Instructions to use hopski/graph-qwenputer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hopski/graph-qwenputer with Transformers:

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

pipe = pipeline("text-generation", model="hopski/graph-qwenputer")
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("hopski/graph-qwenputer")
model = AutoModelForCausalLM.from_pretrained("hopski/graph-qwenputer", 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 hopski/graph-qwenputer with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/hopski/graph-qwenputer

SGLang

How to use hopski/graph-qwenputer 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 "hopski/graph-qwenputer" \
    --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": "hopski/graph-qwenputer",
		"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 "hopski/graph-qwenputer" \
        --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": "hopski/graph-qwenputer",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use hopski/graph-qwenputer with Docker Model Runner:
```
docker model run hf.co/hopski/graph-qwenputer
```

hopski commited on 1 day ago

Commit

63c032a

verified ·

1 Parent(s): ea783eb

Model card: Qwen's layers keep the released weights; they are not bit-identical to the release

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -10,7 +10,7 @@ tags:
 # Graph Qwenputer: Qwen3.5-4B with a graph computer inside
-Qwen3.5-4B with 11 layers added in front of its 32. The added layers are an RV32IM CPU with Zba, compiled by [Qwenputer](https://github.com/divagr18/qwenputer), with 85 added exit units so the machine can print ordinary tokens. Nothing is trained, and Qwen's 424 tensors are bit-identical.
 With the project's firmware loaded, the model can read and write an exact graph from inside its own run: typed, directed edges, up to 1,024 nodes, with calls such as `path`, `neighbours`, `add_node` and `link`. New York's subway is the worked example; the graph can be anything.

 # Graph Qwenputer: Qwen3.5-4B with a graph computer inside
+Qwen3.5-4B with 11 layers added in front of its 32. The added layers are an RV32IM CPU with Zba, compiled by [Qwenputer](https://github.com/divagr18/qwenputer), with 85 added exit units so the machine can print ordinary tokens. Nothing is trained. Qwen's 32 layers keep Qwen's released weights: 360 of their 424 tensors match the Qwen3.5-4B release bit for bit in Qwen's own dimensions, and the other 64 are normalization gains that Qwenputer rescales for the wider model. Qwenputer also adds columns that are designed to keep Qwen's layers quiet on the computer's tokens.
 With the project's firmware loaded, the model can read and write an exact graph from inside its own run: typed, directed edges, up to 1,024 nodes, with calls such as `path`, `neighbours`, `add_node` and `link`. New York's subway is the worked example; the graph can be anything.