Instructions to use finding1/LongCat-Flash-Thinking-2601-MLX-5.5bpw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use finding1/LongCat-Flash-Thinking-2601-MLX-5.5bpw with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("finding1/LongCat-Flash-Thinking-2601-MLX-5.5bpw") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Transformers
How to use finding1/LongCat-Flash-Thinking-2601-MLX-5.5bpw with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="finding1/LongCat-Flash-Thinking-2601-MLX-5.5bpw", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, LongcatCausalLM tokenizer = AutoTokenizer.from_pretrained("finding1/LongCat-Flash-Thinking-2601-MLX-5.5bpw", trust_remote_code=True) model = LongcatCausalLM.from_pretrained("finding1/LongCat-Flash-Thinking-2601-MLX-5.5bpw", 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
- LM Studio
- vLLM
How to use finding1/LongCat-Flash-Thinking-2601-MLX-5.5bpw with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "finding1/LongCat-Flash-Thinking-2601-MLX-5.5bpw" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "finding1/LongCat-Flash-Thinking-2601-MLX-5.5bpw", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/finding1/LongCat-Flash-Thinking-2601-MLX-5.5bpw
- SGLang
How to use finding1/LongCat-Flash-Thinking-2601-MLX-5.5bpw 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 "finding1/LongCat-Flash-Thinking-2601-MLX-5.5bpw" \ --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": "finding1/LongCat-Flash-Thinking-2601-MLX-5.5bpw", "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 "finding1/LongCat-Flash-Thinking-2601-MLX-5.5bpw" \ --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": "finding1/LongCat-Flash-Thinking-2601-MLX-5.5bpw", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Pi
How to use finding1/LongCat-Flash-Thinking-2601-MLX-5.5bpw with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "finding1/LongCat-Flash-Thinking-2601-MLX-5.5bpw"
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "finding1/LongCat-Flash-Thinking-2601-MLX-5.5bpw" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use finding1/LongCat-Flash-Thinking-2601-MLX-5.5bpw with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "finding1/LongCat-Flash-Thinking-2601-MLX-5.5bpw"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "finding1/LongCat-Flash-Thinking-2601-MLX-5.5bpw" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "finding1/LongCat-Flash-Thinking-2601-MLX-5.5bpw", "messages": [ {"role": "user", "content": "Hello"} ] }' - Docker Model Runner
How to use finding1/LongCat-Flash-Thinking-2601-MLX-5.5bpw with Docker Model Runner:
docker model run hf.co/finding1/LongCat-Flash-Thinking-2601-MLX-5.5bpw
- Hermes Agent
How to use finding1/LongCat-Flash-Thinking-2601-MLX-5.5bpw with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "finding1/LongCat-Flash-Thinking-2601-MLX-5.5bpw"
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default finding1/LongCat-Flash-Thinking-2601-MLX-5.5bpw
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use finding1/LongCat-Flash-Thinking-2601-MLX-5.5bpw with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "finding1/LongCat-Flash-Thinking-2601-MLX-5.5bpw"
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "finding1/LongCat-Flash-Thinking-2601-MLX-5.5bpw" \ --custom-provider-id mlx-lm \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
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