Downloads · 30 days
46
1% of all-time downloads
M4-ai/Orca-2.0-Tau-1.8B
Orca-2.0-Tau-1.8B is a text generation model from M4-ai. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as other.
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.
Downloads · 30 days
46
1% of all-time downloads
All-time downloads
6.2K
Public
Parameters
1.8B
7.3 GB on disk
Likes
10
Public
Click a slice to open those files.
.safetensors3.7 GB · 100%
From the Hugging Face model README
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.
This model has capabilities in math, coding, writing, and more. We fine-tuned it using a high quality mix for general-purpose assistants.
General purpose assistant, question answering, chain-of-thought, etc..
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
Coming soon
| 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 |
We used 8 Kaggle TPUs, and we trained at a global batch size of 128 and sequence length of 2048.
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 |