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gyung/
lfm25-agentic-sft-lora-20260619-lfm25-agentic-sft-from-jsonrl-v1

Name: lfm25-agentic-sft-lora-20260619-lfm25-agentic-sft-from-jsonrl-v1
Availability: InStock

LiquidAI/LFM2.5-8B-A1B

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LFM2.5-8B-A1B__agentic_sft_lora_20260619_lfm25_agentic_sft_from_jsonrl_v1

This public artifact is part of the ko-law-retriever work. It is intended for Korean legal retrieval, evidence selection, or related tool-use experiments.

Scope

Artifact type: PEFT LoRA adapter
This is not a standalone legal-advice model.
Use it to retrieve, rank, or prepare evidence for a separate answer model.

Basic Use

Install:

pip install -U "huggingface_hub[cli]" peft transformers

Download:

hf download gyung/lfm25-agentic-sft-lora-20260619-lfm25-agentic-sft-from-jsonrl-v1 --local-dir lfm25-agentic-sft-lora-20260619-lfm25-agentic-sft-from-jsonrl-v1

For LoRA adapters, load with the matching base model:

python

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_id = "LiquidAI/LFM2.5-8B-A1B"
adapter_id = "gyung/lfm25-agentic-sft-lora-20260619-lfm25-agentic-sft-from-jsonrl-v1"

tokenizer = AutoTokenizer.from_pretrained(base_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    base_id,
    device_map="auto",
    torch_dtype="auto",
    trust_remote_code=True,
)
model = PeftModel.from_pretrained(model, adapter_id)

For vLLM LoRA serving:

bash

python -m vllm.entrypoints.openai.api_server \
  --model LiquidAI/LFM2.5-8B-A1B \
  --enable-lora \
  --lora-modules lfm25-agentic-sft-lora-20260619-lfm25-agentic-sft-from-jsonrl-v1=./lfm25-agentic-sft-lora-20260619-lfm25-agentic-sft-from-jsonrl-v1 \
  --served-model-name lfm25-agentic-sft-lora-20260619-lfm25-agentic-sft-from-jsonrl-v1 \
  --max-model-len 12288

Related Repositories

Main code: gyung/ko-law-retriever
Large data/eval artifacts: gyung/ko-law-retriever-artifacts-20260622