---
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.