Instructions to use lmstudio-community/LFM2.5-1.2B-Instruct-MLX-6bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lmstudio-community/LFM2.5-1.2B-Instruct-MLX-6bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="lmstudio-community/LFM2.5-1.2B-Instruct-MLX-6bit") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("lmstudio-community/LFM2.5-1.2B-Instruct-MLX-6bit") model = AutoModelForCausalLM.from_pretrained("lmstudio-community/LFM2.5-1.2B-Instruct-MLX-6bit", 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]:])) - MLX
How to use lmstudio-community/LFM2.5-1.2B-Instruct-MLX-6bit 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("lmstudio-community/LFM2.5-1.2B-Instruct-MLX-6bit") 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) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- vLLM
How to use lmstudio-community/LFM2.5-1.2B-Instruct-MLX-6bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "lmstudio-community/LFM2.5-1.2B-Instruct-MLX-6bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lmstudio-community/LFM2.5-1.2B-Instruct-MLX-6bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/lmstudio-community/LFM2.5-1.2B-Instruct-MLX-6bit
- SGLang
How to use lmstudio-community/LFM2.5-1.2B-Instruct-MLX-6bit 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 "lmstudio-community/LFM2.5-1.2B-Instruct-MLX-6bit" \ --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": "lmstudio-community/LFM2.5-1.2B-Instruct-MLX-6bit", "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 "lmstudio-community/LFM2.5-1.2B-Instruct-MLX-6bit" \ --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": "lmstudio-community/LFM2.5-1.2B-Instruct-MLX-6bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Pi
How to use lmstudio-community/LFM2.5-1.2B-Instruct-MLX-6bit with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "lmstudio-community/LFM2.5-1.2B-Instruct-MLX-6bit"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "lmstudio-community/LFM2.5-1.2B-Instruct-MLX-6bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use lmstudio-community/LFM2.5-1.2B-Instruct-MLX-6bit 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 "lmstudio-community/LFM2.5-1.2B-Instruct-MLX-6bit"
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 lmstudio-community/LFM2.5-1.2B-Instruct-MLX-6bit
Run Hermes
hermes
- OpenClaw new
How to use lmstudio-community/LFM2.5-1.2B-Instruct-MLX-6bit with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "lmstudio-community/LFM2.5-1.2B-Instruct-MLX-6bit"
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 "lmstudio-community/LFM2.5-1.2B-Instruct-MLX-6bit" \ --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"
- MLX LM
How to use lmstudio-community/LFM2.5-1.2B-Instruct-MLX-6bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "lmstudio-community/LFM2.5-1.2B-Instruct-MLX-6bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "lmstudio-community/LFM2.5-1.2B-Instruct-MLX-6bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lmstudio-community/LFM2.5-1.2B-Instruct-MLX-6bit", "messages": [ {"role": "user", "content": "Hello"} ] }' - Docker Model Runner
How to use lmstudio-community/LFM2.5-1.2B-Instruct-MLX-6bit with Docker Model Runner:
docker model run hf.co/lmstudio-community/LFM2.5-1.2B-Instruct-MLX-6bit
| {{- bos_token -}} | |
| {%- set keep_past_thinking = keep_past_thinking | default(false) -%} | |
| {%- set ns = namespace(system_prompt="") -%} | |
| {%- if messages[0]["role"] == "system" -%} | |
| {%- set ns.system_prompt = messages[0]["content"] -%} | |
| {%- set messages = messages[1:] -%} | |
| {%- endif -%} | |
| {%- if tools -%} | |
| {%- set ns.system_prompt = ns.system_prompt + ("\n" if ns.system_prompt else "") + "List of tools: [" -%} | |
| {%- for tool in tools -%} | |
| {%- if tool is not string -%} | |
| {%- set tool = tool | tojson -%} | |
| {%- endif -%} | |
| {%- set ns.system_prompt = ns.system_prompt + tool -%} | |
| {%- if not loop.last -%} | |
| {%- set ns.system_prompt = ns.system_prompt + ", " -%} | |
| {%- endif -%} | |
| {%- endfor -%} | |
| {%- set ns.system_prompt = ns.system_prompt + "]" -%} | |
| {%- endif -%} | |
| {%- if ns.system_prompt -%} | |
| {{- "<|im_start|>system\n" + ns.system_prompt + "<|im_end|>\n" -}} | |
| {%- endif -%} | |
| {%- set ns.last_assistant_index = -1 -%} | |
| {%- 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.get('tool_calls') %} | |
| {# ───── create a list to append tool calls to ───── #} | |
| {%- set tool_calls_ns = namespace(tool_calls=[])%} | |
| {%- for tool_call in message['tool_calls'] %} | |
| {%- set func_name = tool_call['function']['name'] %} | |
| {%- set func_args = tool_call['function']['arguments'] %} | |
| {# ───── create a list of func_arg strings to accumulate for each tool call ───── #} | |
| {%- set args_ns = namespace(arg_strings=[])%} | |
| {%- for arg_name, arg_value in func_args.items() %} | |
| {%- if arg_value is none %} | |
| {%- set formatted_arg_value = 'null' %} | |
| {%- elif arg_value is boolean %} | |
| {%- set formatted_arg_value = 'True' if arg_value else 'False' %} | |
| {%- elif arg_value is string %} | |
| {%- set formatted_arg_value = '"' ~ arg_value ~ '"' %} | |
| {%- elif arg_value is mapping or arg_value is iterable %} | |
| {%- set formatted_arg_value = arg_value | tojson %} | |
| {%- else %} | |
| {%- set formatted_arg_value = arg_value | string %} | |
| {%- endif %} | |
| {# ───── format each argument key,value pair ───── #} | |
| {%- set args_ns.arg_strings = args_ns.arg_strings + [arg_name ~ '=' ~ formatted_arg_value] %} | |
| {%- endfor %} | |
| {# ───── append each formatted tool call ───── #} | |
| {%- set tool_calls_ns.tool_calls = tool_calls_ns.tool_calls + [(func_name + '(' + (args_ns.arg_strings | join(", ")) + ')' )]%} | |
| {%- endfor %} | |
| {# ───── format the final tool calls ───── #} | |
| {{-'<|tool_call_start|>[' + (tool_calls_ns.tool_calls | join(", ")) + ']<|tool_call_end|>'}} | |
| {%- endif %} | |
| {%- set content = message["content"] -%} | |
| {%- if content is not string -%} | |
| {%- set content = content | tojson -%} | |
| {%- endif -%} | |
| {%- if message["role"] == "assistant" and 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 + "<|im_end|>\n" -}} | |
| {%- endfor -%} | |
| {%- if add_generation_prompt -%} | |
| {{- "<|im_start|>assistant\n" -}} | |
| {%- endif -%} | |