Instructions to use vasanth009/LC-lfm2.5-350m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vasanth009/LC-lfm2.5-350m 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-lfm2.5-350m")

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

How to use vasanth009/LC-lfm2.5-350m 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-lfm2.5-350m"

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-lfm2.5-350m"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use vasanth009/LC-lfm2.5-350m 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-lfm2.5-350m"

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-lfm2.5-350m

Run Hermes

hermes

OpenClaw new

How to use vasanth009/LC-lfm2.5-350m 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-lfm2.5-350m"

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-lfm2.5-350m" \
  --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-lfm2.5-350m 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-lfm2.5-350m"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "vasanth009/LC-lfm2.5-350m"
# 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-lfm2.5-350m",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

LC-lfm2.5-350m

File size: 1,082 Bytes

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{
    "adapter_path": "/Users/vas/Documents/macwispr/bench/polish_finetune/adapters/sotto-lc-ft-clean",
    "batch_size": 2,
    "clear_cache_threshold": 0,
    "config": null,
    "data": "/Users/vas/Documents/macwispr/bench/polish_finetune/data/sotto_ft",
    "fine_tune_type": "lora",
    "grad_accumulation_steps": 1,
    "grad_checkpoint": false,
    "iters": 600,
    "learning_rate": 5e-06,
    "lora_parameters": {
        "rank": 8,
        "dropout": 0.0,
        "scale": 20.0
    },
    "lr_schedule": null,
    "mask_prompt": false,
    "max_seq_length": 512,
    "model": "juanquivilla/sotto-cleanup-lfm25-350m-mlx-5bit",
    "num_layers": 8,
    "optimizer": "adam",
    "optimizer_config": {
        "adam": {},
        "adamw": {},
        "muon": {},
        "sgd": {},
        "adafactor": {}
    },
    "project_name": null,
    "report_to": null,
    "resume_adapter_file": null,
    "save_every": 200,
    "seed": 42,
    "steps_per_eval": 200,
    "steps_per_report": 100,
    "test": false,
    "test_batches": 500,
    "train": true,
    "val_batches": 25
}