feat: GRPO CLI 接入 + on-policy，OpenAI API top_k 参数化，补充训练参数表

- train.py 新增 --train_type=grpo 及参数 (--grpo_clip_eps, --grpo_kl_coef, --group_size, --grpo_sync_interval, --start_epoch)
- GRPOStrategy 统一 on-policy 模式，ratio = exp(logπ_θ - logπ_ref)，PPO 裁剪目标，sync_interval 自动同步 ref_model
- ChatCompletionRequest 新增 top_k 参数，不再硬编码
- 补充 README 完整训练参数表（含此前缺失的 max_grad_norm / adamw / window_size / stride 等）

.gitignore

@@ -15,6 +15,7 @@
 !/.gitattributes
 !/.dockerignore
 !/Dockerfile
+!/docker-compose.yml
 !/assets/**
 !/CONTRIBUTING.md
 !/LICENSE

README.md

@@ -47,6 +47,7 @@
 - 📦 **Lightweight**: Minimal dependencies, easy to deploy.
 - 🔬 **Research‑Friendly**: Modular design, easy to experiment with new ideas.
 - 🤗 **HuggingFace Integration**: Compatible with HuggingFace models and datasets.
+- 🔌 **Dual API Compatibility**: Supports both OpenAI and Anthropic chat completion APIs out of the box.
 
 ### Quick Start
 
@@ -67,44 +68,48 @@ pip install -e ".[dev]"
 #### Train a Model
 
 ```bash
-python scripts/tools/train.py \
+python scripts/tools/train.py --train_type=seq --data_root_path=/path/to/dataset --param_path=/path/to/model
-  --train_type=seq \
-  --data_root_path=/path/to/dataset \
-  --param_path=/path/to/model \
-  --n_epoch=3 \
-  --batch_size=4 \
-  --accumulation_steps=8 \
-  --max_lr=3e-4 \
-  --warmup_steps=2000 \
-  --ckpt_interval=5000 \
-  --ckpt_dir=./checkpoints
 ```
 
+| Parameter | Description | Default |
+|-----------|-------------|---------|
+| `--train_type` | Training type (`seq`, `sft`, `dpo`, `grpo`) | required |
+| `--data_root_path` | Dataset root directory | required |
+| `--param_path` | Model / checkpoint path | required |
+| `--n_epoch` | Training epochs | 1 |
+| `--batch_size` | Batch size | 1 |
+| `--accumulation_steps` | Gradient accumulation steps | 1 |
+| `--warmup_steps` | LR warmup steps | 1000 |
+| `--max_lr` | Peak learning rate (cosine decay) | 3e-4 |
+| `--max_grad_norm` | Max gradient norm for clipping | 1.0 |
+| `--adamw_beta1` | AdamW beta1 | 0.9 |
+| `--adamw_beta2` | AdamW beta2 | 0.95 |
+| `--adamw_weight_decay` | AdamW weight decay | 0.01 |
+| `--random_seed` | Random seed | 3407 |
+| `--num_workers` | DataLoader workers | 4 |
+| `--window_size` | Max input sequence length | auto |
+| `--stride` | Sequence stride | auto |
+| `--label_smoothing` | Label smoothing for cross entropy | 0.1 |
+| `--dpo_beta` | DPO beta | 0.1 |
+| `--grpo_clip_eps` | GRPO clip epsilon | 0.2 |
+| `--grpo_kl_coef` | GRPO KL penalty coefficient | 0.01 |
+| `--group_size` | GRPO group size | 4 |
+| `--grpo_sync_interval` | GRPO ref model sync interval (steps) | 200 |
+| `--ckpt_interval` | Checkpoint interval (iters) | 5000 |
+| `--ckpt_dir` | Checkpoint directory | checkpoint |
+| `--start_epoch` | Start epoch (for resume) | 0 |
+| `--start_batch` | Start batch (for resume) | 0 |
+| `--nprocs` | Number of GPUs | 1 |
+| `--device_type` | Device type | cuda |
+
+Full reference at [Parameter Guide](./assets/docs/params.md#training-parameters).
+
 #### Generate Text
 
 ```bash
 python scripts/tools/generate.py --param_path=/path/to/param_path
 ```
 
-#### Training Parameters
-
-| Parameter | Description | Default |
-|-----------|-------------|---------|
-| `--train_type` | Training type (`seq`, `sft`, `dpo`) | required |
-| `--data_root_path` | Dataset root directory | required |
-| `--param_path` | Model / checkpoint path | required |
-| `--n_epoch` | Training epochs | 1 |
-| `--batch_size` | Batch size | 1 |
-| `--accumulation_steps` | Gradient accumulation steps | 1 |
-| `--max_lr` | Peak learning rate (cosine decay) | 3e-4 |
-| `--warmup_steps` | LR warmup steps | 1000 |
-| `--ckpt_interval` | Checkpoint interval (iters) | 5000 |
-| `--ckpt_dir` | Checkpoint directory | checkpoint |
-| `--num_workers` | DataLoader workers | 4 |
-| `--nprocs` | Number of GPUs | 1 |
-
-Full reference at [Parameter Guide](./assets/docs/params.md#training-parameters).
-
 #### Docker
 
