Update model card with pending TB2-lite evaluation status

README.md

@@ -1,88 +1,167 @@
 ---
-license: apache-2.0
-base_model: LLM-OS-Models/KoHRM-Text-1.4B
+language:
+- en
+- ko
+library_name: transformers
+pipeline_tag: text-generation
 tags:
-- kohrm
-- hrm-text
-- lora
-- adapter
-- text-generation
+- terminal
+- sft
+- vllm
+- tb2-lite
+- evaluation-pending
+base_model: unknown
 ---
 
-# KoHRM-Text-1.4B-lora-comp-agent-reasoning-25m-v1
-
-This repository contains a KoHRM-Text repo-local LoRA adapter.
-
-It is **not** a PEFT-format adapter. The tensors are saved by
-`models/lora.py` in the KoHRM-Text codebase and are intended to be used with
-the matching KoHRM/HRM-Text architecture code.
-
-## Files
-
-- `lora_epoch_1.pt`: LoRA A/B tensors only.
-- `lora_epoch_1_info.json`: adapter metadata, training step, matched modules.
-- `lora_train_config.json`: exact training config.
-- `latest_lora.txt`: latest adapter tag.
-
-## Training
-
-- Base model: `LLM-OS-Models/KoHRM-Text-1.4B`
-- Resume checkpoint: `/home/work/.data/hrm_text_checkpoints/KoHRM-Text-1.4B-stage4d-korean-tool-finance-repeat3-gbs180`
-- Dataset: `kohrm_sft_comp_agent_reasoning_25m_v1`
-- Dataset tokens: `25002136`
-- Max sequence length: `4096`
-- Global batch size: `16384`
-- Epochs: `1`
-- Learning rate: `8e-05`
-- LoRA rank: `16`
-- LoRA alpha: `32.0`
-- LoRA dropout: `0.0`
-- Adapter tag: `epoch_1`
-- Training step: `1688`
-
-## Matched Modules
-
-- `model.H_level.core.layers.0.attn.gqkv_proj`
-- `model.H_level.core.layers.0.attn.o_proj`
-- `model.H_level.core.layers.0.mlp.gate_up_proj`
-- `model.H_level.core.layers.0.mlp.down_proj`
-- `model.H_level.core.layers.1.attn.gqkv_proj`
-- `model.H_level.core.layers.1.attn.o_proj`
-- `model.H_level.core.layers.1.mlp.gate_up_proj`
-- `model.H_level.core.layers.1.mlp.down_proj`
-- `model.H_level.core.layers.2.attn.gqkv_proj`
-- `model.H_level.core.layers.2.attn.o_proj`
-- `model.H_level.core.layers.2.mlp.gate_up_proj`
-- `model.H_level.core.layers.2.mlp.down_proj`
-- `model.H_level.core.layers.3.attn.gqkv_proj`
-- `model.H_level.core.layers.3.attn.o_proj`
-- `model.H_level.core.layers.3.mlp.gate_up_proj`
-- `model.H_level.core.layers.3.mlp.down_proj`
-- `model.H_level.core.layers.4.attn.gqkv_proj`
-- `model.H_level.core.layers.4.attn.o_proj`
-- `model.H_level.core.layers.4.mlp.gate_up_proj`
-- `model.H_level.core.layers.4.mlp.down_proj`
-- `model.H_level.core.layers.5.attn.gqkv_proj`
-- `model.H_level.core.layers.5.attn.o_proj`
-- `model.H_level.core.layers.5.mlp.gate_up_proj`
-- `model.H_level.core.layers.5.mlp.down_proj`
-- `model.H_level.core.layers.6.attn.gqkv_proj`
-- `model.H_level.core.layers.6.attn.o_proj`
-- `model.H_level.core.layers.6.mlp.gate_up_proj`
-- `model.H_level.core.layers.6.mlp.down_proj`
-- `model.H_level.core.layers.7.attn.gqkv_proj`
-- `model.H_level.core.layers.7.attn.o_proj`
-- `model.H_level.core.layers.7.mlp.gate_up_proj`
-- `model.H_level.core.layers.7.mlp.down_proj`
-- ... 97 more
-
-## Usage
-
-Use this adapter with the KoHRM-Text repository code that defines
-`models/lora.py`. Load the base KoHRM checkpoint, inject LoRA into the same
-target modules, and load `lora_epoch_1.pt`.
-
-The adapter should be evaluated together with the exact base checkpoint listed
-above. It does not include base model weights.
-
-HF repo: `https://huggingface.co/LLM-OS-Models/KoHRM-Text-1.4B-lora-comp-agent-reasoning-25m-v1`
+# LLM-OS-Models/KoHRM-Text-1.4B-lora-comp-agent-reasoning-25m-v1
+
+터미널 작업 자동화를 위한 Terminal SFT 모델입니다. 입력된 작업/이전 터미널 상태를 보고 다음에 실행할 명령을 JSON 형태로 생성하는 용도로 학습했습니다.
+
+## 모델 요약
+
+- Base model: `unknown`
+- Training setup: `Terminal SFT`
+- Model card snapshot: `2026-06-03 22:09:10 UTC`
+- Corrected TB2-lite evaluated results currently indexed: `60`
+- Corrected TB2-lite score: `pending / not matched in current result directory`
+
+## Quickstart
+
+설치와 로그인:
+
+```bash
+pip install -U vllm transformers huggingface_hub
+huggingface-cli login
+```
+
+관련 코드:
+
+- GitHub: https://github.com/LLM-OS-Models/Terminal
+- vLLM 평가 실행: `tb2_lite/scripts/replay_eval.py`
+- chat template/fallback 생성: `tb2_lite/scripts/prompt_builder.py`
