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

Libraries

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

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

pipe = pipeline("text-generation", model="InterpreticAI/Qwenputer-4B-Uncensored-lite-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-lite-v2")
model = AutoModelForCausalLM.from_pretrained("InterpreticAI/Qwenputer-4B-Uncensored-lite-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-lite-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-lite-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-lite-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

Qwenputer-4B-Uncensored-lite-v2

Qwenputer-4B-Uncensored-lite-v2 is a lossless compact copy of Qwenputer-4B-Uncensored-v2: the same weights, stored without the zero padding. It is 8.7 GB on disk instead of 18.7 GB and runs on a single 16 GB GPU.

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.

Size

	Qwenputer-4B-Uncensored-v2	lite-v2
Computer (11 layers)	3.85 GB	0.009 GB
Embedding (including 230,324 trace tokens)	3.80 GB	1.51 GB
Language model (32 layers)	11.07 GB	7.14 GB
Total on disk	18.7 GB	8.7 GB
GPU memory, loaded (BF16)	18.7 GB	12.8 GB

The computer adds 0.24 GB over the language model at its own width. Every one of the 580 tensors rebuilds bit for bit from this repository (SHA256SUMS.dense lists the per-tensor hashes).

Evaluation

All tests ran on one NVIDIA RTX A4000 (16 GB) with slimload.py, using the Qwenputer evaluation code unchanged. Greedy decoding throughout. The full checkpoint's results are from an NVIDIA H100.

1. Exact arithmetic

40 synthetic integer arithmetic problems from the Qwenputer evaluation harness. 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
Qwenputer-4B-Uncensored-lite-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 pass. The hidden states after the computer layers are bit-identical to those of the full checkpoint.

2. Text behavior

Next-token distributions on the Qwenputer text-fidelity prompts, compared with Qwen3.5-4B-Uncensored-v2. The noise floor compares Qwen3.5-4B-Uncensored-v2 with itself under a different attention implementation on the same GPU.

Model	Mean KL divergence	Noise floor	Ratio
Qwenputer-4B-Uncensored-v2 (H100)	5.2 × 10⁻⁴	5.0 × 10⁻⁴	1.03
Qwenputer-4B-Uncensored-lite-v2 (A4000)	1.0 × 10⁻³	9.1 × 10⁻⁴	1.10

3. Political questions (Pan & Xu, 2026)

We generated answers from Qwen3.5-4B-Uncensored-v2 and from this model 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 answers of this model and of Qwen3.5-4B-Uncensored-v2 first differ at a median of token 43.5; the same model run under two attention kernels on the same GPU first differs at a median of token 66. The differences are 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.

Comparison (78 questions)	Identical answers	Median token of first difference
Qwenputer-4B-Uncensored-v2 vs. Qwen3.5-4B-Uncensored-v2 (H100)	1	52
Noise floor: Qwen3.5-4B-Uncensored-v2, two attention kernels (H100)	0	49
lite-v2 vs. Qwen3.5-4B-Uncensored-v2 (A4000)	0	43.5
Noise floor: Qwen3.5-4B-Uncensored-v2, two attention kernels (A4000)	1	66

Usage

Run on a 16 GB GPU

git clone https://github.com/divagr18/qwenputer && cd qwenputer/experimental/riscv && uv sync --extra hf
hf download InterpreticAI/Qwenputer-4B-Uncensored-lite-v2 --local-dir qwenputer-lite

import sys; sys.path.insert(0, "qwenputer-lite")
from slimload import load_slim
model = load_slim("qwenputer-lite", device="cuda")   # a Qwen3_5ForCausalLM

To use the Qwenputer chat driver with the compact weights, make Qwen3_5ForCausalLM.from_pretrained return load_slim(path) before importing chipformer.rv.chat; the driver needs no other change.

Rebuild the full checkpoint

python qwenputer-lite/unpack.py qwenputer-lite qwenputer-4b-uncensored-v2 --verify
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?"

unpack.py needs only numpy and writes the standard 18.7 GB checkpoint, identical to Qwenputer-4B-Uncensored-v2.

Limitations

The compact files load with slimload.py or after unpack.py, not with from_pretrained directly.
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

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