chat](https://huggingface.co/Felladrin/onnx-Minueza-32M-Chat) ## Recommended Prompt Format ``` <|im_start|>system {system_message}<|im_end|> <|im_start|>user {user_message}<|im_end|> <|im_start|>assistant ``` ## Recommended Inference Parameters ```yml do_sample: true temperature: 0.65 top_p: 0.55 top_k: 35 repetition_penalty: 1.176 ``` ## Usage Example ```python from transformers import pipeline generate = pipeline("text-generation", "Felladrin/Minueza-32M-Chat") messages = [ { "role": "system", "content": "You are a helpful assistant who answers the user's questions with details and curiosity.", }, { "role": "user", "content": "What are some potential applications for quantum computing?", }, ] prompt = generate.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) output = generate( prompt, max_new_tokens=256, do_sample=True, temperature=0.65, top_k=35, top_p=0.55, repetition_penalty=1.176, ) print(output[0]["generated_text"]) ``` ## How it was trained This model was trained with [SFT Trainer](https://huggingface.co/docs/trl/main/en/sft_trainer) and [DPO Trainer](https://huggingface.co/docs/trl/main/en/dpo_trainer), in several sessions, using the following settings: For Supervised Fine-Tuning: | Hyperparameter | Value | | :-------------------------- | :-------------------------------------------- | | learning_rate | 2e-5 | | total_train_batch_size | 24 | | max_seq_length | 2048 | | weight_decay | 0 | | warmup_ratio | 0.02 | For Direct Preference Optimization: | Hyperparameter | Value | | :-------------------------- | :-------------------------------------------- | | learning_rate | 7.5e-7 | | total_train_batch_size | 6 | | max_length | 2048 | | max_prompt_length | 1536 | | max_steps | 200 | | weight_decay | 0 | | warmup_ratio | 0.02 | | beta | 0.1 | ## [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Felladrin__Minueza-32M-Chat) | Metric |Value| |---------------------------------|----:| |Avg. |28.49| |AI2 Reasoning Challenge (25-Shot)|20.39| |HellaSwag (10-Shot) |26.54| |MMLU (5-Shot) |25.75| |TruthfulQA (0-shot) |47.27| |Winogrande (5-shot) |50.99| |GSM8k (5-shot) | 0.00|