Image-Text-to-Text
Transformers
ONNX
Transformers.js
lfm2_vl
lfm2
vision
multimodal
food
calories
nutrition
conversational
Instructions to use opencal/opencal-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use opencal/opencal-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="opencal/opencal-base") 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("opencal/opencal-base") model = AutoModelForMultimodalLM.from_pretrained("opencal/opencal-base", 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]:])) - Transformers.js
How to use opencal/opencal-base with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('image-text-to-text', 'opencal/opencal-base'); - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use opencal/opencal-base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "opencal/opencal-base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "opencal/opencal-base", "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/opencal/opencal-base
- SGLang
How to use opencal/opencal-base 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 "opencal/opencal-base" \ --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": "opencal/opencal-base", "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 "opencal/opencal-base" \ --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": "opencal/opencal-base", "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 opencal/opencal-base with Docker Model Runner:
docker model run hf.co/opencal/opencal-base
File size: 3,836 Bytes
4ecaaf8 | 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 | {{- bos_token -}}
{%- set keep_past_thinking = keep_past_thinking | default(false) -%}
{%- macro format_arg_value(arg_value) -%}
{%- if arg_value is string -%}
{{- '"' + arg_value + '"' -}}
{%- elif arg_value is mapping -%}
{{- arg_value | tojson -}}
{%- else -%}
{{- arg_value | string -}}
{%- endif -%}
{%- endmacro -%}
{%- macro parse_content(content) -%}
{%- if content is string -%}
{{- content -}}
{%- else -%}
{%- set _ns = namespace(result="") -%}
{%- for item in content -%}
{%- if item.type == "image" -%}
{%- set _ns.result = _ns.result + "<image>" -%}
{%- elif item.type == "text" -%}
{%- set _ns.result = _ns.result + item.text -%}
{%- else -%}
{%- set _ns.result = _ns.result + item | tojson -%}
{%- endif -%}
{%- endfor -%}
{{- _ns.result -}}
{%- endif -%}
{%- endmacro -%}
{%- macro render_tool_calls(tool_calls) -%}
{%- set tool_calls_ns = namespace(tool_calls=[]) -%}
{%- for tool_call in tool_calls -%}
{%- set func_name = tool_call.function.name -%}
{%- set func_args = tool_call.function.arguments -%}
{%- set args_ns = namespace(arg_strings=[]) -%}
{%- for arg_name, arg_value in func_args.items() -%}
{%- set args_ns.arg_strings = args_ns.arg_strings + [arg_name + "=" + format_arg_value(arg_value)] -%}
{%- endfor -%}
{%- set tool_calls_ns.tool_calls = tool_calls_ns.tool_calls + [func_name + "(" + (args_ns.arg_strings | join(", ")) + ")"] -%}
{%- endfor -%}
{{- "<|tool_call_start|>[" + (tool_calls_ns.tool_calls | join(", ")) + "]<|tool_call_end|>" -}}
{%- endmacro -%}
{%- set ns = namespace(system_prompt="", last_assistant_index=-1) -%}
{%- if messages[0].role == "system" -%}
{%- if messages[0].content is defined -%}
{%- set ns.system_prompt = parse_content(messages[0].content) -%}
{%- endif -%}
{%- set messages = messages[1:] -%}
{%- endif -%}
{%- if tools -%}
{%- set ns.system_prompt = ns.system_prompt + ("\n\n" if ns.system_prompt else "") + "Today's date: " + strftime_now("%Y-%m-%d") + "\n\nList of tools: " + (tools | tojson) -%}
{%- endif -%}
{%- if ns.system_prompt -%}
{{- "<|im_start|>system\n" + ns.system_prompt + "<|im_end|>\n" -}}
{%- endif -%}
{%- for message in messages -%}
{%- if message.role == "assistant" -%}
{%- set ns.last_assistant_index = loop.index0 -%}
{%- endif -%}
{%- endfor -%}
{%- for message in messages -%}
{{- "<|im_start|>" + message.role + "\n" -}}
{%- if message.role == "assistant" -%}
{%- generation -%}
{%- if message.thinking is defined and (keep_past_thinking or loop.index0 == ns.last_assistant_index) -%}
{{- "<think>" + message.thinking + "</think>" -}}
{%- endif -%}
{%- if message.tool_calls is defined -%}
{{- render_tool_calls(message.tool_calls) -}}
{%- endif -%}
{%- if message.content is defined -%}
{%- set content = parse_content(message.content) -%}
{%- if not keep_past_thinking and loop.index0 != ns.last_assistant_index -%}
{%- if "</think>" in content -%}
{%- set content = content.split("</think>")[-1] | trim -%}
{%- endif -%}
{%- endif -%}
{{- content + ("" if (continue_final_message and loop.last) else "<|im_end|>\n") -}}
{%- endif -%}
{%- endgeneration -%}
{%- else %}
{%- if message.content is defined -%}
{{- parse_content(message.content) + "<|im_end|>\n" -}}
{%- endif -%}
{%- endif %}
{%- endfor -%}
{%- if add_generation_prompt -%}
{{- "<|im_start|>assistant\n" -}}
{%- endif -%} |