Quantization made by Richard Erkhov.
neural-chat-mini-v2.2-1.8B - bnb 8bits
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
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.
