Orca-2.0-Tau-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.
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 45.20 |
| AI2 Reasoning Challenge (25-Shot) | 37.12 |
| HellaSwag (10-Shot) | 61.13 |
| MMLU (5-Shot) | 45.27 |
| TruthfulQA (0-shot) | 39.10 |
| Winogrande (5-shot) | 59.59 |
| GSM8k (5-shot) | 28.96 |
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Datasets used to train M4-ai/Orca-2.0-Tau-1.8B
Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard37.120
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard61.130
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard45.270
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard39.100
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard59.590
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard28.960