Image-Text-to-Text
Transformers
Safetensors
English
Chinese
qwen3_5_moe
code
agent
agentic-coding
Mixture of Experts
vision-language
nvfp4
fp4
quantized
blackwell
vllm
conversational
8-bit precision
compressed-tensors
Instructions to use beyoru/KAT-Coder-V2.5-Dev-VL-Flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use beyoru/KAT-Coder-V2.5-Dev-VL-Flash with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="beyoru/KAT-Coder-V2.5-Dev-VL-Flash") 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 AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("beyoru/KAT-Coder-V2.5-Dev-VL-Flash") model = AutoModelForMultimodalLM.from_pretrained("beyoru/KAT-Coder-V2.5-Dev-VL-Flash", device_map="auto") 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?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use beyoru/KAT-Coder-V2.5-Dev-VL-Flash with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "beyoru/KAT-Coder-V2.5-Dev-VL-Flash" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "beyoru/KAT-Coder-V2.5-Dev-VL-Flash", "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/beyoru/KAT-Coder-V2.5-Dev-VL-Flash
- SGLang
How to use beyoru/KAT-Coder-V2.5-Dev-VL-Flash 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 "beyoru/KAT-Coder-V2.5-Dev-VL-Flash" \ --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": "beyoru/KAT-Coder-V2.5-Dev-VL-Flash", "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 "beyoru/KAT-Coder-V2.5-Dev-VL-Flash" \ --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": "beyoru/KAT-Coder-V2.5-Dev-VL-Flash", "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 beyoru/KAT-Coder-V2.5-Dev-VL-Flash with Docker Model Runner:
docker model run hf.co/beyoru/KAT-Coder-V2.5-Dev-VL-Flash
File size: 2,544 Bytes
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default_modifiers:
QuantizationModifier:
config_groups:
group_0:
targets: ['re:.*self_attn\.(q|k|v|o)_proj$', 're:.*linear_attn\.(in_proj_qkv|in_proj_z|out_proj)$',
're:.*lm_head']
weights:
num_bits: 8
type: float
symmetric: true
group_size: null
strategy: channel
block_structure: null
dynamic: false
actorder: null
scale_dtype: null
zp_dtype: null
observer: memoryless_minmax
observer_kwargs: {}
input_activations:
num_bits: 8
type: float
symmetric: true
group_size: null
strategy: token
block_structure: null
dynamic: true
actorder: null
scale_dtype: null
zp_dtype: null
observer: null
observer_kwargs: {}
output_activations: null
format: null
group_1:
targets: ['re:.*mlp\.experts\.\d+\.(gate|up|down)_proj$', 're:.*shared_expert\.(gate|up|down)_proj$']
weights:
num_bits: 4
type: float
symmetric: true
group_size: 16
strategy: tensor_group
block_structure: null
dynamic: false
actorder: null
scale_dtype: torch.float8_e4m3fn
zp_dtype: null
observer: memoryless_minmax
observer_kwargs: {}
input_activations:
num_bits: 4
type: float
symmetric: true
group_size: 16
strategy: tensor_group
block_structure: null
dynamic: local
actorder: null
scale_dtype: torch.float8_e4m3fn
zp_dtype: null
observer: static_minmax
observer_kwargs: {}
output_activations: null
format: null
targets: [Linear]
ignore: ['re:.*\.mlp\.gate$', 're:.*\.shared_expert_gate$', 're:.*\.linear_attn\.(in_proj_a|in_proj_b|norm)$',
're:.*visual.*', 're:^mtp.*']
kv_cache_scheme:
num_bits: 8
type: float
symmetric: true
group_size: null
strategy: tensor
block_structure: null
dynamic: false
actorder: null
scale_dtype: null
zp_dtype: null
observer: static_minmax
observer_kwargs: {}
bypass_divisibility_checks: false
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