⚠️ Consider this model a creative text generator.
Without additional finetuning, it gives wildly inaccurate answers. Don't trust the output of this model without additional verification.
Supervised Fine-Tuning (SFT) on
crash-course
for 1 epoch.
Direct Preference Optimization (DPO) on
orca_dpo_pairs
.
Intended Uses
For research, experimentation, and educational purposes where a small instruction-following model is desired.
Limitations
Hallucinations:
Prone to generating incorrect information due to its small size.
Repetitive Output:
May produce repetitive text.
Training Details
Both SFT and DPO share common settings: liger_kernel booster, LoRA fine-tuning, custom model, BF16 compute type, batch size of 2, and a cosine scheduler with a learning rate of 5e-5. RSLoRA is enabled with a rank of 16 and alpha of 32.
The main differences are in the dataset and training specifics. SFT uses CrashCourse_120K with packing enabled and LoRA dropout of 0, while DPO uses orca_pairs with packing disabled and a LoRA dropout of 0.95.
Evaluation
Provides coherent and creative answers but may often be incorrect. Thorough evaluation is recommended before deployment.
Runs of agentlans SmolLM2-135M-Instruct-Plus on huggingface.co
15
Total runs
0
24-hour runs
0
3-day runs
2
7-day runs
5
30-day runs
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