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---
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
<!-- Provide a quick summary of what the model is/does. -->
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
<!-- 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](https://huggingface.co/M4-ai/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.
## 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. |