 Build and run with Docker (recommended for GPU environments):
@@ -125,13 +130,21 @@ docker run --gpus all -p 8000:8000 astrai:latest \
 
 # Run with volume mount for data
 docker run --gpus all -v /path/to/data:/data -it astrai:latest
+
+# Docker Compose (GPU, default)
+docker compose up -d
+
+# Docker Compose (CPU only)
+docker compose --profile cpu up -d
 ```
 
 > **Note**: `--gpus all` is required for CUDA support. Without it, `torch.cuda.is_available()` will return `False`.
 
+> **Note**: `--gpus all` is required for CUDA support. Without it, `torch.cuda.is_available()` will return `False`.
+
 #### Start HTTP Server
 
-Start the inference server with OpenAI-compatible HTTP API:
+Start the inference server with OpenAI and Anthropic-compatible HTTP API:
 
 ```bash
 python -m scripts.tools.server --port 8000 --device cuda
@@ -140,7 +153,7 @@ python -m scripts.tools.server --port 8000 --device cuda
 Make requests:
 
 ```bash
-# Chat API (OpenAI compatible)
+# OpenAI-compatible
 curl -X POST http://localhost:8000/v1/chat/completions \
   -H "Content-Type: application/json" \
   -d '{
@@ -148,7 +161,7 @@ curl -X POST http://localhost:8000/v1/chat/completions \
     "max_tokens": 512
   }'
 
-# Streaming response
+# OpenAI-compatible streaming
 curl -X POST http://localhost:8000/v1/chat/completions \
   -H "Content-Type: application/json" \
   -d '{
@@ -157,6 +170,27 @@ curl -X POST http://localhost:8000/v1/chat/completions \
     "max_tokens": 500
   }'
 
+# Anthropic-compatible
+curl -X POST http://localhost:8000/v1/messages \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "astrai",
+    "system": "You are a helpful assistant.",
+    "messages": [{"role": "user", "content": "Hello"}],
+    "max_tokens": 512
+  }'
+
+# Anthropic-compatible streaming with stop sequences
+curl -X POST http://localhost:8000/v1/messages \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "astrai",
+    "messages": [{"role": "user", "content": "Write a story"}],
+    "max_tokens": 500,
+    "stream": true,
+    "stop_sequences": ["The end"]
+  }'
+
 # Health check
 curl http://localhost:8000/health
 ```

assets/docs/README-zh-CN.md

@@ -53,6 +53,7 @@
 - 📦 **轻量**: 依赖少，部署简单。
 - 🔬 **研究友好**: 模块化设计，便于实验新想法。
 - 🤗 **HuggingFace 集成**: 兼容 HuggingFace 模型与数据集。
+- 🔌 **双 API 兼容**: 同时支持 OpenAI 和 Anthropic 聊天补全 API，开箱即用。
 
 ### 快速开始
 
@@ -73,44 +74,48 @@ pip install -e ".[dev]"
 #### 训练模型
 
 ```bash
-python scripts/tools/train.py \
+python scripts/tools/train.py --train_type=seq --data_root_path=/path/to/dataset --param_path=/path/to/model
-  --train_type=seq \
-  --data_root_path=/path/to/dataset \
-  --param_path=/path/to/model \
-  --n_epoch=3 \
-  --batch_size=4 \
-  --accumulation_steps=8 \
-  --max_lr=3e-4 \
-  --warmup_steps=2000 \
-  --ckpt_interval=5000 \
-  --ckpt_dir=./checkpoints
 ```
 
