Instructions to use fangwu97/DeepSearch-1.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fangwu97/DeepSearch-1.5B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="fangwu97/DeepSearch-1.5B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("fangwu97/DeepSearch-1.5B")
model = AutoModelForCausalLM.from_pretrained("fangwu97/DeepSearch-1.5B", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use fangwu97/DeepSearch-1.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fangwu97/DeepSearch-1.5B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fangwu97/DeepSearch-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/fangwu97/DeepSearch-1.5B

SGLang

How to use fangwu97/DeepSearch-1.5B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "fangwu97/DeepSearch-1.5B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fangwu97/DeepSearch-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "fangwu97/DeepSearch-1.5B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fangwu97/DeepSearch-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use fangwu97/DeepSearch-1.5B with Docker Model Runner:
```
docker model run hf.co/fangwu97/DeepSearch-1.5B
```

fangwu97 commited on Sep 29, 2025

Commit

d6e578b

verified ·

1 Parent(s): 83983bd

Update README.md

Browse files

Files changed (1) hide show

README.md +7 -7

README.md CHANGED Viewed

@@ -22,7 +22,7 @@ model-index:
       name: AIME 2024
       type: text
     metrics:
-    - type: pass@1
       value: 53.65
   - task:
       name: Mathematical Reasoning
@@ -31,7 +31,7 @@ model-index:
       name: AIME 2025
       type: text
     metrics:
-    - type: pass@1
       value: 35.42
   - task:
       name: Mathematical Reasoning
@@ -40,7 +40,7 @@ model-index:
       name: AMC 2023
       type: text
     metrics:
-    - type: pass@1
       value: 90.39
   - task:
       name: Mathematical Reasoning
@@ -49,7 +49,7 @@ model-index:
       name: MATH500
       type: text
     metrics:
-    - type: pass@1
       value: 92.53
   - task:
       name: Mathematical Reasoning
@@ -58,7 +58,7 @@ model-index:
       name: Minerva
       type: text
     metrics:
-    - type: pass@1
       value: 40.00
   - task:
       name: Mathematical Reasoning
@@ -67,7 +67,7 @@ model-index:
       name: Olympiad
       type: text
     metrics:
-    - type: pass@1
       value: 65.72
 ---
 <div align="center">
@@ -77,7 +77,7 @@ model-index:
 **DeepSearch-1.5B🌟** is a 1.5B parameter reasoning model trained with **Reinforcement Learning with Verifiable Rewards (RLVR)**, enhanced by **Monte Carlo Tree Search (MCTS)**.
 Unlike prior approaches that restrict structured search to inference, DeepSearch integrates MCTS *into training*, enabling systematic exploration, fine-grained credit assignment, and efficient replay buffering.
-This model achieves **state-of-the-art accuracy among 1.5B reasoning models** while being **72× more compute-efficient** than extended RL training baselines.
 ![Illstration of DeepSearch algorithm](./deepsearch.png)

       name: AIME 2024
       type: text
     metrics:
+    - type: avg@32
       value: 53.65
   - task:
       name: Mathematical Reasoning
       name: AIME 2025
       type: text
     metrics:
+    - type: avg@32
       value: 35.42
   - task:
       name: Mathematical Reasoning
       name: AMC 2023
       type: text
     metrics:
+    - type: avg@32
       value: 90.39
   - task:
       name: Mathematical Reasoning
       name: MATH500
       type: text
     metrics:
+    - type: avg@32
       value: 92.53
   - task:
       name: Mathematical Reasoning
       name: Minerva
       type: text
     metrics:
+    - type: avg@32
       value: 40.00
   - task:
       name: Mathematical Reasoning
       name: Olympiad
       type: text
     metrics:
+    - type: avg@32
       value: 65.72
 ---
 <div align="center">
 **DeepSearch-1.5B🌟** is a 1.5B parameter reasoning model trained with **Reinforcement Learning with Verifiable Rewards (RLVR)**, enhanced by **Monte Carlo Tree Search (MCTS)**.
 Unlike prior approaches that restrict structured search to inference, DeepSearch integrates MCTS *into training*, enabling systematic exploration, fine-grained credit assignment, and efficient replay buffering.
+This model achieves **state-of-the-art accuracy among 1.5B reasoning models** while being **5.7× more compute-efficient** than extended RL training baselines.
 ![Illstration of DeepSearch algorithm](./deepsearch.png)