Model Card: agentlans/Llama-3.2-1B-Instruct-CrashCourse12K
Model Overview
- Base Model: Llama-3.2-1B-Instruct
- Fine-tuning Type: Supervised Fine-Tuning (SFT)
- Dataset: agentlans/crash-course (12,000 rows)
- Purpose: Enhanced instruction-following capabilities
Training Details
- Method: Supervised fine-tuning on high-quality instruction dataset
- Training Rows: 12,000
- Objective: Improve task completion and instruction understanding
Performance
- Optimized for multi-task instruction following
- Improved zero-shot and few-shot performance
- Enhanced reasoning and response coherence
Limitations
- 1B parameter model with constrained complex reasoning
- Knowledge cutoff: December 2023
- Potential inherited biases from base model and training data
Recommended Use
- General instruction-based tasks
- Educational content generation
- Simple reasoning and task completion
Open LLM Leaderboard Evaluation Results
Detailed results can be found here! Summarized results can be found here!
| Metric | Value (%) |
|---|---|
| Average | 13.35 |
| IFEval (0-Shot) | 53.95 |
| BBH (3-Shot) | 9.39 |
| MATH Lvl 5 (4-Shot) | 6.57 |
| GPQA (0-shot) | 0.00 |
| MuSR (0-shot) | 1.20 |
| MMLU-PRO (5-shot) | 8.99 |
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Evaluation results
- averaged accuracy on IFEval (0-Shot)Open LLM Leaderboard53.950
- normalized accuracy on BBH (3-Shot)test set Open LLM Leaderboard9.390
- exact match on MATH Lvl 5 (4-Shot)test set Open LLM Leaderboard6.570
- acc_norm on GPQA (0-shot)Open LLM Leaderboard0.000
- acc_norm on MuSR (0-shot)Open LLM Leaderboard1.200
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard8.990