+| 参数 | 说明 | 默认值 |
+|------|------|--------|
+| `--train_type` | 训练类型（`seq`, `sft`, `dpo`, `grpo`） | 必填 |
+| `--data_root_path` | 数据集根目录 | 必填 |
+| `--param_path` | 模型参数或断点路径 | 必填 |
+| `--n_epoch` | 训练轮数 | 1 |
+| `--batch_size` | 批次大小 | 1 |
+| `--accumulation_steps` | 梯度累积步数 | 1 |
+| `--warmup_steps` | 预热步数 | 1000 |
+| `--max_lr` | 峰值学习率（余弦衰减） | 3e-4 |
+| `--max_grad_norm` | 梯度裁剪最大值 | 1.0 |
+| `--adamw_beta1` | AdamW beta1 | 0.9 |
+| `--adamw_beta2` | AdamW beta2 | 0.95 |
+| `--adamw_weight_decay` | AdamW 权重衰减 | 0.01 |
+| `--random_seed` | 随机种子 | 3407 |
+| `--num_workers` | 数据加载线程数 | 4 |
+| `--window_size` | 最大输入序列长度 | auto |
+| `--stride` | 序列步长 | auto |
+| `--label_smoothing` | 交叉熵标签平滑 | 0.1 |
+| `--dpo_beta` | DPO beta | 0.1 |
+| `--grpo_clip_eps` | GRPO 裁剪 epsilon | 0.2 |
+| `--grpo_kl_coef` | GRPO KL 惩罚系数 | 0.01 |
+| `--group_size` | GRPO 组大小 | 4 |
+| `--grpo_sync_interval` | GRPO ref_model 同步间隔（步） | 200 |
+| `--ckpt_interval` | 检查点间隔（迭代步） | 5000 |
+| `--ckpt_dir` | 检查点保存目录 | checkpoint |
+| `--start_epoch` | 起始轮次（用于断点续训） | 0 |
+| `--start_batch` | 起始批次（用于断点续训） | 0 |
+| `--nprocs` | GPU 数量 | 1 |
+| `--device_type` | 设备类型 | cuda |
+
+完整参数列表见[参数说明](./params.md#training-parameters)。
+
 #### 文本生成
 
 ```bash
 python scripts/tools/generate.py --param_path=/path/to/param_path
 ```
 
-#### 训练参数
-
-| 参数 | 说明 | 默认值 |
-|------|------|--------|
-| `--train_type` | 训练类型（`seq`, `sft`, `dpo`） | 必填 |
-| `--data_root_path` | 数据集根目录 | 必填 |
-| `--param_path` | 模型参数或断点路径 | 必填 |
-| `--n_epoch` | 训练轮数 | 1 |
-| `--batch_size` | 批次大小 | 1 |
-| `--accumulation_steps` | 梯度累积步数 | 1 |
-| `--max_lr` | 峰值学习率（余弦衰减） | 3e-4 |
-| `--warmup_steps` | 预热步数 | 1000 |
-| `--ckpt_interval` | 检查点间隔（迭代步） | 5000 |
-| `--ckpt_dir` | 检查点保存目录 | checkpoint |
-| `--num_workers` | 数据加载线程数 | 4 |
-| `--nprocs` | GPU 数量 | 1 |
-
-完整参数列表见[参数说明](./params.md#training-parameters)。
-
 #### Docker
 
 使用 Docker 构建和运行（推荐用于 GPU 环境）：
@@ -131,13 +136,19 @@ docker run --gpus all -p 8000:8000 astrai:latest \
 
 # 挂载数据卷
 docker run --gpus all -v /path/to/data:/data -it astrai:latest
+
+# Docker Compose（GPU，默认）
+docker compose up -d
+
+# Docker Compose（仅 CPU）
+docker compose --profile cpu up -d
 ```
 
 > **注意**: 必须使用 `--gpus all` 才能启用 CUDA 支持，否则 `torch.cuda.is_available()` 将返回 `False`。
 
 #### 启动 HTTP 服务
 
-启动推理服务器，支持 OpenAI 兼容的 HTTP API：
+启动推理服务器，支持 OpenAI 和 Anthropic 兼容的 HTTP API：
 
 ```bash
 python -m scripts.tools.server --port 8000 --device cuda
@@ -146,7 +157,7 @@ python -m scripts.tools.server --port 8000 --device cuda
 发起请求：
 
 ```bash
-# Chat API（OpenAI 兼容）
+# OpenAI 兼容
 curl -X POST http://localhost:8000/v1/chat/completions \
   -H "Content-Type: application/json" \
   -d '{
@@ -154,7 +165,7 @@ curl -X POST http://localhost:8000/v1/chat/completions \
     "max_tokens": 512
   }'
 
-# 流式响应
+# OpenAI 兼容流式
 curl -X POST http://localhost:8000/v1/chat/completions \
   -H "Content-Type: application/json" \
   -d '{
@@ -163,6 +174,27 @@ curl -X POST http://localhost:8000/v1/chat/completions \
     "max_tokens": 500
   }'
 
+# Anthropic 兼容
+curl -X POST http://localhost:8000/v1/messages \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "astrai",
+    "system": "你是一个乐于助人的助手。",
+    "messages": [{"role": "user", "content": "你好"}],
+    "max_tokens": 512
+  }'
+
+# Anthropic 兼容流式并设置停止序列
+curl -X POST http://localhost:8000/v1/messages \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "astrai",
+    "messages": [{"role": "user", "content": "写个故事"}],
+    "max_tokens": 500,
+    "stream": true,
+    "stop_sequences": ["结束"]
+  }'
+
 # 健康检查
 curl http://localhost:8000/health
 ```

assets/docs/introduction.md

@@ -262,25 +262,60 @@ curl -X POST http://localhost:8000/v1/chat/completions \
 
 The server uses Server-Sent Events (SSE) with content type `text/event-stream`.
 
