These are llama only weights of https://huggingface.co/liuhaotian/llava-v1.6-34b . The Clip encoder part is removed and and this model is llama weights only that can be loaded using LlamaForCausalLM. Which indirectly is a https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B licence.
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 70.73 |
| AI2 Reasoning Challenge (25-Shot) | 66.04 |
| HellaSwag (10-Shot) | 83.81 |
| MMLU (5-Shot) | 76.40 |
| TruthfulQA (0-shot) | 51.46 |
| Winogrande (5-shot) | 81.45 |
| GSM8k (5-shot) | 65.20 |
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 25.97 |
| IFEval (0-Shot) | 35.29 |
| BBH (3-Shot) | 43.62 |
| MATH Lvl 5 (4-Shot) | 3.93 |
| GPQA (0-shot) | 11.86 |
| MuSR (0-shot) | 20.24 |
| MMLU-PRO (5-shot) | 40.91 |
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Model tree for AbacusResearch/Jallabi-34B
Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard66.040
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard83.810
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard76.400
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard51.460
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard81.450
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard65.200
- strict accuracy on IFEval (0-Shot)Open LLM Leaderboard35.290
- normalized accuracy on BBH (3-Shot)Open LLM Leaderboard43.620
- exact match on MATH Lvl 5 (4-Shot)Open LLM Leaderboard3.930
- acc_norm on GPQA (0-shot)Open LLM Leaderboard11.860