metadata
library_name: transformers
license: other
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
language:
- en
inference:
parameters:
do_sample: true
temperature: 0.8
top_p: 0.95
top_k: 40
max_new_tokens: 250
repetition_penalty: 1.1
Hercules-Mini-1.8B
We fine-tuned tau-1.8B on a high quality mix for general-purpose assistants. A DPO version of this will be released soon. We use the ChatML prompt format.
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.
Evaluation
Coming soon
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
Evaluations
| Tasks | Version | Filter | n-shot | Metric | Value | Stderr | |
|---|---|---|---|---|---|---|---|
| agieval_nous | N/A | none | 0 | acc | 0.2537 | ± | 0.0086 |
| none | 0 | acc_norm | 0.2474 | ± | 0.0085 | ||
| - agieval_aqua_rat | 1 | none | 0 | acc | 0.2283 | ± | 0.0264 |
| none | 0 | acc_norm | 0.2441 | ± | 0.0270 | ||
| - agieval_logiqa_en | 1 | none | 0 | acc | 0.2750 | ± | 0.0175 |
| none | 0 | acc_norm | 0.3164 | ± | 0.0182 | ||
| - agieval_lsat_ar | 1 | none | 0 | acc | 0.2087 | ± | 0.0269 |
| none | 0 | acc_norm | 0.1739 | ± | 0.0250 | ||
| - agieval_lsat_lr | 1 | none | 0 | acc | 0.1843 | ± | 0.0172 |
| none | 0 | acc_norm | 0.2353 | ± | 0.0188 | ||
| - agieval_lsat_rc | 1 | none | 0 | acc | 0.2602 | ± | 0.0268 |
| none | 0 | acc_norm | 0.1784 | ± | 0.0234 | ||
| - agieval_sat_en | 1 | none | 0 | acc | 0.3544 | ± | 0.0334 |
| none | 0 | acc_norm | 0.2961 | ± | 0.0319 | ||
| - agieval_sat_en_without_passage | 1 | none | 0 | acc | 0.3107 | ± | 0.0323 |
| none | 0 | acc_norm | 0.2282 | ± | 0.0293 | ||
| - agieval_sat_math | 1 | none | 0 | acc | 0.2727 | ± | 0.0301 |
| none | 0 | acc_norm | 0.2091 | ± | 0.0275 | ||
| truthfulqa_mc2 | 2 | none | 0 | acc | 0.3923 | ± | 0.0139 |
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.