-### Simple Generation Endpoint
+### Health Check
 
-For basic text generation without chat format:
+
+### Anthropic-Compatible Endpoint
+
+The server also provides an Anthropic-compatible endpoint at `/v1/messages`:
 
 ```bash
-curl -X POST "http://localhost:8000/generate?query=Hello&max_len=1000" \
+curl -X POST http://localhost:8000/v1/messages \
-  -H "Content-Type: application/json"
-```
-
-Or with conversation history:
-
-```bash
-curl -X POST "http://localhost:8000/generate" \
   -H "Content-Type: application/json" \
   -d '{
-    "query": "What is AI?",
+    "model": "astrai",
-    "history": [["Hello", "Hi there!"], ["How are you?", "I'm doing well"]],
+    "system": "You are a helpful assistant.",
-    "temperature": 0.8,
+    "messages": [{"role": "user", "content": "Hello, how are you?"}],
-    "max_len": 2048
+    "max_tokens": 2048
+  }'
+```
+
+Response:
+```json
+{
+  "id": "msg_abc123...",
+  "type": "message",
+  "role": "assistant",
+  "model": "astrai",
+  "content": [{"type": "text", "text": "Hello! I am doing well..."}],
+  "stop_reason": "end_turn",
+  "stop_sequence": null,
+  "usage": {"input_tokens": 20, "output_tokens": 15}
+}
+```
+
+Streaming:
+```bash
+curl -X POST http://localhost:8000/v1/messages \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "astrai",
+    "system": "You are a helpful assistant.",
+    "messages": [{"role": "user", "content": "Write a short poem"}],
+    "max_tokens": 500,
+    "stream": true
+  }'
+```
+
+Supports `stop_sequences` for early termination:
+```bash
+curl -X POST http://localhost:8000/v1/messages \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "astrai",
+    "messages": [{"role": "user", "content": "Write a story"}],
+    "max_tokens": 500,
+    "stop_sequences": ["The end", "THE END"]
   }'
 ```
 

astrai/inference/server.py

@@ -1,5 +1,5 @@
 """
-OpenAI-compatible chat completion server backed by continuous-batching inference.
+OpenAI / Anthropic-compatible chat completion server backed by continuous-batching inference.
 """
 
 import json
@@ -51,6 +51,7 @@ class ChatCompletionRequest(BaseModel):
     messages: List[ChatMessage]
     temperature: Optional[float] = Field(default=1.0, ge=0.0, le=2.0)
     top_p: Optional[float] = Field(default=1.0, ge=0.0, le=1.0)
+    top_k: Optional[int] = Field(default=50, ge=1)
     stream: Optional[bool] = False
     stop: Optional[Union[str, List[str]]] = None
     max_tokens: Optional[int] = Field(default=2048, ge=1)
@@ -61,6 +62,25 @@ class ChatCompletionRequest(BaseModel):
     user: Optional[str] = None
 
 
+class AnthropicMessage(BaseModel):
+    role: str
+    content: Union[str, List[Dict[str, Any]]]
+
+
+class MessagesRequest(BaseModel):
+    """Anthropic Messages API request body."""
+
+    model: str = "astrai"
+    max_tokens: int = Field(default=1024, ge=1)
+    messages: List[AnthropicMessage]
+    system: Optional[str] = None
+    temperature: Optional[float] = Field(default=1.0, ge=0.0, le=2.0)
+    top_p: Optional[float] = Field(default=1.0, ge=0.0, le=1.0)
+    top_k: Optional[int] = Field(default=50, ge=1)
+    stream: Optional[bool] = False
+    stop_sequences: Optional[List[str]] = None
+
+
 def configure_server(
     device: str = "cuda",
     dtype: torch.dtype = torch.bfloat16,
@@ -185,7 +205,7 @@ async def chat_completion(request: ChatCompletionRequest):
             max_tokens=request.max_tokens,
             temperature=request.temperature,
             top_p=request.top_p,
-            top_k=50,
+            top_k=request.top_k,
         )
 
         async def event_stream():
@@ -237,7 +257,7 @@ async def chat_completion(request: ChatCompletionRequest):
         max_tokens=request.max_tokens,
         temperature=request.temperature,
         top_p=request.top_p,
-        top_k=50,
+        top_k=request.top_k,
     )
     async for token in agen:
         chunks.append(token)
@@ -264,55 +284,183 @@ async def chat_completion(request: ChatCompletionRequest):
     }
 
