Trained Models 🏋️
Collection
They may be small, but they're training like giants!
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A Llama Chat Model of 101M Parameters
- Base model: BEE-spoke-data/smol_llama-101M-GQA
- Datasets:
- Availability in other ML formats:
Recommended Prompt Format
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{user_message}<|im_end|>
<|im_start|>assistant
Recommended Inference Parameters
penalty_alpha: 0.5
top_k: 4
repetition_penalty: 1.105
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 28.73 |
| AI2 Reasoning Challenge (25-Shot) | 22.87 |
| HellaSwag (10-Shot) | 28.69 |
| MMLU (5-Shot) | 24.93 |
| TruthfulQA (0-shot) | 45.76 |
| Winogrande (5-shot) | 50.04 |
| GSM8k (5-shot) | 0.08 |
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Model tree for Felladrin/Smol-Llama-101M-Chat-v1
Base model
BEE-spoke-data/smol_llama-101M-GQADatasets used to train Felladrin/Smol-Llama-101M-Chat-v1
Space using Felladrin/Smol-Llama-101M-Chat-v1 1
Collection including Felladrin/Smol-Llama-101M-Chat-v1
Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard22.870
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard28.690
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard24.930
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard45.760
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard50.040
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard0.080