88 lines
2.1 KiB
Markdown
88 lines
2.1 KiB
Markdown
---
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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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