 
-@app.post("/generate")
+def _make_anthropic_sse(event: str, data: Dict[str, Any]) -> str:
-async def generate(
+    return f"event: {event}\ndata: {json.dumps(data, ensure_ascii=False)}\n\n"
-    query: str,
+
-    history: Optional[List[List[str]]] = None,
+
-    temperature: float = 0.8,
+def _check_stop_sequence(text: str, stop_sequences: List[str]) -> Optional[str]:
-    top_p: float = 0.95,
+    for seq in stop_sequences:
-    top_k: int = 50,
+        if seq and seq in text:
-    max_len: int = 2048,
+            return seq
-    stream: bool = False,
+    return None
-):
+
-    """Legacy non-OpenAI generation endpoint (kept for backward compat)."""
+
+def _extract_text_content(content: Union[str, List[Dict[str, Any]]]) -> str:
+    if isinstance(content, str):
+        return content
+    if isinstance(content, list):
+        for block in content:
+            if isinstance(block, dict) and block.get("type") == "text":
+                return block.get("text", "")
+    return ""
+
+
+def _build_anthropic_messages(
+    messages: List[AnthropicMessage], system: Optional[str]
+) -> List[Dict[str, str]]:
+    result: List[Dict[str, str]] = []
+    if system:
+        result.append({"role": "system", "content": system})
+    for m in messages:
+        content = _extract_text_content(m.content)
+        if content:
+            result.append({"role": m.role, "content": content})
+    return result
+
+
+@app.post("/v1/messages")
+async def create_message(request: MessagesRequest):
+    """Anthropic-compatible Messages API endpoint (streaming + non-streaming)."""
     engine = _get_engine()
+    resp_id = f"msg_{uuid.uuid4().hex[:24]}"
+    model = request.model
 
-    messages = []
+    chat_messages = _build_anthropic_messages(request.messages, request.system)
-    if history:
+    prompt = engine.tokenizer.apply_chat_template(chat_messages, tokenize=False)
-        for h in history:
+    prompt_tokens = len(engine.tokenizer.encode(prompt))
-            if len(h) >= 2:
-                messages.append({"role": "user", "content": h[0]})
-                messages.append({"role": "assistant", "content": h[1]})
-    messages.append({"role": "user", "content": query})
 
-    prompt = engine.tokenizer.apply_chat_template(messages, tokenize=False)
+    stop_sequences = request.stop_sequences or []
 
-    if stream:
+    if request.stream:
         agen = engine.generate_async(
             prompt=prompt,
-            max_tokens=max_len,
+            max_tokens=request.max_tokens,
-            temperature=temperature,
+            temperature=request.temperature,
-            top_p=top_p,
+            top_p=request.top_p,
-            top_k=top_k,
+            top_k=request.top_k,
         )
 
-        async def text_stream():
+        async def event_stream():
-            async for token in agen:
+            yield _make_anthropic_sse(
-                yield token + "\n"
+                "message_start",
+                {
+                    "type": "message_start",
+                    "message": {
+                        "id": resp_id,
+                        "type": "message",
+                        "role": "assistant",
+                        "model": model,
+                        "content": [],
+                        "usage": {"input_tokens": prompt_tokens},
+                    },
+                },
+            )
 
