Instructions to use tripplet-research/suzhou3.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tripplet-research/suzhou3.2 with Transformers:

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

pipe = pipeline("text-generation", model="tripplet-research/suzhou3.2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("tripplet-research/suzhou3.2", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use tripplet-research/suzhou3.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tripplet-research/suzhou3.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tripplet-research/suzhou3.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tripplet-research/suzhou3.2

SGLang

How to use tripplet-research/suzhou3.2 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 "tripplet-research/suzhou3.2" \
    --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": "tripplet-research/suzhou3.2",
		"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 "tripplet-research/suzhou3.2" \
        --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": "tripplet-research/suzhou3.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tripplet-research/suzhou3.2 with Docker Model Runner:
```
docker model run hf.co/tripplet-research/suzhou3.2
```

alextripplet commited on 12 days ago

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@@ -28,18 +28,18 @@ base_model:
 # Suzhou 3.2
-A ~12 billion parameter instruction-tuned language model by **Triplet Research**. Suzhou 3.2 is a weighted merge of Suzhou 3.1 and Qwen2.5-3B, designed to improve reasoning and math capabilities while keeping under the 15B parameter limit.
 ## Merge Details
 - **Method**: Weighted blending (70% Suzhou 3.1 + 30% Qwen2.5-3B)
-- **Model A**: Suzhou 3.1 (~9B params) - strong agent/tool-use, reasoning
-- **Model B**: Qwen2.5-3B-Instruct (~3B params) - math reasoning, general knowledge
-- **Target**: ~12B parameters total
 ## Key Features
-- **~12B parameters** - under 15B limit
 - **262K context window**
 - Strong **reasoning** and **chain-of-thought** capabilities
 - **Tool calling** and **agent** support
@@ -51,7 +51,11 @@ A ~12 billion parameter instruction-tuned language model by **Triplet Research**
 - Type: Causal Language Model
 - Architecture: Qwen3.5 Text
 - Layers: 32
-- Parameters: ~12B
 ## Quickstart

 # Suzhou 3.2
+A 12 billion parameter instruction-tuned language model by **Triplet Research**. Suzhou 3.2 is a weighted merge of Suzhou 3.1 and Qwen2.5-3B, designed to improve reasoning and math capabilities.
 ## Merge Details
 - **Method**: Weighted blending (70% Suzhou 3.1 + 30% Qwen2.5-3B)
+- **Model A**: Suzhou 3.1 - strong agent/tool-use, reasoning
+- **Model B**: Qwen2.5-3B-Instruct - math reasoning, general knowledge
+- **Target**: 12B parameters
 ## Key Features
+- **12B parameters**
 - **262K context window**
 - Strong **reasoning** and **chain-of-thought** capabilities
 - **Tool calling** and **agent** support
 - Type: Causal Language Model
 - Architecture: Qwen3.5 Text
 - Layers: 32
+- Parameters: 12B
+## Safetensors
+- 12B parameters
 ## Quickstart