Update model card with corrected TB2-lite evaluation

README.md

@@ -20,9 +20,138 @@ base_model: ByteDance/Ouro-2.6B
 
 - Base model: `ByteDance/Ouro-2.6B`
 - Training setup: `Terminal SFT`
-- Evaluation snapshot: `2026-05-08 16:03:48 UTC`
+- Evaluation snapshot: `2026-05-08 16:08:25 UTC`
 - Evaluation result id: `ouro_2p6b_terminal_sft`
 
+## 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/Ouro-2.6B-terminal-sft"
+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/Ouro-2.6B-terminal-sft \
+  --model-short ouro_2p6b_terminal_sft \
+  --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 결과
 
 평가는 corrected TB2-lite replay set에서 vLLM으로 수행했습니다. 순위 점수는 `100 * avg_command_f1`만 사용하고, `first_cmd_exact_pct`는 보조 지표로만 봅니다.