Instructions to use onnx-internal-testing/tiny-random-Mistral4ForCausalLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use onnx-internal-testing/tiny-random-Mistral4ForCausalLM with Transformers:

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

pipe = pipeline("image-text-to-text", model="onnx-internal-testing/tiny-random-Mistral4ForCausalLM")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("onnx-internal-testing/tiny-random-Mistral4ForCausalLM")
model = AutoModelForMultimodalLM.from_pretrained("onnx-internal-testing/tiny-random-Mistral4ForCausalLM")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use onnx-internal-testing/tiny-random-Mistral4ForCausalLM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "onnx-internal-testing/tiny-random-Mistral4ForCausalLM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "onnx-internal-testing/tiny-random-Mistral4ForCausalLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/onnx-internal-testing/tiny-random-Mistral4ForCausalLM

SGLang

How to use onnx-internal-testing/tiny-random-Mistral4ForCausalLM 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 "onnx-internal-testing/tiny-random-Mistral4ForCausalLM" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "onnx-internal-testing/tiny-random-Mistral4ForCausalLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "onnx-internal-testing/tiny-random-Mistral4ForCausalLM" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "onnx-internal-testing/tiny-random-Mistral4ForCausalLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use onnx-internal-testing/tiny-random-Mistral4ForCausalLM with Docker Model Runner:
```
docker model run hf.co/onnx-internal-testing/tiny-random-Mistral4ForCausalLM
```

tiny-random-Mistral4ForCausalLM / config.json

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Upload optimized ONNX model (#1)

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	{
	"architectures": [
	"Mistral4ForCausalLM"
	],
	"attention_bias": false,
	"attention_dropout": 0.0,
	"bos_token_id": 1,
	"dtype": "float32",
	"eos_token_id": 2,
	"first_k_dense_replace": 1,
	"head_dim": 24,
	"hidden_act": "silu",
	"hidden_size": 64,
	"initializer_range": 0.02,
	"intermediate_size": 32,
	"kv_lora_rank": 16,
	"max_position_embeddings": 1048576,
	"model_type": "mistral4",
	"moe_intermediate_size": 16,
	"n_group": 2,
	"n_routed_experts": 4,
	"n_shared_experts": 1,
	"norm_topk_prob": true,
	"num_attention_heads": 4,
	"num_experts_per_tok": 2,
	"num_hidden_layers": 3,
	"num_key_value_heads": 4,
	"pad_token_id": 11,
	"pretraining_tp": 1,
	"q_lora_rank": 32,
	"qk_head_dim": 24,
	"qk_nope_head_dim": 16,
	"qk_rope_head_dim": 8,
	"rms_norm_eps": 1e-06,
	"rope_interleave": true,
	"rope_parameters": {
	"beta_fast": 32.0,
	"beta_slow": 1.0,
	"factor": 128.0,
	"llama_4_scaling_beta": 0.1,
	"max_position_embeddings": 1048576,
	"mscale": 1.0,
	"mscale_all_dim": 1.0,
	"original_max_position_embeddings": 8192,
	"partial_rotary_factor": 0.3333333333333333,
	"rope_theta": 10000.0,
	"rope_type": "yarn",
	"type": "yarn"
	},
	"routed_scaling_factor": 1.0,
	"tie_word_embeddings": false,
	"topk_group": 1,
	"transformers_version": "5.3.0.dev0",
	"use_cache": true,
	"v_head_dim": 16,
	"vocab_size": 32000,
	"transformers.js_config": {
	"use_external_data_format": {
	"model.onnx": 1,
	"model_fp16.onnx": 1
	},
	"kv_cache_dtype": {
	"q4f16": "float16",
	"fp16": "float16"
	}
	}
	}