license, base_model, library_name, pipeline_tag, tags

license	base_model	library_name	pipeline_tag	tags
mit	WeiboAI/VibeThinker-3B	gguf	text-generation	gguf llama.cpp quantized unsloth lora trl sft

VibeThinker-3B-SFT-Fable5-Glint-GGUF

GGUF quantizations of a LoRA fine-tune of WeiboAI/VibeThinker-3B, supervised fine-tuned on ermiaazarkhalili/Fable-5-Glint-Clean (private).

Quantized from ermiaazarkhalili/VibeThinker-3B-SFT-Fable5-Glint. See that repository for the full-precision weights.

Base model	WeiboAI/VibeThinker-3B
Training data	ermiaazarkhalili/Fable-5-Glint-Clean (private)
Method	LoRA supervised fine-tuning via Unsloth + TRL
License	mit (inherited from the base model)

Available quantizations

File	Size
vibethinker-3b-sft-fable5-glint.q4_k_m.gguf	1.93 GB
vibethinker-3b-sft-fable5-glint.q5_k_m.gguf	2.22 GB
vibethinker-3b-sft-fable5-glint.q8_0.gguf	3.29 GB

Usage

llama.cpp

huggingface-cli download ermiaazarkhalili/VibeThinker-3B-SFT-Fable5-Glint-GGUF vibethinker-3b-sft-fable5-glint.q4_k_m.gguf --local-dir .
llama-cli -m vibethinker-3b-sft-fable5-glint.q4_k_m.gguf -p "Explain gradient checkpointing in two sentences." -n 256

Ollama

echo 'FROM ./vibethinker-3b-sft-fable5-glint.q4_k_m.gguf' > Modelfile
ollama create vibethinker-3b-sft-fable5-glint-gguf -f Modelfile
ollama run vibethinker-3b-sft-fable5-glint-gguf

Training configuration

Setting	Value
LoRA rank (r)	16
LoRA alpha	16
Learning rate	0.0002
Epochs	3
Effective batch size	8 (2 x 4 grad accum)
Max sequence length	4096
Base precision	4-bit (QLoRA)
Target modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj

Observed training loss

Measured from our SLURM logs for this configuration. These are training-loss observations only — no downstream benchmark evaluation has been run on this model, so they should not be read as a quality claim.

SLURM job	Steps	First loss	Final loss
45987994	1,554	3.3731	1.1936
46021015	1,554	3.3731	1.1904

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

Trained by notebooks/fable_distillation_vibethinker-3b_fable-glint_unsloth.ipynb, executed non-interactively with papermill on a SLURM H100 partition (Unsloth + TRL, LoRA).

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