Instructions to use Portx/trial with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Portx/trial with Transformers:

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

pipe = pipeline("image-text-to-text", model="Portx/trial")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Portx/trial")
model = AutoModelForMultimodalLM.from_pretrained("Portx/trial", device_map="auto")
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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Portx/trial with vLLM:

Install from pip and serve model

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

SGLang

How to use Portx/trial 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 "Portx/trial" \
    --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": "Portx/trial",
		"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 "Portx/trial" \
        --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": "Portx/trial",
		"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"
						}
					}
				]
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use Portx/trial with Docker Model Runner:
```
docker model run hf.co/Portx/trial
```

trial / config.json

Portx

(Trained with Unsloth)

25bad97 verified about 1 year ago

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3.46 kB

	{
	"architectures": [
	"MllamaForConditionalGeneration"
	],
	"image_token_index": 128256,
	"model_type": "mllama",
	"pad_token_id": 128004,
	"quantization_config": {
	"bnb_4bit_compute_dtype": "bfloat16",
	"bnb_4bit_quant_type": "nf4",
	"bnb_4bit_use_double_quant": true,
	"llm_int8_enable_fp32_cpu_offload": false,
	"llm_int8_has_fp16_weight": false,
	"llm_int8_skip_modules": [
	"model.vision_model.transformer.layers.13.mlp.fc2",
	"model.language_model.layers.1.self_attn.q_proj",
	"model.language_model.layers.1.self_attn.k_proj",
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	"model.multi_modal_projector",
	"lm_head"
	],
	"llm_int8_threshold": 6.0,
	"load_in_4bit": true,
	"load_in_8bit": false,
	"quant_method": "bitsandbytes"
	},
	"text_config": {
	"_attn_implementation_autoset": false,
	"cross_attention_layers": [
	3,
	8,
	13,
	18,
	23,
	28,
	33,
	38
	],
	"dropout": 0,
	"eos_token_id": [
	128001,
	128008,
	128009
	],
	"hidden_act": "silu",
	"hidden_size": 4096,
	"initializer_range": 0.02,
	"intermediate_size": 14336,
	"max_position_embeddings": 131072,
	"model_type": "mllama_text_model",
	"num_attention_heads": 32,
	"num_hidden_layers": 40,
	"num_key_value_heads": 8,
	"rms_norm_eps": 1e-05,
	"rope_scaling": {
	"factor": 8.0,
	"high_freq_factor": 4.0,
	"low_freq_factor": 1.0,
	"original_max_position_embeddings": 8192,
	"rope_type": "llama3"
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	"rope_theta": 500000.0,
	"torch_dtype": "bfloat16",
	"use_cache": true,
	"vocab_size": 128256
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	"torch_dtype": "bfloat16",
	"transformers_version": "4.55.4",
	"unsloth_fixed": true,
	"unsloth_version": "2025.9.7",
	"vision_config": {
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	"attention_heads": 16,
	"hidden_act": "gelu",
	"hidden_size": 1280,
	"image_size": 560,
	"initializer_range": 0.02,
	"intermediate_layers_indices": [
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	"intermediate_size": 5120,
	"max_num_tiles": 4,
	"model_type": "mllama_vision_model",
	"norm_eps": 1e-05,
	"num_channels": 3,
	"num_global_layers": 8,
	"num_hidden_layers": 32,
	"patch_size": 14,
	"supported_aspect_ratios": [
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	[
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	[
	1,
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	[
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	"torch_dtype": "bfloat16",
	"vision_output_dim": 7680
	}
	}