Llama 3.1 8B Experimental 1206
Overall Strengths
- Logical and Boolean Reasoning – Excels in tasks requiring clear, rule-based logic and manipulation of true/false statements.
- Focused Domain Knowledge – Strong at certain specialized tasks (sports rules, ruin names, hyperbaton) that blend world knowledge with language comprehension.
- Good Instruction Compliance – High prompt-level and instance-level accuracy (both strict and loose) indicate that it follows user instructions effectively, even in more complex or nuanced prompts.
- Reasonable Multi-step Reasoning – While not the best in every logic category, it still shows solid performance (60%+) on tasks like disambiguation and causal reasoning.
- Extended Context Window (138k) – The large 138k token context allows the model to handle lengthy inputs and maintain coherence across extensive passages or multi-turn conversations. This is especially valuable for tasks like long-document question answering, summarization, or complex scenario analysis where context retention is crucial.
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 25.67 |
| IFEval (0-Shot) | 69.67 |
| BBH (3-Shot) | 30.06 |
| MATH Lvl 5 (4-Shot) | 11.10 |
| GPQA (0-shot) | 6.60 |
| MuSR (0-shot) | 8.50 |
| MMLU-PRO (5-shot) | 28.10 |
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Model tree for sethuiyer/Llama-3.1-8B-Experimental-1206-Instruct
Base model
unsloth/Meta-Llama-3.1-8BEvaluation results
- strict accuracy on IFEval (0-Shot)Open LLM Leaderboard69.670
- normalized accuracy on BBH (3-Shot)Open LLM Leaderboard30.060
- exact match on MATH Lvl 5 (4-Shot)Open LLM Leaderboard11.100
- acc_norm on GPQA (0-shot)Open LLM Leaderboard6.600
- acc_norm on MuSR (0-shot)Open LLM Leaderboard8.500
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard28.100