-        return StreamingResponse(text_stream(), media_type="text/plain")
+            yield _make_anthropic_sse(
-    else:
+                "content_block_start",
-        chunks = []
+                {
-        for token in engine.generate(
+                    "type": "content_block_start",
-            prompt=prompt,
+                    "index": 0,
-            stream=True,
+                    "content_block": {"type": "text", "text": ""},
-            max_tokens=max_len,
+                },
-            temperature=temperature,
+            )
-            top_p=top_p,
+
-            top_k=top_k,
+            completion_tokens = 0
-        ):
+            accumulated = ""
-            chunks.append(token)
+            stopped_seq: Optional[str] = None
-        return {"response": "".join(chunks)}
+            async for token in agen:
+                accumulated += token
+                completion_tokens += 1
+
+                matched = _check_stop_sequence(accumulated, stop_sequences)
+                if matched:
+                    text = accumulated[: accumulated.rfind(matched)]
+                    stopped_seq = matched
+                    if text:
+                        yield _make_anthropic_sse(
+                            "content_block_delta",
+                            {
+                                "type": "content_block_delta",
+                                "index": 0,
+                                "delta": {"type": "text_delta", "text": text},
+                            },
+                        )
+                    break
+
+                yield _make_anthropic_sse(
+                    "content_block_delta",
+                    {
+                        "type": "content_block_delta",
+                        "index": 0,
+                        "delta": {"type": "text_delta", "text": token},
+                    },
+                )
+
+            yield _make_anthropic_sse(
+                "content_block_stop",
+                {"type": "content_block_stop", "index": 0},
+            )
+
+            stop_reason = "stop_sequence" if stopped_seq else "end_turn"
+            yield _make_anthropic_sse(
+                "message_delta",
+                {
+                    "type": "message_delta",
+                    "delta": {"stop_reason": stop_reason, "stop_sequence": stopped_seq},
+                    "usage": {"output_tokens": completion_tokens},
+                },
+            )
+
+            yield _make_anthropic_sse(
+                "message_stop",
+                {"type": "message_stop"},
+            )
+
+        return StreamingResponse(
+            event_stream(),
+            media_type="text/event-stream",
+            headers={"Cache-Control": "no-cache", "Connection": "keep-alive"},
+        )
+
+    completion_tokens = 0
+    chunks: List[str] = []
+    agen = engine.generate_async(
+        prompt=prompt,
+        max_tokens=request.max_tokens,
+        temperature=request.temperature,
+        top_p=request.top_p,
+        top_k=request.top_k,
+    )
+    stopped_seq: Optional[str] = None
+    accumulated = ""
+    async for token in agen:
+        chunks.append(token)
+        completion_tokens += 1
+        accumulated += token
+        matched = _check_stop_sequence(accumulated, stop_sequences)
+        if matched:
+            stopped_seq = matched
+            break
+
+    content = "".join(chunks)
+    if stopped_seq:
+        idx = content.rfind(stopped_seq)
+        if idx != -1:
+            content = content[:idx]
+
+    return {
+        "id": resp_id,
+        "type": "message",
+        "role": "assistant",
+        "model": model,
+        "content": [{"type": "text", "text": content}],
+        "stop_reason": "stop_sequence" if stopped_seq else "end_turn",
+        "stop_sequence": stopped_seq,
+        "usage": {
+            "input_tokens": prompt_tokens,
+            "output_tokens": completion_tokens,
+        },
+    }
 
 
 def run_server(

astrai/trainer/strategy.py

@@ -265,7 +265,9 @@ class DPOStrategy(BaseStrategy):
 class GRPOStrategy(BaseStrategy):
     """Group Relative Policy Optimization strategy.
 
-    Implements GRPO with clipping and KL penalty.
+    On-policy GRPO following DeepSeek-R1: the policy model is updated while
+    a frozen ref_model stores the old-policy log-probs.  ratio = exp(logπ_θ - logπ_ref),
+    clipped PPO objective.  Call ``sync_ref_model()`` after each data-generation round.
     """
 
     def __init__(
@@ -276,6 +278,7 @@ class GRPOStrategy(BaseStrategy):
         kl_coef: float = 0.01,
         group_size: int = 4,
         reduction: str = "mean",
+        sync_interval: int = 200,
         **kwargs,
     ):
         super().__init__(model, device, **kwargs)
@@ -284,8 +287,19 @@ class GRPOStrategy(BaseStrategy):
         self.kl_coef = kl_coef
         self.group_size = group_size
         self.reduction = reduction
+        self.sync_interval = sync_interval
+        self._step = 0
+
+    def sync_ref_model(self):
+        """Copy current model weights to ref model."""
+        ref_state = self.model.state_dict()
+        self.ref_model.load_state_dict(ref_state)
 
     def compute_loss(self, batch: Dict[str, Tensor]) -> Tensor:
+        self._step += 1
+        if self._step % self.sync_interval == 0:
+            self.sync_ref_model()
+
         batch = move_to_device(batch, self.device)
         prompts = batch["prompts"]
         responses = batch["responses"]
@@ -297,7 +311,6 @@ class GRPOStrategy(BaseStrategy):
         masks_flat = masks.view(-1, response_len)
         prompt_expanded = prompts.unsqueeze(1).repeat(1, group_size, 1).flatten(0, 1)
 
-        # Shape: (batch_size * group_size, seq_len + response_len)
         full_sequences = torch.cat([prompt_expanded, responses_flat], dim=-1)
         full_masks = torch.cat([torch.ones_like(prompt_expanded), masks_flat], dim=-1)
 
@@ -312,14 +325,13 @@ class GRPOStrategy(BaseStrategy):
             )
             log_probs_ref = log_probs_ref.view(batch_size, group_size)
 
-        # Compute advantages from rewards with normalization
         eps = torch.finfo(log_probs_policy.dtype).eps
         mean = rewards.mean(dim=-1, keepdim=True)
         std = rewards.std(dim=-1, keepdim=True)
         advantages = (rewards - mean) / (std + eps)
 
