Update model card: add system prompt, inference params, reasoning parser flags

README.md

@@ -22,8 +22,6 @@ quantization_config:
 
 AWQ INT4 quantization of [microsoft/Phi-4-reasoning](https://huggingface.co/microsoft/Phi-4-reasoning) (14B parameters).
 
-Calibrated with WikiText-2 for general-purpose use.
-
 ## Quantization Details
 
 | Parameter | Value |
@@ -43,11 +41,36 @@ Calibrated with WikiText-2 for general-purpose use.
 vllm serve steadyflow/Phi-4-reasoning-AWQ \
   --quantization awq \
   --gpu-memory-utilization 0.90 \
-  --max-model-len 4096
+  --max-model-len 4096 \
+  --enable-reasoning \
+  --reasoning-parser deepseek_r1
 ```
 
+The `--enable-reasoning --reasoning-parser deepseek_r1` flags separate the chain-of-thought (`reasoning` field) from the final answer (`content` field) in the OpenAI-compatible API response.
+
+> **Known issue:** Some vLLM versions have bugs with Phi-4 reasoning output (infinite loops, missing `<think>` tags). If you encounter this, try setting `VLLM_USE_V1=0` or omit the reasoning flags and parse `<think>...</think>` tags from the output yourself.
+
 Fits on a single NVIDIA T4 GPU (16GB VRAM).
 
+## Recommended Inference Parameters
+
+Per Microsoft's model card, use these parameters for best reasoning quality:
+
+```python
+temperature = 0.8
+top_p = 0.95
+do_sample = True
+max_new_tokens = 32768  # increase for complex reasoning chains
+```
+
+## Required System Prompt
+
+Phi-4 reasoning models require a specific system prompt to activate structured reasoning with `<think>` tags:
+
+```
+Your role as an assistant involves thoroughly exploring questions through a systematic thinking process before providing the final precise and accurate solutions. This requires engaging in a comprehensive cycle of analysis, summarizing, exploration, reassessment, reflection, backtracing, and iteration to develop well-considered thinking process. Please structure your response into two main sections: Thought and Solution using the specified format: <think> {Thought section} </think> {Solution section}. In the Thought section, detail your reasoning process in steps. Each step should include detailed considerations such as analysing questions, summarizing relevant findings, brainstorming new ideas, verifying the accuracy of the current steps, refining any errors, and revisiting previous steps. In the Solution section, based on various attempts, explorations, and reflections from the Thought section, systematically present the final solution that you deem correct. The Solution section should be logical, accurate, and concise and detail necessary steps needed to reach the conclusion. Now, try to solve the following question through the above guidelines:
+```
+
 ## Usage with Transformers
 
 ```python
@@ -56,4 +79,12 @@ from transformers import AutoTokenizer
 
 model = AutoAWQForCausalLM.from_quantized("steadyflow/Phi-4-reasoning-AWQ")
 tokenizer = AutoTokenizer.from_pretrained("steadyflow/Phi-4-reasoning-AWQ")
+
+messages = [
+    {"role": "system", "content": "<system prompt above>"},
+    {"role": "user", "content": "Your question here"},
+]
+inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
+outputs = model.generate(inputs.to(model.device), max_new_tokens=4096, temperature=0.8, top_p=0.95, do_sample=True)
+print(tokenizer.decode(outputs[0]))
 ```