Instructions to use tugot17/lfm2.5-1.2b-instruct-smoltalk-lora-classic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use tugot17/lfm2.5-1.2b-instruct-smoltalk-lora-classic with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("LiquidAI/LFM2.5-1.2B-Instruct") model = PeftModel.from_pretrained(base_model, "tugot17/lfm2.5-1.2b-instruct-smoltalk-lora-classic") - Notebooks
- Google Colab
- Kaggle
LFM2.5-1.2B-Instruct SmolTalk LoRA โ SGLang-ready
LoRA adapter for LiquidAI/LFM2.5-1.2B-Instruct, derived from LiquidAI/LFM2.5-1.2B-Instruct-smoltalk-LoRA by dropping the ShortConv (conv.in_proj / conv.out_proj) LoRA weights, which SGLang does not have an injection point for yet. All other weights are unchanged (native w1/w3/w2 MLP naming is kept โ both PEFT and SGLang handle it).
See convert_lfm2_moe_lora_to_per_expert.py (bundled; the same script converts LFM2-MoE adapters to the per-expert format).
What's in this adapter
- Attention (every attention layer):
q_proj,k_proj,v_proj,out_proj - MLP (every layer):
w1,w3,w2(gate / up / down)
Dropped relative to the original (out of v1 scope): ShortConv conv.in_proj, conv.out_proj.
Usage with SGLang
Requires SGLang with the LFM2 LoRA wiring patch (tugot17/sglang#10). Once it lands upstream, stock SGLang works.
sglang serve \
--model-path LiquidAI/LFM2.5-1.2B-Instruct \
--port 30000 \
--enable-lora --max-lora-rank 8 \
--lora-paths "smoltalk=tugot17/lfm2.5-1.2b-instruct-smoltalk-lora-classic" \
--lora-target-modules q_proj k_proj v_proj out_proj gate_proj up_proj down_proj \
--lora-strict-loading
Generate with LoRA:
curl -sS http://localhost:30000/generate \
-H "Content-Type: application/json" \
-d '{
"text": "Write a short story set in an unusual location.",
"sampling_params": {"temperature": 0.0, "max_new_tokens": 80},
"lora_path": "smoltalk"
}'
Correctness
Verified against HF+PEFT by teacher-forced top-k logprob comparison (greedy, bf16), with the HF reference using the original adapter filtered to the same module set: SGLang's generation is token-identical to HF, and the logprob divergence matches the base model's no-LoRA noise floor.
Training (original adapter)
Per the parent adapter card: r=8, alpha=16, dropout=0.1, lr 5e-5, 1 epoch on a 5k-example slice of HuggingFaceTB/smoltalk. This artifact only removes the ShortConv weights; everything else carries over.
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Model tree for tugot17/lfm2.5-1.2b-instruct-smoltalk-lora-classic
Base model
LiquidAI/LFM2.5-1.2B-Base