LLM-OS-Models/LFM2.5-8B-A1B-KO-Agentic-SFT

LFM2.5-8B-A1B-KO-Agentic-SFT

Agentic/Fable diagnostic SFT follow-up for LLM-OS-Models/LFM2.5-8B-A1B-KO-SFT.

• —SFT model repo: <https://huggingface.co/LLM-OS-Models/LFM2.5-8B-A1B-KO-SFT>
• —CPT model repo: <https://huggingface.co/LLM-OS-Models/LFM2.5-8B-A1B-KO-CPT-FULL>
• —GitHub SFT repo: <https://github.com/gyunggyung/LFM25-KO-SFT>
• —GitHub CPT repo: <https://github.com/gyunggyung/LFM25-KO-CPT>
• —Base model: <https://huggingface.co/LiquidAI/LFM2.5-8B-A1B>
• —Liquid chat template docs: <https://docs.liquid.ai/lfm/key-concepts/chat-template>
• —Liquid tool-use docs: <https://docs.liquid.ai/lfm/key-concepts/tool-use>

Status

This model is uploaded for reproducibility and diagnostic analysis. It should not be treated as a public benchmark improvement over KO-CPT. It adds a small Agentic/Fable behavior stage after the failed Stage2 KO-SFT line.

Verdict

The Stage3 Agentic/Fable run is not a successful public benchmark repair. It shows small partial recovery on a few diagnostic slices, but it does not restore the broad benchmark quality that KO-CPT had before SFT.

Interpretation:

• —The model slightly recovers some law/economics/global-mmlu diagnostic slices.
• —It does not improve IFEval.
• —KMMLU direct hard remains weak.
• —GSM8K recovers only from the failed Stage2 level and remains below KO-CPT and the original base reference.

Data

Agentic/Fable data sources:

Public dataset releases:

• —Raw Agentic/Fable data: <https://huggingface.co/datasets/LLM-OS-Models/LFM2.5-KO-Agentic-Fable-Grounded-LFMChat-Raw>
• —Tokenized Agentic/Fable 8k arrays: <https://huggingface.co/datasets/LLM-OS-Models/LFM2.5-KO-Agentic-Fable-Grounded-LFMChat-8K>

Why It Did Not Fix Benchmarks

This stage is only 7.12M tokens and is focused on behavior traces: reading logs, following repository docs, planning terminal commands, and explaining evidence. Those examples are not direct repair data for Korean multiple-choice likelihood, exact-answer extraction, or option-only output.

The previous Stage2 KO-SFT checkpoint had already moved away from the KO-CPT benchmark distribution. Training a small agentic trace set on top of that did not restore the lost answer-token scoring behavior.

Recommended Use

Use this checkpoint only for inspecting the Agentic/Fable behavior experiment. For public Korean benchmark quality, prefer:

<https://huggingface.co/LLM-OS-Models/LFM2.5-8B-A1B-KO-CPT-FULL>

If future work resumes, start from KO-CPT and run a small MCQA/answer-format repair SFT with strict gates instead of continuing this checkpoint.

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "LLM-OS-Models/LFM2.5-8B-A1B-KO-Agentic-SFT"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto",
)

messages = [
    {"role": "system", "content": "You are a careful terminal and log analysis assistant."},
    {"role": "user", "content": "다음 학습 로그에서 loss가 갑자기 튀는 원인을 어떻게 확인할지 단계별로 말해줘."},
]
inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt",
).to(model.device)
outputs = model.generate(inputs, max_new_tokens=512, do_sample=False)
print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))

Korean Summary

이 모델은 Stage2 KO-SFT 위에 Fable/문서/로그 기반 agentic SFT를 소량 얹은 진단용 모델입니다. 공개 벤치 개선 모델이 아닙니다. 일부 항목은 Stage2보다 조금 회복했지만, KO-CPT가 가진 공개 벤치 성능을 되찾지 못했습니다.