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--- |
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language: |
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- en |
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license: other |
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library_name: transformers |
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datasets: |
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- Open-Orca/SlimOrca |
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- m-a-p/Code-Feedback |
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- MaziyarPanahi/WizardLM_evol_instruct_V2_196k |
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- camel-ai/math |
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- camel-ai/physics |
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- camel-ai/biology |
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- camel-ai/chemistry |
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- LDJnr/Capybara |
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- jondurbin/airoboros-3.2 |
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- microsoft/orca-math-word-problems-200k |
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inference: |
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parameters: |
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do_sample: true |
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temperature: 0.8 |
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top_p: 0.95 |
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top_k: 40 |
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max_new_tokens: 250 |
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repetition_penalty: 1.1 |
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--- |
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# neural-chat-mini-v2.2-1.8B |
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<!-- Provide a quick summary of what the model is/does. --> |
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We fine-tuned tau-1.8B using SFT and DPOP on a high quality mix for general-purpose assistants. |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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This model has capabilities in math, coding, writing, and more. We fine-tuned it using a high quality mix for general-purpose assistants. |
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- **Developed by:** M4-ai |
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- **Language(s) (NLP):** English and maybe Chinese |
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- **License:** tongyi-qianwen license |
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- **Finetuned from model:** [tau-1.8B](https://huggingface.co/M4-ai/tau-1.8B) |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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General purpose assistant, question answering, chain-of-thought, etc.. |
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### Recommendations |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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## Training Details |
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### Training Data |
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- Open-Orca/SlimOrca |
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- m-a-p/Code-Feedback |
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- MaziyarPanahi/WizardLM_evol_instruct_V2_196k |
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- camel-ai/math |
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- camel-ai/physics |
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- camel-ai/biology |
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- camel-ai/chemistry |
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- LDJnr/Capybara |
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- jondurbin/airoboros-3.2 |
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- microsoft/orca-math-word-problems-200k |
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- mlabonne/orpo-dpo-mix-40k |
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## Evaluations |
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coming soon |
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#### Training Hyperparameters |
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- **Training regime:** bf16 non-mixed precision |
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## Technical Specifications |
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#### Hardware |
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We used 8 Kaggle TPUs, and we trained at a global batch size of 128 and sequence length of 2048. |
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