-        # PPO-style clipped surrogate objective
+        ratio = torch.exp(log_probs_policy - log_probs_ref)
-        ratio = torch.exp(0)  # Off-policy: policy_model = old_model
+
         surr1 = ratio * advantages
         surr2 = torch.clamp(ratio, 1 - self.clip_eps, 1 + self.clip_eps) * advantages
 

docker-compose.yml

@@ -0,0 +1,42 @@
+services:
+  server:
+    build: .
+    image: astrai:latest
+    ports:
+      - "8000:8000"
+    volumes:
+      - ./params:/app/params:ro
+      - ./checkpoints:/app/checkpoints
+    command: python -m scripts.tools.server --port 8000 --device cuda
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: 1
+              capabilities: [gpu]
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
+      interval: 30s
+      timeout: 10s
+      retries: 3
+      start_period: 60s
+    restart: unless-stopped
+
+  server-cpu:
+    profiles: [cpu]
+    build: .
+    image: astrai:latest
+    ports:
+      - "8000:8000"
+    volumes:
+      - ./params:/app/params:ro
+      - ./checkpoints:/app/checkpoints
+    command: python -m scripts.tools.server --port 8000 --device cpu
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
+      interval: 30s
+      timeout: 10s
+      retries: 3
+      start_period: 120s
+    restart: unless-stopped

scripts/tools/train.py

@@ -23,7 +23,7 @@ def parse_args() -> argparse.Namespace:
         "--train_type",
         type=str,
         required=True,
-        choices=["seq", "sft", "dpo"],
+        choices=["seq", "sft", "dpo", "grpo"],
         help="Train type.",
     )
     parser.add_argument(
@@ -42,9 +42,7 @@ def parse_args() -> argparse.Namespace:
     parser.add_argument(
         "--n_epoch", type=int, default=1, help="Number of epochs to train."
     )
-    parser.add_argument(
+    parser.add_argument("--group_size", type=int, default=4, help="GRPO group size.")
-        "--batch_size", type=int, default=1, help="Batch size for training."
-    )
     parser.add_argument(
         "--accumulation_steps",
         type=int,
@@ -106,6 +104,17 @@ def parse_args() -> argparse.Namespace:
         "--stride", type=int, default=None, help="the step size of the input sequence."
     )
     parser.add_argument("--dpo_beta", type=float, default=0.1, help="DPO beta value.")
+    parser.add_argument("--group_size", type=int, default=4, help="GRPO group size.")
+    parser.add_argument(
+        "--on_policy",
+        action="store_true",
+        default=False,
+        help="Enable on-policy GRPO mode.",
+    )
+    parser.add_argument(
+        "--grpo_kl_coef", type=float, default=0.01, help="GRPO KL penalty coefficient."
+    )
+    parser.add_argument("--group_size", type=int, default=4, help="GRPO group size.")
     parser.add_argument(
         "--label_smoothing",
         type=float,
@@ -125,6 +134,13 @@ def parse_args() -> argparse.Namespace:
         default="checkpoint",
         help="Directory to save checkpoints.",
     )
+    parser.add_argument("--group_size", type=int, default=4, help="GRPO group size.")
+    parser.add_argument(
+        "--grpo_sync_interval",
+        type=int,
+        default=200,
+        help="GRPO ref model sync interval (steps).",
+    )
     parser.add_argument(
         "--start_epoch", type=int, default=0, help="Start epoch for training."
     )
@@ -182,6 +198,10 @@ def train(
     ckpt_interval: int,
     ckpt_dir: str,
     dpo_beta: float,
+    grpo_clip_eps: float,
+    grpo_kl_coef: float,
+    group_size: int,
+    grpo_sync_interval: int,
     adamw_beta1: float,
     adamw_beta2: float,
     adamw_weight_decay: float,
@@ -195,7 +215,7 @@ def train(
     nprocs: int,
     device_type: str,
 ):
-    assert train_type in ["seq", "sft", "dpo"]
+    assert train_type in ["seq", "sft", "dpo", "grpo"]
     assert os.path.exists(param_path)
 
     # Load config
@@ -216,7 +236,14 @@ def train(
         state_dict = st.load_file(weights_path)
         model.load_state_dict(state_dict, strict=False)
 
-    strategy_kwargs = {"dpo_beta": dpo_beta, "label_smoothing": label_smoothing}
+    strategy_kwargs = {
+        "dpo_beta": dpo_beta,
+        "label_smoothing": label_smoothing,
+        "clip_eps": grpo_clip_eps,
+        "kl_coef": grpo_kl_coef,
+        "group_size": group_size,
+        "sync_interval": grpo_sync_interval,
+    }
 
     dataset = DatasetFactory.load(
         train_type=train_type,

tests/inference/test_server.py

@@ -1,7 +1,5 @@
 """Unit tests for the inference HTTP server."""
 
