Instructions to use hf-internal-testing/tiny-mistral3-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-mistral3-diffusers with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="hf-internal-testing/tiny-mistral3-diffusers") 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("hf-internal-testing/tiny-mistral3-diffusers") model = AutoModelForMultimodalLM.from_pretrained("hf-internal-testing/tiny-mistral3-diffusers", 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=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use hf-internal-testing/tiny-mistral3-diffusers with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "hf-internal-testing/tiny-mistral3-diffusers" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "hf-internal-testing/tiny-mistral3-diffusers", "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/hf-internal-testing/tiny-mistral3-diffusers
- SGLang
How to use hf-internal-testing/tiny-mistral3-diffusers 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 "hf-internal-testing/tiny-mistral3-diffusers" \ --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": "hf-internal-testing/tiny-mistral3-diffusers", "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 "hf-internal-testing/tiny-mistral3-diffusers" \ --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": "hf-internal-testing/tiny-mistral3-diffusers", "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 hf-internal-testing/tiny-mistral3-diffusers with Docker Model Runner:
docker model run hf.co/hf-internal-testing/tiny-mistral3-diffusers
File size: 1,330 Bytes
c5ae680 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | {
"architectures": [
"Mistral3ForConditionalGeneration"
],
"bos_token_id": 2,
"dtype": "float32",
"eos_token_id": 3,
"image_seq_length": 4,
"image_token_index": 1,
"model_dtype": "mistral3",
"model_type": "mistral3",
"multimodal_projector_bias": false,
"pad_token_id": 4,
"projector_hidden_act": "gelu",
"spatial_merge_size": 2,
"text_config": {
"attention_dropout": 0.0,
"bos_token_id": 2,
"eos_token_id": 3,
"head_dim": null,
"hidden_act": "silu",
"hidden_size": 16,
"initializer_range": 0.02,
"intermediate_size": 37,
"max_position_embeddings": 512,
"model_type": "mistral",
"num_attention_heads": 4,
"num_hidden_layers": 1,
"num_key_value_heads": 2,
"pad_token_id": 4,
"rms_norm_eps": 1e-05,
"rope_theta": 1000000000.0,
"sliding_window": null,
"use_cache": true,
"vocab_size": 32000
},
"transformers_version": "4.57.1",
"vision_config": {
"attention_dropout": 0.0,
"head_dim": 4,
"hidden_act": "gelu",
"hidden_size": 16,
"image_size": 30,
"initializer_range": 0.02,
"intermediate_size": 37,
"model_type": "pixtral",
"num_attention_heads": 4,
"num_channels": 3,
"num_hidden_layers": 1,
"patch_size": 6,
"rope_theta": 10000.0
},
"vision_feature_layer": -1
}
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