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

Libraries

How to use tcclaviger/Tess-27B-RFI with Transformers:

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

pipe = pipeline("image-text-to-text", model="tcclaviger/Tess-27B-RFI")
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("tcclaviger/Tess-27B-RFI")
model = AutoModelForMultimodalLM.from_pretrained("tcclaviger/Tess-27B-RFI")
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 tcclaviger/Tess-27B-RFI with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tcclaviger/Tess-27B-RFI"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tcclaviger/Tess-27B-RFI",
		"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/tcclaviger/Tess-27B-RFI

SGLang

How to use tcclaviger/Tess-27B-RFI 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 "tcclaviger/Tess-27B-RFI" \
    --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": "tcclaviger/Tess-27B-RFI",
		"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 "tcclaviger/Tess-27B-RFI" \
        --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": "tcclaviger/Tess-27B-RFI",
		"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 tcclaviger/Tess-27B-RFI with Docker Model Runner:
```
docker model run hf.co/tcclaviger/Tess-27B-RFI
```

Tess-27B-RFI / config.json

tcclaviger

Add model weights, config, tokenizer; 512K YaRN context note; best-in-row table bolding

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	{
	"architectures": [
	"Qwen3_5ForConditionalGeneration"
	],
	"image_token_id": 248056,
	"language_model_only": false,
	"model_type": "qwen3_5",
	"text_config": {
	"attention_bias": false,
	"attention_dropout": 0.0,
	"attn_output_gate": true,
	"bos_token_id": 248044,
	"dtype": "bfloat16",
	"eos_token_id": 248044,
	"full_attention_interval": 4,
	"head_dim": 256,
	"hidden_act": "silu",
	"hidden_size": 5120,
	"initializer_range": 0.02,
	"intermediate_size": 17408,
	"layer_types": [
	"linear_attention",
	"linear_attention",
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	"linear_key_head_dim": 128,
	"linear_num_key_heads": 16,
	"linear_num_value_heads": 48,
	"linear_value_head_dim": 128,
	"mamba_ssm_dtype": "float32",
	"max_position_embeddings": 262144,
	"model_type": "qwen3_5_text",
	"mtp_num_hidden_layers": 1,
	"mtp_use_dedicated_embeddings": false,
	"num_attention_heads": 24,
	"num_hidden_layers": 64,
	"num_key_value_heads": 4,
	"output_gate_type": "swish",
	"pad_token_id": null,
	"partial_rotary_factor": 0.25,
	"rms_norm_eps": 1e-06,
	"rope_parameters": {
	"mrope_interleaved": true,
	"mrope_section": [
	11,
	11,
	10
	],
	"partial_rotary_factor": 0.25,
	"rope_theta": 10000000,
	"rope_type": "default"
	},
	"tie_word_embeddings": false,
	"use_cache": true,
	"vocab_size": 248320
	},
	"tie_word_embeddings": false,
	"transformers_version": "4.57.1",
	"video_token_id": 248057,
	"vision_config": {
	"deepstack_visual_indexes": [],
	"depth": 27,
	"hidden_act": "gelu_pytorch_tanh",
	"hidden_size": 1152,
	"in_channels": 3,
	"initializer_range": 0.02,
	"intermediate_size": 4304,
	"model_type": "qwen3_5",
	"num_heads": 16,
	"num_position_embeddings": 2304,
	"out_hidden_size": 5120,
	"patch_size": 16,
	"spatial_merge_size": 2,
	"temporal_patch_size": 2
	},
	"vision_end_token_id": 248054,
	"vision_start_token_id": 248053,
	"quantization_config": {
	"config_groups": {
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	"output_activations": null,
	"targets": [
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	"weights": {
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	"type": "int",
	"group_size": 32,
	"scale_dtype": "torch.int8",
	"scale_exp_dtype": "torch.int8",
	"scale_encoding": "block_float_i8m_i8e",
	"symmetric": true,
	"act_dtype": "int8",
	"rotation": "hadamard32"
	}
	},
	"group_rfa": {
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	"output_activations": null,
	"targets": [
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	],
	"weights": {
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	"symmetric": false,
	"rotation": "hadamard16"
	}
	}
	},
	"format": "rfi-quantized",
	"quant_method": "rfi",
	"quantization_status": "compressed",
	"version": "rfi-composite-1.0",
	"ignore": [
	"re:(?:^\|\\.)visual\\.",
	"re:.\\.linear_attn\\..",
	"re:^lm_head",
	"re:.\\.mtp\\..",
	"re:^model\\.language_model\\.embed_tokens$"
	],
	"rfa_experts": false
	}
	}