metadata

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

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

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

Model Details

Model Description

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

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

Recommendations

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.