Instructions to use migtissera/Tess-4-27B-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use migtissera/Tess-4-27B-NVFP4 with Transformers:

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

pipe = pipeline("image-text-to-text", model="migtissera/Tess-4-27B-NVFP4")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("migtissera/Tess-4-27B-NVFP4")
model = AutoModelForMultimodalLM.from_pretrained("migtissera/Tess-4-27B-NVFP4")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use migtissera/Tess-4-27B-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "migtissera/Tess-4-27B-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": "migtissera/Tess-4-27B-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/migtissera/Tess-4-27B-NVFP4

SGLang

How to use migtissera/Tess-4-27B-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 "migtissera/Tess-4-27B-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": "migtissera/Tess-4-27B-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "migtissera/Tess-4-27B-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": "migtissera/Tess-4-27B-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use migtissera/Tess-4-27B-NVFP4 with Docker Model Runner:
```
docker model run hf.co/migtissera/Tess-4-27B-NVFP4
```

Tess-4-27B · NVFP4

NVFP4 (4-bit floating point) quantization of migtissera/Tess-4-27B — 19 GB (vs 52 GB BF16, −63%). Blackwell-native W4A4 for maximum speed on RTX 50-series / B200; runs on Hopper via vLLM fallback.

Produced with llm-compressor (scheme=NVFP4, group 16, FP8 block scales), calibrated on 512 of Tess-4's own on-policy generations (reasoning + coding + tool-call traces) rather than generic text. Vision tower, lm_head, and MTP block kept in BF16.

Quality check

Verified via vLLM (greedy): factual recall, multi-step reasoning (correct algebra on classic trap questions), and code generation with Tess-4's characteristic verification-style thinking all intact — including weight-scaled reasoning (empty think block on trivial questions).

Usage (vLLM)

vllm serve migtissera/Tess-4-27B-NVFP4 --trust-remote-code

On hybrid-arch builds you may need --max-num-seqs 64 (mamba cache blocks). For agentic/tool use add: --enable-auto-tool-choice --tool-call-parser hermes --reasoning-parser qwen3.

Pro tip: pair with the EAGLE-3 draft for compound speedup — quantization and speculative decoding stack.

License

Apache 2.0, matching the base model. Part of the Tess series by Migel Tissera.

Downloads last month: 131

Safetensors

Model size

17B params

Tensor type

F32

BF16

F8_E4M3

Model tree for migtissera/Tess-4-27B-NVFP4

Base model

Qwen/Qwen3.6-27B

Finetuned

migtissera/Tess-4-27B

Quantized

(30)

this model