SmolLM3-3B-GRPO-NuminaMath-10K-GGUF

SmolLM3-3B-GRPO-NuminaMath-10K-GGUF

GGUF quantizations of a LoRA fine-tune of HuggingFaceTB/SmolLM3-3B, trained with GRPO (reinforcement learning) on AI-MO/NuminaMath-CoT.

Quantized from ermiaazarkhalili/SmolLM3-3B-GRPO-NuminaMath-10K. See that repository for the full-precision weights.

Available quantizations

Usage

llama.cpp

huggingface-cli download ermiaazarkhalili/SmolLM3-3B-GRPO-NuminaMath-10K-GGUF smollm3-3b-grpo-numinamath-10k.q4_k_m.gguf --local-dir .
llama-cli -m smollm3-3b-grpo-numinamath-10k.q4_k_m.gguf -p "Explain gradient checkpointing in two sentences." -n 256

Ollama

echo 'FROM ./smollm3-3b-grpo-numinamath-10k.q4_k_m.gguf' > Modelfile
ollama create smollm3-3b-grpo-numinamath-10k-gguf -f Modelfile
ollama run smollm3-3b-grpo-numinamath-10k-gguf

Limitations

• No benchmark evaluation has been run on this checkpoint. The only reported numbers are training-loss observations.
• Inherits the biases, knowledge cutoff and failure modes of the base model.
• Fine-tuned on a single instruction-following dataset; behaviour outside that distribution is untested.
• LoRA adapters were merged into the base weights, so the merged model cannot be detached from this fine-tune.

Reproducing

Produced by an Unsloth + TRL LoRA training pipeline, executed non-interactively with papermill on a SLURM H100 partition.

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Card generated from the training run's own configuration and logs by scripts/generate_hub_model_card.py.