Instructions to use tiny-random/longcat-flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tiny-random/longcat-flash with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="tiny-random/longcat-flash", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("tiny-random/longcat-flash", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("tiny-random/longcat-flash", trust_remote_code=True, device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use tiny-random/longcat-flash with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tiny-random/longcat-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": "tiny-random/longcat-flash", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/tiny-random/longcat-flash
- SGLang
How to use tiny-random/longcat-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 "tiny-random/longcat-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": "tiny-random/longcat-flash", "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 "tiny-random/longcat-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": "tiny-random/longcat-flash", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use tiny-random/longcat-flash with Docker Model Runner:
docker model run hf.co/tiny-random/longcat-flash
File size: 5,391 Bytes
35f4a5f | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 | {%- set tool_choice = tool_choice | default('auto') %}
{%- set ns = namespace(rounds = 0, tool_types = [], last_query_index = -1) %}
{%- if tools and tool_choice != 'none' %}
{{- "# Tools
" }}
{{- "You have access to the following tools:
" }}
{%- for tool in tools %}
{%- if tool.type in ['code_interpreter', 'function'] %}
{%- if tool.type not in ns.tool_types %}
{%- set ns.tool_types = ns.tool_types + [tool.type] %}
{{- "## Tool namespace: " ~ tool.type ~ "
" }}
{%- 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.
Code sandbox environment (network isolated) Any external network requests or online API calls are prohibited.
If online functionality is needed, please use other permitted tools.
Code 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:
- Importing network libraries such as requests, httplib, etc.
- Any form of HTTP requests.
- External API calls.
- Network port operations. Example: ```python
import pandas as pd
pd.DataFrame({'A':[1,2]})
```"},"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 + "
" }}
{{- "Description: " + tool.function.description + "
" }}
{{- "InputSchema:
" + tool.function.parameters | tojson(indent=2) + "
" }}
{%- endif %}
{%- endfor %}
{{- '**Note**: For each function call, return a json object with function name and arguments within <longcat_tool_call></longcat_tool_call> XML tags as follows:
<longcat_tool_call>
{"name": <function-name>, "arguments": <args-dict>}
</longcat_tool_call>
' }}
{{- 'When multiple functions need to be called simultaneously, each function call should be wrapped in its own <longcat_tool_call> tag and placed consecutively. For example:
<longcat_tool_call>
{"name": <function-name>, "arguments": <args-dict>}
</longcat_tool_call><longcat_tool_call>
{"name": <function-name>, "arguments": <args-dict>}
</longcat_tool_call>
' }}
{{- "# Messages
" }}
{%- 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" %}
{{- "SYSTEM:" + msg.content }}
{%- elif msg.role == "user" %}
{%- if loop.first %}
{{- "[Round " ~ (ns.rounds) ~ "] USER:" }}
{%- else %}
{{- " [Round " ~ (ns.rounds) ~ "] USER:"}}
{%- endif %}
{%- set ns.rounds = ns.rounds + 1 %}
{%- if msg["files"] %}
{{- '<longcat_files>
' ~ msg.files | tojson(indent=2) ~ '
</longcat_files>' }}
{%- endif %}
{{- msg.content }}
{%- elif msg.role == "assistant" %}
{{- " ASSISTANT:" }}
{%- if enable_thinking == true and msg.reasoning_content and ns.tool_types != [] and loop.index0 > ns.last_query_index %}
{{- "
<longcat_think>
" ~ msg.reasoning_content ~ "
</longcat_think>
" }}
{%- endif %}
{%- if msg.content%}
{{- msg.content }}
{%- endif %}
{%- if msg.tool_calls %}
{%- for tool_call in msg.tool_calls -%}
{{- "<longcat_tool_call>
" -}}
{%- if tool_call.function.arguments is string -%}
{"name": "{{ tool_call.function.name}}", "arguments": {{tool_call.function.arguments}}}
{%- else -%}
{"name": "{{ tool_call.function.name}}", "arguments": {{tool_call.function.arguments | tojson}}}
{%- endif -%}
{{- "
</longcat_tool_call>" }}
{%- endfor %}
{%- endif %}
{{- "</longcat_s>" -}}
{%- elif msg.role == "tool" %}
{{- " TOOL:" -}}
{%- if msg.name -%}
{"name": {{msg.name | tojson}}, "content": {{msg.content | tojson}}}
{%- else -%}
{"content": {{msg.content | tojson}}}
{%- endif -%}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{%- if enable_thinking == true %}
{{- " /think_on" }}
{%- if thinking_budget %}
{%- if thinking_budget < 1024 %}
{%- set thinking_budget = 1024 %}
{%- endif%}
{{- "
thinking_budget: < " ~ thinking_budget ~ "."}}
{%- endif %}
{{- " ASSISTANT:<longcat_think>
"}}
{%- elif enable_thinking == false %}
{{- " /think_off ASSISTANT:<longcat_think>
</longcat_think>
" }}
{%- else %}
{{- " ASSISTANT:" }}
{%- endif %}
{%- endif %} |