Instructions to use md13/fia with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use md13/fia with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meituan-longcat/LongCat-Flash-Lite") model = PeftModel.from_pretrained(base_model, "md13/fia") - Transformers
How to use md13/fia with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="md13/fia") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("md13/fia", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use md13/fia with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "md13/fia" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "md13/fia", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/md13/fia
- SGLang
How to use md13/fia 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 "md13/fia" \ --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": "md13/fia", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "md13/fia" \ --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": "md13/fia", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Studio
How to use md13/fia with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for md13/fia to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for md13/fia to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for md13/fia to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="md13/fia", max_seq_length=2048, ) - Docker Model Runner
How to use md13/fia with Docker Model Runner:
docker model run hf.co/md13/fia
| {%- set tool_choice = tool_choice | default('auto') %} | |
| {%- set ns = namespace(tool_types = [], last_query_index = -1) %} | |
| {%- if tools and tool_choice != 'none' %} | |
| {{- "<longcat_tool_declare>\n"-}} | |
| {{- "# Tools\n" }} | |
| {{- "You have access to the following tools:\n\n" }} | |
| {%- for tool in tools %} | |
| {%- if tool.type not in ns.tool_types %} | |
| {%- set ns.tool_types = ns.tool_types + [tool.type] %} | |
| {{- "## Tool namespace: " ~ tool.type ~ "\n\n" }} | |
| {%- endif %} | |
| {%- if tool.type == 'code_interpreter' %} | |
| {%- set tool = {"type":"code_interpreter","function":{"name":"code_interpreter_preview","description":"The code will be executed in a stateful Jupyter notebook sandbox environment, only supports local computation, data processing, and file operations.\nCode sandbox environment (network isolated) Any external network requests or online API calls are prohibited.\nIf online functionality is needed, please use other permitted tools.\nCode will respond with the output of the execution or time out after 60.0 seconds. ","parameters":{"type":"object","properties":{"language":{"type":"string","description":"The programming language of the code to be executed. Available values: python (Default), java, go, js, ts, c, c++."},"code":{"type":"string","description":"Python code to be executed must not include the following:\n- Importing network libraries such as requests, httplib, etc.\n- Any form of HTTP requests.\n- External API calls.\n- Network port operations. Example: ```python\nimport pandas as pd\npd.DataFrame({'A':[1,2]})\n```"},"timeout":{"type":"number","description":"The maximum execution time of the code, in seconds. Default is 60.0."}}},"required":["code"]}} %} | |
| {%- endif %} | |
| {{- "### Tool name: " + tool.function.name + "\n" }} | |
| {{- "Description: " + tool.function.description + "\n\n" }} | |
| {{- "InputSchema: " + tool.function.parameters | tojson(ensure_ascii=False) + "\n\n" }} | |
| {%- endfor %} | |
| {{- '**Note**: For each function call, output the function name and arguments within the following XML format:\n<longcat_tool_call>{function-name}\n<longcat_arg_key>{arg-key-1}</longcat_arg_key>\n<longcat_arg_value>{arg-value-1}</longcat_arg_value>\n<longcat_arg_key>{arg-key-2}</longcat_arg_key>\n<longcat_arg_value>{arg-value-2}</longcat_arg_value>\n...\n</longcat_tool_call>\n' }} | |
| {{- "</longcat_tool_declare>"-}} | |
| {%- for idx in range(messages|length - 1) %} | |
| {%- set msg = messages[idx] %} | |
| {%- if msg.role == 'assistant' and not msg.tool_calls %} | |
| {%- set ns.last_query_index = idx %} | |
| {%- endif %} | |
| {%- endfor%} | |
| {%- endif %} | |
| {%- for msg in messages %} | |
| {%- if msg.role == "system" %} | |
| {{- "<longcat_system>" + msg.content }} | |
| {%- elif msg.role == "user" %} | |
| {{- "<longcat_user>" }} | |
| {%- if msg["files"] %} | |
| {{- '<longcat_files>\n' ~ msg.files | tojson(indent=2) ~ '\n</longcat_files>' }} | |
| {%- endif %} | |
| {{- msg.content }} | |
| {%- elif msg.role == "assistant" %} | |
| {{- "<longcat_assistant>" }} | |
| {%- if enable_thinking == true and msg.reasoning_content and ns.tool_types != [] and loop.index0 > ns.last_query_index %} | |
| {{- "\n<longcat_think>\n" ~ msg.reasoning_content ~ "\n</longcat_think>\n" }} | |
| {%- endif %} | |
| {%- if msg.content%} | |
| {{- msg.content }} | |
| {%- endif %} | |
| {%- if msg.tool_calls %} | |
| {%- for tool_call in msg.tool_calls -%} | |
| {{- "<longcat_tool_call>" ~ tool_call.function.name ~ "\n" -}} | |
| {% set _args = tool_call.function.arguments %} | |
| {% for k, v in _args.items() %} | |
| {{- "<longcat_arg_key>" ~ k ~ "</longcat_arg_key>\n" -}} | |
| {{- "<longcat_arg_value>" ~ (v if v is string else v | tojson(ensure_ascii=False)) ~ "</longcat_arg_value>\n" -}} | |
| {% endfor %} | |
| {{- "</longcat_tool_call>\n" }} | |
| {%- endfor %} | |
| {%- endif %} | |
| {{- "</longcat_s>" -}} | |
| {%- elif msg.role == "tool" %} | |
| {%- if messages[loop.index0 - 1].role != "tool"%} | |
| {{- "<longcat_user>" -}} | |
| {%- endif %} | |
| {{- "<longcat_tool_response>" ~ msg.content ~ "</longcat_tool_response>"-}} | |
| {%- endif %} | |
| {%- endfor %} | |
| {%- if add_generation_prompt %} | |
| {%- if enable_thinking == true %} | |
| {{- " /think_on" }} | |
| {%- if thinking_budget %} | |
| {%- if thinking_budget < 1024 %} | |
| {%- set thinking_budget = 1024 %} | |
| {%- endif%} | |
| {{- "\nthinking_budget: < " ~ thinking_budget ~ "."}} | |
| {%- endif %} | |
| {{- " <longcat_assistant><longcat_think>\n"}} | |
| {%- elif enable_thinking == false %} | |
| {{- " /think_off <longcat_assistant><longcat_think>\n\n</longcat_think>\n" }} | |
| {%- else %} | |
| {{- "<longcat_assistant>" }} | |
| {%- endif %} | |
| {%- endif %} |