-from unittest.mock import MagicMock
-
 import pytest
 
 
@@ -24,52 +22,6 @@ def test_health_with_model(client, loaded_model):
     assert data["model_loaded"] is True
 
 
-def test_generate_non_stream(client, loaded_model, monkeypatch):
-    """POST /generate with stream=false should return JSON response."""
-    response = client.post(
-        "/generate",
-        params={
-            "query": "Hello",
-            "temperature": 0.8,
-            "top_p": 0.95,
-            "top_k": 50,
-            "max_len": 100,
-            "stream": False,
-        },
-    )
-    assert response.status_code == 200
-    data = response.json()
-    assert "response" in data
-
-
-def test_generate_stream(client, loaded_model, monkeypatch):
-    """POST /generate with stream=true should return plain text stream."""
-
-    async def async_gen():
-        yield "chunk1"
-        yield "chunk2"
-
-    mock_engine = loaded_model
-    mock_engine.generate_async.return_value = async_gen()
-    monkeypatch.setattr("astrai.inference.server._state.engine", mock_engine)
-    response = client.post(
-        "/generate",
-        params={
-            "query": "Hello",
-            "temperature": 0.8,
-            "top_p": 0.95,
-            "top_k": 50,
-            "max_len": 100,
-            "stream": True,
-        },
-        headers={"Accept": "text/plain"},
-    )
-    assert response.status_code == 200
-    content = response.content.decode("utf-8")
-    assert "chunk1" in content
-    assert "chunk2" in content
-
-
 def test_chat_completions_non_stream(client, loaded_model, monkeypatch):
     """POST /v1/chat/completions with stream=false returns OpenAI-style JSON."""
 
@@ -125,17 +77,87 @@ def test_chat_completions_stream(client, loaded_model, monkeypatch):
     assert any("[DONE]" in line for line in lines)
 
 
-def test_generate_with_history(client, loaded_model, monkeypatch):
+def test_messages_non_stream(client, loaded_model, monkeypatch):
-    """POST /generate with history parameter."""
+    """POST /v1/messages with stream=false returns Anthropic-style JSON."""
+
+    async def async_gen():
+        yield "Assistant reply"
+
+    mock_engine = loaded_model
+    mock_engine.generate_async.return_value = async_gen()
+    monkeypatch.setattr("astrai.inference.server._state.engine", mock_engine)
     response = client.post(
-        "/generate",
+        "/v1/messages",
-        params={
+        json={
-            "query": "Hi",
+            "messages": [{"role": "user", "content": "Hello"}],
-            "history": [["user1", "assistant1"], ["user2", "assistant2"]],
+            "temperature": 0.8,
+            "max_tokens": 100,
             "stream": False,
         },
     )
     assert response.status_code == 200
+    data = response.json()
+    assert data["type"] == "message"
+    assert data["role"] == "assistant"
+    assert len(data["content"]) == 1
+    assert data["content"][0]["type"] == "text"
+    assert "usage" in data
+    assert "input_tokens" in data["usage"]
+
+
+def test_messages_stream(client, loaded_model, monkeypatch):
+    """POST /v1/messages with stream=true returns Anthropic SSE stream."""
+
+    async def async_gen():
+        yield "cumulative1"
+        yield "cumulative2"
+
+    mock_engine = loaded_model
+    mock_engine.generate_async.return_value = async_gen()
+    monkeypatch.setattr("astrai.inference.server._state.engine", mock_engine)
+    response = client.post(
+        "/v1/messages",
+        json={
+            "messages": [{"role": "user", "content": "Hello"}],
+            "temperature": 0.8,
+            "max_tokens": 100,
+            "stream": True,
+        },
+        headers={"Accept": "text/event-stream"},
+    )
+    assert response.status_code == 200
+    content = response.content.decode("utf-8")
+    assert "message_start" in content
+    assert "content_block_start" in content
+    assert "content_block_delta" in content
+    assert "cumulative1" in content
+    assert "cumulative2" in content
+    assert "content_block_stop" in content
+    assert "message_delta" in content
+    assert "message_stop" in content
+
+
+def test_messages_with_system(client, loaded_model, monkeypatch):
+    """POST /v1/messages with system prompt."""
+
+    async def async_gen():
+        yield "Reply"
+
+    mock_engine = loaded_model
+    mock_engine.generate_async.return_value = async_gen()
+    monkeypatch.setattr("astrai.inference.server._state.engine", mock_engine)
+    response = client.post(
+        "/v1/messages",
+        json={
+            "messages": [{"role": "user", "content": "Hello"}],
+            "system": "You are a helpful assistant.",
+            "max_tokens": 100,
+            "stream": False,
+        },
+    )
+    assert response.status_code == 200
+    data = response.json()
+    assert data["type"] == "message"
 
 
 if __name__ == "__main__":