Update README.md

README.md

@@ -105,7 +105,6 @@ Training used eight NVIDIA H100 GPUs, with 3,034 GRPO steps across five phases.
 > **Evaluation Settings Note:** All benchmarks were evaluated with extended thinking enabled (`enable_thinking: true`), a maximum generation length of `max_tokens: 65536`, and `temperature: 0.0`.
 >
 > Alpie Seed scores are from the FP16 model and do not apply to the 2-bit, 3-bit, or 4-bit variants. **Bold** indicates the best score in each row. Comparison-model scores are taken from published model cards and reports (including the [Qwen3.5-9B model card](https://huggingface.co/Qwen/Qwen3.5-9B)).
-
 ### 🧮 Language
 
 <div style="background-color: #FAF5FF; border: 1.5px solid #C4B5FD; border-radius: 14px; padding: 12px; margin-bottom: 24px; box-shadow: 0 4px 12px rgba(88, 37, 179, 0.05); overflow-x: auto;">
@@ -285,6 +284,8 @@ Training used eight NVIDIA H100 GPUs, with 3,034 GRPO steps across five phases.
   </table>
 </div>
 
+     
+    
 - SWE-Bench Verified: resolved rate using Mini-SWE-agent, with retries disabled.
 - Comparison-model scores were not re-run by 169Pi, and harnesses, prompts, and benchmark versions differ, so cross-model comparisons are indicative rather than strictly like-for-like. Scores vary with dataset versions, prompts, evaluation harnesses, and inference settings.
 - Empty cells (--) indicate scores not yet available or not applicable.
@@ -299,4 +300,220 @@ Training used eight NVIDIA H100 GPUs, with 3,034 GRPO steps across five phases.
 Qwen3.5 support requires a recent Transformers release (Transformers 5 or later).
 
 ```bash
-pip install -U "transformers>=5.2.0" torch torchvision pillow accelerate
+pip install -U "transformers>=5.2.0" torch torchvision pillow accelerate
+```
+
+### Serve with vLLM (OpenAI-compatible API)
+
+Start the standard Alpie Seed 9B server:
+
+```bash
+vllm serve 169Pi/Alpie_seed \
+  --port 8000 \
+  --max-model-len 262144 \
+  --reasoning-parser qwen3
+```
+
+To enable automatic tool-call parsing, use this command instead:
+
+```bash
+vllm serve 169Pi/Alpie_seed \
+  --port 8000 \
+  --max-model-len 262144 \
+  --reasoning-parser qwen3 \
+  --enable-auto-tool-choice \
+  --tool-call-parser qwen3_coder
+```
+
+The API is available at `http://localhost:8000/v1`. The tool-calling options enable vLLM to parse model-generated tool calls; your application is still responsible for executing the requested tools and returning their results.
+
+### Run with Transformers
+
+The following examples load the full vision-language checkpoint using `AutoModelForImageTextToText` and `AutoProcessor`. They retain the model's multimodal architecture. The `enable_thinking` argument is passed to the chat template to select the response mode.
+
+#### 1. Thinking mode (`enable_thinking=True`)
+
+```python
+import torch
+from transformers import AutoModelForImageTextToText, AutoProcessor
+
+MODEL_ID = "169Pi/Alpie_seed"
+
+processor = AutoProcessor.from_pretrained(MODEL_ID)
+model = AutoModelForImageTextToText.from_pretrained(
+    MODEL_ID,
+    torch_dtype="auto",
+    device_map="auto",
+)
+model.eval()
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "text", "text": "Explain why the sky appears blue."}
+        ],
+    }
+]
+
+inputs = processor.apply_chat_template(
+    messages,
+    tokenize=True,
+    add_generation_prompt=True,
+    enable_thinking=True,
+    return_dict=True,
+    return_tensors="pt",
+).to(model.device)
+
+with torch.inference_mode():
+    output_ids = model.generate(
+        **inputs,
+        max_new_tokens=4096,
+        do_sample=True,
+        temperature=0.7,
+        top_p=0.8,
+    )
+
+new_tokens = output_ids[0, inputs["input_ids"].shape[1]:]
+response = processor.decode(new_tokens, skip_special_tokens=True)
