M4-ai_-_neural-chat-mini-v2.2-1.8B-4bits

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neural-chat-mini-v2.2-1.8B - bnb 4bits

• Model creator: https://huggingface.co/M4-ai/
• Original model: https://huggingface.co/M4-ai/neural-chat-mini-v2.2-1.8B/

Original model description:

language:

• en license: other library_name: transformers datasets:
• Open-Orca/SlimOrca
• m-a-p/Code-Feedback
• MaziyarPanahi/WizardLM_evol_instruct_V2_196k
• camel-ai/math
• camel-ai/physics
• camel-ai/biology
• camel-ai/chemistry
• LDJnr/Capybara
• jondurbin/airoboros-3.2
• microsoft/orca-math-word-problems-200k inference: parameters: do_sample: true temperature: 0.8 top_p: 0.95 top_k: 40 max_new_tokens: 250 repetition_penalty: 1.1

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neural-chat-mini-v2.2-1.8B

<!-- Provide a quick summary of what the model is/does. -->

We fine-tuned tau-1.8B using SFT and DPOP on a high quality mix for general-purpose assistants.

Model Details

Model Description

<!-- Provide a longer summary of what this model is. -->

This model has capabilities in math, coding, writing, and more. We fine-tuned it using a high quality mix for general-purpose assistants.

• Developed by: M4-ai
• Language(s) (NLP): English and maybe Chinese
• License: tongyi-qianwen license
• Finetuned from model: tau-1.8B

Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

General purpose assistant, question answering, chain-of-thought, etc..

Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

Training Details

Training Data

• Open-Orca/SlimOrca
• m-a-p/Code-Feedback
• MaziyarPanahi/WizardLM_evol_instruct_V2_196k
• camel-ai/math
• camel-ai/physics
• camel-ai/biology
• camel-ai/chemistry
• LDJnr/Capybara
• jondurbin/airoboros-3.2
• microsoft/orca-math-word-problems-200k
• mlabonne/orpo-dpo-mix-40k

Evaluations

coming soon

Training Hyperparameters

• Training regime: bf16 non-mixed precision

Technical Specifications

Hardware

We used 8 Kaggle TPUs, and we trained at a global batch size of 128 and sequence length of 2048.