Instructions to use InterpreticAI/Qwenputer-4B-Uncensored-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use InterpreticAI/Qwenputer-4B-Uncensored-v2 with Transformers:

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

pipe = pipeline("text-generation", model="InterpreticAI/Qwenputer-4B-Uncensored-v2")
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("InterpreticAI/Qwenputer-4B-Uncensored-v2")
model = AutoModelForCausalLM.from_pretrained("InterpreticAI/Qwenputer-4B-Uncensored-v2", 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 InterpreticAI/Qwenputer-4B-Uncensored-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "InterpreticAI/Qwenputer-4B-Uncensored-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "InterpreticAI/Qwenputer-4B-Uncensored-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/InterpreticAI/Qwenputer-4B-Uncensored-v2

SGLang

How to use InterpreticAI/Qwenputer-4B-Uncensored-v2 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 "InterpreticAI/Qwenputer-4B-Uncensored-v2" \
    --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": "InterpreticAI/Qwenputer-4B-Uncensored-v2",
		"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 "InterpreticAI/Qwenputer-4B-Uncensored-v2" \
        --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": "InterpreticAI/Qwenputer-4B-Uncensored-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use InterpreticAI/Qwenputer-4B-Uncensored-v2 with Docker Model Runner:
```
docker model run hf.co/InterpreticAI/Qwenputer-4B-Uncensored-v2
```

Qwenputer-4B-Uncensored-v2

Qwenputer-4B-Uncensored-v2 adds the Qwenputer RISC-V computer to Qwen3.5-4B-Uncensored-v2. The computer is a 32-bit RISC-V CPU compiled directly into the weights by the Qwenputer compiler of Divyansh Agrawal. It sits in 11 layers in front of the language model and computes exact arithmetic inside the forward pass. The language model's own weights are unchanged, so its behavior on political questions is the same as Qwen3.5-4B-Uncensored-v2.

We built this checkpoint with the unmodified Qwenputer merge tool (commit bec1a0c9) applied to Qwen3.5-4B-Uncensored-v2 (commit f16581e2).

Model details


Language model	Qwen3.5-4B-Uncensored-v2, weights unchanged
Computer	11 compiled layers before the 32 language-model layers; RV32IM + Zba + bignum coprocessor
Architecture	stock `Qwen3_5ForCausalLM` (text only)
Hidden size	3,968 (2,560 for the language model plus the computer's dimensions)
Vocabulary	478,656 (the base vocabulary plus 230,324 trace tokens)
Size	18.7 GB in BF16, mostly zero padding

Evaluation

All tests use greedy decoding.

1. Exact arithmetic

40 synthetic integer arithmetic problems from the Qwenputer evaluation harness. With the computer, the model writes a calc tool call and the computer layers produce the result inside the forward pass. No external tool runs.

Model	Correct (of 40)
Qwen3.5-4B-Uncensored-v2 (no tools, instructed to compute exactly)	29
Qwenputer-4B-Uncensored-v2	40

The computer's output matched a reference model of the machine at every position (0 lockstep errors, 0 head mismatches). The three built-in chat checks of the Qwenputer repository also pass.

2. Text behavior

We compare next-token distributions of Qwenputer-4B-Uncensored-v2 with those of Qwen3.5-4B-Uncensored-v2 on the Qwenputer text-fidelity prompts. As a noise floor, we compare Qwen3.5-4B-Uncensored-v2 with itself under a different attention implementation.

Comparison	Mean KL divergence
Qwenputer-4B-Uncensored-v2 vs. Qwen3.5-4B-Uncensored-v2	5.2 × 10⁻⁴
Noise floor (same model, different attention kernel)	5.0 × 10⁻⁴

3. Political questions (Pan & Xu, 2026)

We generated answers from both models to the 78 politically sensitive questions of Pan and Xu (2026) whose subject Qwen3.5-4B identifies correctly, in non-thinking mode with up to 512 tokens. The two models' answers first differ at a median of token 52. The same model run under a different attention kernel first differs at a median of token 49. The differences are therefore within BF16 numerical noise. We did not re-run the censorship judge, and the censorship results reported for Qwen3.5-4B-Uncensored-v2 apply to this model.

Usage

The computer runs only under the Qwenputer chat driver, which loads its firmware into the model state after the prompt:

git clone https://github.com/divagr18/qwenputer && cd qwenputer/experimental/riscv && uv sync --extra hf
hf download InterpreticAI/Qwenputer-4B-Uncensored-v2 --local-dir qwenputer-4b-uncensored-v2
uv run python -m chipformer.rv.chat qwenputer-4b-uncensored-v2 --firmware tests/data/rv/calc-bridge.elf \
    --ask "Use calc: what is 123456789 * 987654321?"

Loaded as a plain Qwen3_5ForCausalLM without the driver, the model behaves as Qwen3.5-4B-Uncensored-v2 in text.

Limitations

Text only. The vision encoder of Qwen3.5-4B-Uncensored-v2 is not included.
The limitations of Qwen3.5-4B-Uncensored-v2 apply, including its higher rate of fabricated facts than Qwen3.5-4B. A substantive answer is not necessarily a correct one.
The political evaluation covers Chinese-language questions only.

License

Apache 2.0, inherited from Qwen/Qwen3.5-4B and Qwenputer. The Qwenputer code is MIT-licensed (Copyright (c) 2026 Divyansh Agrawal). The attribution of the Qwen team and of the Qwenputer author is retained.

References

Agrawal, D. (2026). Qwenputer: a computer compiled into the weights of a language model (Version 0.2.0) [Computer software]. https://github.com/divagr18/qwenputer

Pan, J., & Xu, X. (2026). Political censorship in large language models originating from China. PNAS Nexus, 5(2), pgag013. https://doi.org/10.1093/pnasnexus/pgag013