+- JSON/command 채점: `tb2_lite/scripts/replay_metrics.py`
+
+vLLM 직접 실행 예시. 평가 코드와 동일하게 chat template을 우선 사용하고, template이 없으면 ChatML/Gemma fallback을 사용합니다.
+
+```python
+from transformers import AutoTokenizer
+from vllm import LLM, SamplingParams
+
+model_id = "LLM-OS-Models/KoHRM-Text-1.4B-lora-comp-agent-reasoning-25m-v1"
+tp = 1
+
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+llm = LLM(
+    model=model_id,
+    tokenizer=model_id,
+    trust_remote_code=True,
+    dtype="bfloat16",
+    tensor_parallel_size=tp,
+    max_model_len=49152,
+    gpu_memory_utilization=0.92,
+)
+
+messages = [
+    {"role": "system", "content": "You are a terminal automation assistant. Return JSON only."},
+    {"role": "user", "content": "Inspect the current directory and list Python files."},
+]
+
+def render_chatml(messages):
+    parts = []
+    for message in messages:
+        role = "assistant" if message["role"] == "assistant" else message["role"]
+        if role == "tool":
+            role = "user"
+        parts.append(f"<|im_start|>{role}\n{message['content']}<|im_end|>\n")
+    parts.append("<|im_start|>assistant\n")
+    return "".join(parts)
+
+def render_gemma4_turn(messages, empty_thought_channel=False):
+    parts = ["<bos>"]
+    for message in messages:
+        role = "model" if message["role"] == "assistant" else message["role"]
+        if role == "tool":
+            role = "user"
+        parts.append(f"<|turn>{role}\n{message['content'].strip()}<turn|>\n")
+    parts.append("<|turn>model\n")
+    if empty_thought_channel:
+        parts.append("<|channel>thought\n<channel|>")
+    return "".join(parts)
+
+def render_prompt(model_id, tokenizer, messages):
+    model_key = model_id.lower()
+    if "gemma-4" in model_key:
+        try:
+            return tokenizer.apply_chat_template(
+                messages,
+                tokenize=False,
+                add_generation_prompt=True,
+                enable_thinking=False,
+            )
+        except Exception:
+            return render_gemma4_turn(
+                messages,
+                empty_thought_channel=("26b" in model_key or "31b" in model_key),
+            )
+    try:
+        return tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    except Exception:
+        return render_chatml(messages)
+
+prompt = render_prompt(model_id, tokenizer, messages)
+sampling = SamplingParams(
+    temperature=0.0,
+    top_p=1.0,
+    max_tokens=1024,
+    repetition_penalty=1.0,
+)
+outputs = llm.generate([prompt], sampling_params=sampling)
+print(outputs[0].outputs[0].text)
+```
+
+권장 출력 형식:
+
+```json
+{
+  "analysis": "brief reasoning about the next terminal action",
+  "plan": "short execution plan",
+  "commands": [
+    {"keystrokes": "ls -la\n", "duration": 0.1}
+  ],
+  "task_complete": false
+}
+```
+
+평가와 동일한 replay 명령:
+
+```bash
+python tb2_lite/scripts/replay_eval.py \
+  --model LLM-OS-Models/KoHRM-Text-1.4B-lora-comp-agent-reasoning-25m-v1 \
+  --model-short LLM-OS-Models__KoHRM-Text-1.4B-lora-comp-agent-reasoning-25m-v1 \
+  --eval-path tb2_lite/data/replay_full.jsonl \
+  --output-dir /home/work/.data/tb2_lite_eval/corrected_readme_models_vllm \
+  --dtype bfloat16 \
+  --tp 1 \
+  --max-model-len 49152 \
+  --max-tokens 1024 \
+  --temperature 0.0 \
+  --top-p 1.0 \
+  --gpu-memory-utilization 0.92 \
+  --language-model-only
+```
+
+- 기본 권장 tensor parallel: `1`. OOM이면 `--tp`와 `tensor_parallel_size`를 2/4/8로 올리세요.
+- corrected TB2-lite 평가는 `temperature=0.0`, `top_p=1.0`, `max_tokens=1024`로 고정했습니다.
+- Gemma 4는 JSON 출력을 위해 `enable_thinking=False`를 사용하고, 26B/31B 계열은 평가 코드에서 empty thought channel 처리를 자동 적용합니다.
+
+## 평가 상태
+
+- Current corrected TB2-lite score: `pending`
+- Reason: 현재 `/home/work/.data/tb2_lite_eval/corrected_readme_models_vllm` 집계 결과와 이 HF repo명이 직접 매칭되지 않았습니다.
+- Next step: 동일한 `tb2_lite/scripts/replay_eval.py` 경로로 평가를 돌린 뒤 점수 카드로 자동 교체합니다.
+
+## 모델군 해석
+
+- 이 repo는 아직 현재 corrected TB2-lite 집계 JSON과 직접 매칭되는 점수가 없습니다.
+- TB2-lite 점수는 일반 지능 벤치마크가 아니라 터미널 next-action JSON 재현 능력을 측정합니다.
+- 생성 명령은 실제 실행 전에 sandbox, allowlist, human review 같은 안전장치를 거쳐야 합니다.