+print(response)
+```
+
+#### 2. Non-thinking mode (`enable_thinking=False`)
+
+```python
+import torch
+from transformers import AutoModelForImageTextToText, AutoProcessor
+
+MODEL_ID = "169Pi/Alpie_seed"
+
+processor = AutoProcessor.from_pretrained(MODEL_ID)
+model = AutoModelForImageTextToText.from_pretrained(
+    MODEL_ID,
+    torch_dtype="auto",
+    device_map="auto",
+)
+model.eval()
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "text", "text": "Explain why the sky appears blue in two sentences."}
+        ],
+    }
+]
+
+inputs = processor.apply_chat_template(
+    messages,
+    tokenize=True,
+    add_generation_prompt=True,
+    enable_thinking=False,
+    return_dict=True,
+    return_tensors="pt",
+).to(model.device)
+
+with torch.inference_mode():
+    output_ids = model.generate(
+        **inputs,
+        max_new_tokens=512,
+        do_sample=True,
+        temperature=0.7,
+        top_p=0.8,
+    )
+
+new_tokens = output_ids[0, inputs["input_ids"].shape[1]:]
+response = processor.decode(new_tokens, skip_special_tokens=True)
+print(response)
+```
+
+#### 3. Tool calling with Transformers
+
+Transformers can format the available tool definitions into the prompt. The model generates a tool-call request; your application must parse the request, execute the corresponding function, append the tool result, and ask the model to continue. This example demonstrates generating and inspecting a tool call; it does not execute external tools automatically.
+
+```python
+import json
+import torch
+from transformers import AutoModelForImageTextToText, AutoProcessor
+
+MODEL_ID = "169Pi/Alpie_seed"
+
+processor = AutoProcessor.from_pretrained(MODEL_ID)
+model = AutoModelForImageTextToText.from_pretrained(
+    MODEL_ID,
+    torch_dtype="auto",
+    device_map="auto",
+)
+model.eval()
+
+tools = [
+    {
+        "type": "function",
+        "function": {
+            "name": "get_weather",
+            "description": "Get the current weather for a city.",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "city": {
+                        "type": "string",
+                        "description": "The city to check.",
+                    }
+                },
+                "required": ["city"],
+            },
+        },
+    }
+]
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "text", "text": "What is the weather like in Patna?"}
+        ],
+    }
+]
+
+inputs = processor.apply_chat_template(
+    messages,
+    tools=tools,
+    tokenize=True,
+    add_generation_prompt=True,
+    enable_thinking=True,
+    return_dict=True,
+    return_tensors="pt",
+).to(model.device)
+
+with torch.inference_mode():
+    output_ids = model.generate(
+        **inputs,
+        max_new_tokens=1024,
+        do_sample=True,
+        temperature=0.7,
+        top_p=0.8,
+    )
+
+new_tokens = output_ids[0, inputs["input_ids"].shape[1]:]
+generated_text = processor.decode(new_tokens, skip_special_tokens=False)
+print("Model output:")
+print(generated_text)
+
+# Tool execution is application-side. Inspect the generated tool-call
+# structure, validate its arguments, and dispatch only to tools you trust.
+```
+
+For image or video inputs, provide the corresponding image/video content items in the user message and use the same `AutoProcessor` + `AutoModelForImageTextToText` loading pattern. See the upstream Qwen3.5 documentation for multimodal message examples and framework-specific details.
+
+## Citation
+
+If you use Alpie Seed 9B in your work, please cite:
+
+```bibtex
+@misc{alpie_seed_9b_2026,
+  title        = {Alpie Seed 9B},
+  author       = {{169Pi AI}},
+  year         = {2026},
+  howpublished = {\url{https://huggingface.co/169Pi/Alpie_seed}}
+}
+```