Image-Text-to-Text
Transformers
PyTorch
English
mobilevlm
text-generation
multimodal
mllm
knowledge-distillation
mobilellama
Instructions to use jsun39/Cosine-Beta-KD-Task with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jsun39/Cosine-Beta-KD-Task with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="jsun39/Cosine-Beta-KD-Task")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("jsun39/Cosine-Beta-KD-Task", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use jsun39/Cosine-Beta-KD-Task with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jsun39/Cosine-Beta-KD-Task" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jsun39/Cosine-Beta-KD-Task", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/jsun39/Cosine-Beta-KD-Task
- SGLang
How to use jsun39/Cosine-Beta-KD-Task 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 "jsun39/Cosine-Beta-KD-Task" \ --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": "jsun39/Cosine-Beta-KD-Task", "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 "jsun39/Cosine-Beta-KD-Task" \ --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": "jsun39/Cosine-Beta-KD-Task", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use jsun39/Cosine-Beta-KD-Task with Docker Model Runner:
docker model run hf.co/jsun39/Cosine-Beta-KD-Task
File size: 1,170 Bytes
5407462 | 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 | {
"_name_or_path": "outputs/mobilevlm_v2-1.pretrain-1246k",
"architectures": [
"MobileLlamaForCausalLM"
],
"bos_token_id": 1,
"eos_token_id": 2,
"freeze_mm_mlp_adapter": false,
"hidden_act": "silu",
"hidden_size": 2048,
"image_aspect_ratio": "pad",
"image_grid_pinpoints": null,
"initializer_range": 0.02,
"intermediate_size": 5632,
"max_position_embeddings": 2048,
"max_sequence_length": 2048,
"mm_hidden_size": 1024,
"mm_projector_lr": null,
"mm_projector_type": "ldpnetv2",
"mm_use_im_patch_token": false,
"mm_use_im_start_end": false,
"mm_vision_select_feature": "patch",
"mm_vision_select_layer": -2,
"mm_vision_tower": "openai/clip-vit-large-patch14-336",
"model_type": "mobilevlm",
"num_attention_heads": 16,
"num_hidden_layers": 24,
"num_key_value_heads": 16,
"pad_token_id": 0,
"pretraining_tp": 1,
"rms_norm_eps": 1e-06,
"rope_scaling": null,
"rope_theta": 10000.0,
"tie_word_embeddings": false,
"torch_dtype": "float16",
"transformers_version": "4.31.0",
"tune_mm_mlp_adapter": false,
"use_cache": false,
"use_mm_proj": true,
"vision_tower_type": "clip",
"vocab_size": 32000
}
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