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ermiaazarkhalili/LFM2.5-2.6B-SFT-Fable5-GGUF
LFM2.5-2.6B-SFT-Fable5-GGUF is a text generation model from ermiaazarkhalili. Use it when you need the model to write or continue text. It is set up for gguf. The card lists the license as other.
GGUF quantizations of a LoRA fine-tune of LiquidAI/LFM2.5-2.6B, supervised fine-tuned on ermiaazarkhalili/Fable-5-Complete-2M-Clean (private).
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From the Hugging Face model README
GGUF quantizations of a LoRA fine-tune of LiquidAI/LFM2.5-2.6B, supervised fine-tuned on ermiaazarkhalili/Fable-5-Complete-2M-Clean (private).
Quantized from ermiaazarkhalili/LFM2.5-2.6B-SFT-Fable5. See that repository for the full-precision weights.
| Base model | LiquidAI/LFM2.5-2.6B |
| Training data | ermiaazarkhalili/Fable-5-Complete-2M-Clean (private) |
| Method | LoRA supervised fine-tuning via Unsloth + TRL |
| License | other (inherited from the base model) |
| File | Size |
|---|---|
lfm2.5-2.6b-sft-fable5.q4_k_m.gguf | 1.67 GB |
lfm2.5-2.6b-sft-fable5.q5_k_m.gguf | 1.94 GB |
lfm2.5-2.6b-sft-fable5.q8_0.gguf | 2.87 GB |
huggingface-cli download ermiaazarkhalili/LFM2.5-2.6B-SFT-Fable5-GGUF lfm2.5-2.6b-sft-fable5.q4_k_m.gguf --local-dir .
llama-cli -m lfm2.5-2.6b-sft-fable5.q4_k_m.gguf -p "Explain gradient checkpointing in two sentences." -n 256
echo 'FROM ./lfm2.5-2.6b-sft-fable5.q4_k_m.gguf' > Modelfile
ollama create lfm2.5-2.6b-sft-fable5-gguf -f Modelfile
ollama run lfm2.5-2.6b-sft-fable5-gguf
| Setting | Value |
|---|---|
| LoRA rank (r) | 16 |
| LoRA alpha | 16 |
| Learning rate | 0.0002 |
| Epochs | 2 |
| Effective batch size | 8 (2 x 4 grad accum) |
| Max sequence length | 4096 |
| Base precision | 4-bit (QLoRA) |
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 |
|---|---|---|---|
| unlabelled | 94,256 | 1.1420 | 0.9263 |
Trained by notebooks/fable_distillation_lfm2.5-2.6b_fable_unsloth.ipynb, executed non-interactively with
papermill on a SLURM H100 partition (Unsloth + TRL, LoRA).
Card generated from the training run's own configuration and logs by
scripts/generate_hub_model_card.py.