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Ex0bit / lfm-Nanotron
Model's Last Updated: January 25 2026
text-generation
Introduction of lfm-Nanotron
Model Details of lfm-Nanotron
# lfm-Nanotron: 2.6B-PRISM-SFT-GSPO-AutoRoundV2
** LFM Architeture model SFT + GSPO RL + PRISM **
Model Description
lfm-Nanotron : Limited Edition 2.6B PRISM Model Access. Unlock a cutting-edge Nano sized AI model!
This is lfm-Nanotron — A Nano Sized 2.6B parameter hybrid architecture language model fine-tuned with advanced techniques you won't find in mainstream releases:
- SFT (Test-Time Supervised-Fine-Tuning) — Adaptive optimization at inference
- GSPO (Group Sequence Policy Optimization) — RL Enhanced reasoning, Instruction following, thinking, tool calling & logic
- PRISM (Projected Refusal Isolation via Subspace Modification) — State-of-the-art over-refusal/propaganda removal from LLMs
- 128K Context Window — Handle massive prompts with ease
- Agentic Tool Calling — Built for multi-turn, thinking, and instruction-following tasks
Architecture Details
| Parameter | Value |
|---|---|
| Parameters | ~2.6B |
| Hidden Size | 2048 |
| Layers | 30 (22 Conv + 8 Full Attention) |
| Attention Heads | 32 |
| KV Heads | 8 (GQA) |
| Vocabulary | 65,536 |
| Max Context | 128,000 tokens |
| Architecture | Hybrid Conv + Attention (LFM2) |
Available Quantizations
| File | Quantization | Size | Use Case |
|---|---|---|---|
lfm2-nanotron-ttft-gspo-prism-bf16.gguf
|
BF16 | ~4.8GB | Full precision, best quality |
lfm2-nanotron-ttft-gspo-prism-Q4_K_M.gguf (+W4A16)
|
Q4_K_M | ~1.5GB | Balanced quality/size |
lfm2-nanotron-ttft-gspo-prism-Q2_K.gguf
|
Q2_K (+W2A16) | ~0.9GB | Maximum compression |
Usage
With llama.cpp
./llama-cli -m lfm2-nanotron-ttft-gspo-prism-Q4_K_M.gguf -p "Your prompt here" --temp 0.3 --min-p 0.15 --repeat-penalty 1.05
Recommended Generation Parameters
{
"temperature": 0.3,
"min_p": 0.15,
"repeat_penalty": 1.05
}
Citation
If you use this model in your research, please cite:
@misc{lfm2-nanotron-2026,
title={lfm2-Nanotron: Test-Time Fine-Tuned LFM2 with GSPO+PRISM},
author={Exobit (Eric Elbaz)},
year={2026},
publisher={Hugging Face},
url={https://huggingface.co/Ex0bit/lfm2-Nanotron}
}
License
This model is released under a custom research license. See LICENSE.md for details.
Acknowledgments
Runs of Ex0bit lfm-Nanotron on huggingface.co
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Url of lfm-Nanotron
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