Text Generation
MLX
Safetensors
English
lfm2
dictation
speech-to-text
cleanup
polish
apple-silicon
lfm
lc-350m
conversational
4-bit precision
Instructions to use vasanth009/LC-350M-light with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use vasanth009/LC-350M-light 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("vasanth009/LC-350M-light") 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
- Pi
How to use vasanth009/LC-350M-light with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "vasanth009/LC-350M-light"
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": "vasanth009/LC-350M-light" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use vasanth009/LC-350M-light 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 "vasanth009/LC-350M-light"
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 vasanth009/LC-350M-light
Run Hermes
hermes
- OpenClaw new
How to use vasanth009/LC-350M-light with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "vasanth009/LC-350M-light"
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 "vasanth009/LC-350M-light" \ --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 vasanth009/LC-350M-light with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "vasanth009/LC-350M-light"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "vasanth009/LC-350M-light" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "vasanth009/LC-350M-light", "messages": [ {"role": "user", "content": "Hello"} ] }'
| {{- bos_token -}} | |
| {%- set keep_past_thinking = keep_past_thinking | default(false) -%} | |
| {%- set ns = namespace(system_prompt="") -%} | |
| {%- if messages[0]["role"] == "system" -%} | |
| {%- set sys_content = messages[0]["content"] -%} | |
| {%- if sys_content is not string -%} | |
| {%- for item in sys_content -%} | |
| {%- if item["type"] == "text" -%} | |
| {%- set ns.system_prompt = ns.system_prompt + item["text"] -%} | |
| {%- endif -%} | |
| {%- endfor -%} | |
| {%- else -%} | |
| {%- set ns.system_prompt = sys_content -%} | |
| {%- endif -%} | |
| {%- 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" -}} | |
| {%- set content = message["content"] -%} | |
| {%- if content is not string -%} | |
| {%- set ns.content = "" -%} | |
| {%- for item in content -%} | |
| {%- if item["type"] == "image" -%} | |
| {%- set ns.content = ns.content + "<image>" -%} | |
| {%- elif item["type"] == "text" -%} | |
| {%- set ns.content = ns.content + item["text"] -%} | |
| {%- else -%} | |
| {%- set ns.content = ns.content + item | tojson -%} | |
| {%- endif -%} | |
| {%- endfor -%} | |
| {%- set content = ns.content -%} | |
| {%- 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 -%} | |