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
Download config.json from MosRat/mixtex_gex: direct link, hf CLI and curl.
- Browser
- Download file 2.14 kB
-
https://huggingface.co/MosRat/mixtex_gex/resolve/main/config.json
- Command line
-
hf download hf://MosRat/mixtex_gex/config.json
-
curl -L -o config.json https://huggingface.co/MosRat/mixtex_gex/resolve/main/config.json
2.14 kB
| { | |
| "architectures": [ | |
| "VisionEncoderDecoderModel" | |
| ], | |
| "decoder": { | |
| "_name_or_path": "gpt2_base", | |
| "activation_function": "gelu_new", | |
| "add_cross_attention": true, | |
| "architectures": [ | |
| "GPT2LMHeadModel" | |
| ], | |
| "attn_pdrop": 0.1, | |
| "embd_pdrop": 0.1, | |
| "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": { | |
| "_name_or_path": "encoder", | |
| "architectures": [ | |
| "SwinModel" | |
| ], | |
| "attention_probs_dropout_prob": 0.0, | |
| "depths": [ | |
| 2, | |
| 2, | |
| 18, | |
| 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": [ | |
| 3, | |
| 6, | |
| 12, | |
| 24 | |
| ], | |
| "num_layers": 4, | |
| "out_features": [ | |
| "stage4" | |
| ], | |
| "out_indices": [ | |
| 4 | |
| ], | |
| "patch_size": 4, | |
| "path_norm": true, | |
| "qkv_bias": true, | |
| "stage_names": [ | |
| "stem", | |
| "stage1", | |
| "stage2", | |
| "stage3", | |
| "stage4" | |
| ], | |
| "torch_dtype": "bfloat16", | |
| "use_absolute_embeddings": false, | |
| "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 | |
| } | |