Instructions to use rodrigoramosrs/veriloop-coder-e2-nvfp4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rodrigoramosrs/veriloop-coder-e2-nvfp4 with Transformers:

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

pipe = pipeline("text-generation", model="rodrigoramosrs/veriloop-coder-e2-nvfp4")
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("rodrigoramosrs/veriloop-coder-e2-nvfp4")
model = AutoModelForCausalLM.from_pretrained("rodrigoramosrs/veriloop-coder-e2-nvfp4", 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 rodrigoramosrs/veriloop-coder-e2-nvfp4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rodrigoramosrs/veriloop-coder-e2-nvfp4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rodrigoramosrs/veriloop-coder-e2-nvfp4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rodrigoramosrs/veriloop-coder-e2-nvfp4

SGLang

How to use rodrigoramosrs/veriloop-coder-e2-nvfp4 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 "rodrigoramosrs/veriloop-coder-e2-nvfp4" \
    --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": "rodrigoramosrs/veriloop-coder-e2-nvfp4",
		"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 "rodrigoramosrs/veriloop-coder-e2-nvfp4" \
        --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": "rodrigoramosrs/veriloop-coder-e2-nvfp4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rodrigoramosrs/veriloop-coder-e2-nvfp4 with Docker Model Runner:
```
docker model run hf.co/rodrigoramosrs/veriloop-coder-e2-nvfp4
```

rodrigoramosrs commited on Sep 26

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@@ -127,6 +127,11 @@ python scripts/validate_nvfp4.py ./model-nvfp4
 On Windows the Triton JIT needs a C compiler: run from a Visual Studio Native Tools prompt (`vcvars64`) with `CC` pointing at `cl.exe`. See the header of `scripts/quantize_veriloop.py` for multi-GPU layout tuning (`--layers-split`, `--gpu-order`).
 ## Acknowledgements
 - **Libo Wang** and the **Intelligent Robotics Laboratory, Tsinghua SIGS** for developing the original VeriLoop E2 model.

 On Windows the Triton JIT needs a C compiler: run from a Visual Studio Native Tools prompt (`vcvars64`) with `CC` pointing at `cl.exe`. See the header of `scripts/quantize_veriloop.py` for multi-GPU layout tuning (`--layers-split`, `--gpu-order`).
+## Runtime notes
+- **Verified working:** FreeToken (NVFP4, Blackwell) — end-to-end generation confirmed.
+- **Known limitation (SGLang ≤ 0.5.20):** serving this checkpoint yields degenerate output (constant-token repetition). Bisection points at SGLang's Gated-Delta-Net path for the dense-27B head layout (48 value heads / 16 key heads, ratio 3): the same SGLang build serves Llama-3.1-8B-NVFP4 and Qwen3.5-9B (ratio 2, BF16 and NVFP4-MLP) correctly, across PP/TP layouts, KV dtypes, attention/sampling/GEMM backends and with the SiLU fusion disabled. Affects any dense-27B Qwen3.5 regardless of which blocks are quantized. vLLM / TensorRT-LLM untested with this checkpoint.
 ## Acknowledgements
 - **Libo Wang** and the **Intelligent Robotics Laboratory, Tsinghua SIGS** for developing the original VeriLoop E2 model.