Instructions to use MosRat/mixtex_gex with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MosRat/mixtex_gex with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="MosRat/mixtex_gex")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("MosRat/mixtex_gex") model = AutoModelForMultimodalLM.from_pretrained("MosRat/mixtex_gex", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MosRat/mixtex_gex with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MosRat/mixtex_gex" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MosRat/mixtex_gex", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MosRat/mixtex_gex
- SGLang
How to use MosRat/mixtex_gex 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 "MosRat/mixtex_gex" \ --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": "MosRat/mixtex_gex", "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 "MosRat/mixtex_gex" \ --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": "MosRat/mixtex_gex", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MosRat/mixtex_gex with Docker Model Runner:
docker model run hf.co/MosRat/mixtex_gex
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"architectures": [
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],
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"initializer_range": 0.02,
"is_decoder": true,
"layer_norm_epsilon": 1e-05,
"model_type": "gpt2",
"n_embd": 768,
"n_head": 12,
"n_inner": null,
"n_layer": 6,
"n_positions": 1024,
"reorder_and_upcast_attn": false,
"resid_pdrop": 0.1,
"scale_attn_by_inverse_layer_idx": false,
"scale_attn_weights": true,
"summary_activation": null,
"summary_first_dropout": 0.1,
"summary_proj_to_labels": true,
"summary_type": "cls_index",
"summary_use_proj": true,
"torch_dtype": "bfloat16",
"type_vocab_size": 1,
"use_cache": true,
"vocab_size": 30002
},
"decoder_start_token_id": 0,
"encoder": {
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"architectures": [
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"depths": [
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2,
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2
],
"drop_path_rate": 0.1,
"embed_dim": 96,
"encoder_stride": 32,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 768,
"image_size": 224,
"initializer_range": 0.02,
"layer_norm_eps": 1e-05,
"mlp_ratio": 4.0,
"model_type": "swin",
"num_channels": 3,
"num_heads": [
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24
],
"num_layers": 4,
"out_features": [
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],
"patch_size": 4,
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"stage_names": [
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"stage3",
"stage4"
],
"torch_dtype": "bfloat16",
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"window_size": 7
},
"eos_token_id": 30000,
"is_encoder_decoder": true,
"max_length": null,
"model_type": "vision-encoder-decoder",
"pad_token_id": 1,
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "4.52.4",
"vocab_size": 30002
}
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