Image-Text-to-Text
Transformers
Safetensors
English
visionpsynano
feature-extraction
vision-language-model
nanovlm
chart-understanding
ocr
crypto
launchpad
stable-mainnet
fefer
pegd-fun
conversational
custom_code
Instructions to use feferai/FEFER-AI-460M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use feferai/FEFER-AI-460M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="feferai/FEFER-AI-460M", trust_remote_code=True) 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 AutoModel model = AutoModel.from_pretrained("feferai/FEFER-AI-460M", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use feferai/FEFER-AI-460M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "feferai/FEFER-AI-460M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "feferai/FEFER-AI-460M", "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/feferai/FEFER-AI-460M
- SGLang
How to use feferai/FEFER-AI-460M 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 "feferai/FEFER-AI-460M" \ --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": "feferai/FEFER-AI-460M", "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 "feferai/FEFER-AI-460M" \ --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": "feferai/FEFER-AI-460M", "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 feferai/FEFER-AI-460M with Docker Model Runner:
docker model run hf.co/feferai/FEFER-AI-460M
File size: 3,937 Bytes
8ce9251 | 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 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 | {
"architectures": [
"VisionPsyNanoForConditionalGeneration"
],
"auto_map": {
"AutoConfig": "configuration_visionpsynano.VisionPsyNanoConfig",
"AutoModel": "modeling_visionpsynano.VisionPsyNanoForConditionalGeneration",
"AutoModelForImageTextToText": "modeling_visionpsynano.VisionPsyNanoForConditionalGeneration",
"AutoProcessor": "processing_visionpsynano.VisionPsyNanoProcessor"
},
"compile_inference": true,
"compile_inference_mode": "default",
"cuda_graphs_cache_quantum": 128,
"eos_check_interval": 16,
"extra_token_amount": 66,
"hf_repo_name": "qvac/VisionPsy-Nano-460M",
"inference_max_img_size": null,
"is_flash": false,
"lm_attn_scaling": 1.0,
"lm_base_vocab_size": 49152,
"lm_chat_template": "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
"lm_dropout": 0.0,
"lm_hidden_dim": 960,
"lm_inter_dim": 2560,
"lm_max_length": 8192,
"lm_max_position_embeddings": 8192,
"lm_model_type": "HuggingFaceTB/SmolLM2-360M-Instruct",
"lm_n_blocks": 32,
"lm_n_heads": 15,
"lm_n_kv_heads": 5,
"lm_re_base": 100000,
"lm_rms_eps": 1e-05,
"lm_tie_weights": true,
"lm_tokenizer": "HuggingFaceTB/SmolLM2-360M-Instruct",
"lm_use_tokens": false,
"lm_vocab_size": 49218,
"max_img_size": 2048,
"model_type": "visionpsynano",
"mp_image_token_length": 64,
"mp_pixel_shuffle_factor": 4,
"resize_min_side_len": null,
"resize_to_max_side_len": true,
"transformers_version": "5.14.1",
"vit_cls_flag": false,
"vit_dropout": 0.0,
"vit_hidden_dim": 768,
"vit_img_size": 512,
"vit_inter_dim": 3072,
"vit_ln_eps": 1e-06,
"vit_model_type": "google/siglip2-base-patch16-512",
"vit_n_blocks": 12,
"vit_n_heads": 12,
"vit_patch_size": 16,
"vlm_checkpoint_path": null,
"vlm_extra_tokens": {
"global_image_token": "<|global_image|>",
"image_token": "<|image|>",
"r1c1": "<row_1_col_1>",
"r1c2": "<row_1_col_2>",
"r1c3": "<row_1_col_3>",
"r1c4": "<row_1_col_4>",
"r1c5": "<row_1_col_5>",
"r1c6": "<row_1_col_6>",
"r1c7": "<row_1_col_7>",
"r1c8": "<row_1_col_8>",
"r2c1": "<row_2_col_1>",
"r2c2": "<row_2_col_2>",
"r2c3": "<row_2_col_3>",
"r2c4": "<row_2_col_4>",
"r2c5": "<row_2_col_5>",
"r2c6": "<row_2_col_6>",
"r2c7": "<row_2_col_7>",
"r2c8": "<row_2_col_8>",
"r3c1": "<row_3_col_1>",
"r3c2": "<row_3_col_2>",
"r3c3": "<row_3_col_3>",
"r3c4": "<row_3_col_4>",
"r3c5": "<row_3_col_5>",
"r3c6": "<row_3_col_6>",
"r3c7": "<row_3_col_7>",
"r3c8": "<row_3_col_8>",
"r4c1": "<row_4_col_1>",
"r4c2": "<row_4_col_2>",
"r4c3": "<row_4_col_3>",
"r4c4": "<row_4_col_4>",
"r4c5": "<row_4_col_5>",
"r4c6": "<row_4_col_6>",
"r4c7": "<row_4_col_7>",
"r4c8": "<row_4_col_8>",
"r5c1": "<row_5_col_1>",
"r5c2": "<row_5_col_2>",
"r5c3": "<row_5_col_3>",
"r5c4": "<row_5_col_4>",
"r5c5": "<row_5_col_5>",
"r5c6": "<row_5_col_6>",
"r5c7": "<row_5_col_7>",
"r5c8": "<row_5_col_8>",
"r6c1": "<row_6_col_1>",
"r6c2": "<row_6_col_2>",
"r6c3": "<row_6_col_3>",
"r6c4": "<row_6_col_4>",
"r6c5": "<row_6_col_5>",
"r6c6": "<row_6_col_6>",
"r6c7": "<row_6_col_7>",
"r6c8": "<row_6_col_8>",
"r7c1": "<row_7_col_1>",
"r7c2": "<row_7_col_2>",
"r7c3": "<row_7_col_3>",
"r7c4": "<row_7_col_4>",
"r7c5": "<row_7_col_5>",
"r7c6": "<row_7_col_6>",
"r7c7": "<row_7_col_7>",
"r7c8": "<row_7_col_8>",
"r8c1": "<row_8_col_1>",
"r8c2": "<row_8_col_2>",
"r8c3": "<row_8_col_3>",
"r8c4": "<row_8_col_4>",
"r8c5": "<row_8_col_5>",
"r8c6": "<row_8_col_6>",
"r8c7": "<row_8_col_7>",
"r8c8": "<row_8_col_8>"
},
"vlm_load_backbone_weights": false,
"torch_dtype": "float32"
}
|