Instructions to use hybridaione/LFM2.5-1.2B-Text2SQL-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hybridaione/LFM2.5-1.2B-Text2SQL-MLX 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("hybridaione/LFM2.5-1.2B-Text2SQL-MLX")

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
LM Studio

Pi new

How to use hybridaione/LFM2.5-1.2B-Text2SQL-MLX with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "hybridaione/LFM2.5-1.2B-Text2SQL-MLX"

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": "hybridaione/LFM2.5-1.2B-Text2SQL-MLX"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use hybridaione/LFM2.5-1.2B-Text2SQL-MLX 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 "hybridaione/LFM2.5-1.2B-Text2SQL-MLX"

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 hybridaione/LFM2.5-1.2B-Text2SQL-MLX

Run Hermes

hermes

MLX LM

How to use hybridaione/LFM2.5-1.2B-Text2SQL-MLX with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "hybridaione/LFM2.5-1.2B-Text2SQL-MLX"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "hybridaione/LFM2.5-1.2B-Text2SQL-MLX"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "hybridaione/LFM2.5-1.2B-Text2SQL-MLX",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

furukama commited on Jan 31

Commit

1aacf8b

verified ·

1 Parent(s): 714792b

Upload folder using huggingface_hub

Browse files

Files changed (1) hide show

README.md +1 -7

README.md CHANGED Viewed

@@ -58,7 +58,7 @@ The model shows consistent improvement across all checkpoints with no signs of o
 ```python
 from mlx_lm import load, generate
-model, tokenizer = load("furukama/LFM2.5-1.2B-Text2SQL-MLX")
 # Example query
 prompt = '''CREATE TABLE employees (id INT, name VARCHAR, salary DECIMAL);
@@ -69,12 +69,6 @@ response = generate(model, tokenizer, prompt=prompt, max_tokens=256)
 print(response)
 ```
-## Limitations
-- Trained on synthetic data for a specific database schema
-- Best suited for similar SQL query patterns seen during training
-- May not generalize well to very different database schemas
 ## License
 This model is released under the Apache 2.0 license, following the base model's license.

 ```python
 from mlx_lm import load, generate
+model, tokenizer = load("hybridaione/LFM2.5-1.2B-Text2SQL-MLX")
 # Example query
 prompt = '''CREATE TABLE employees (id INT, name VARCHAR, salary DECIMAL);
 print(response)
 ```
 ## License
 This model is released under the Apache 2.0 license, following the base model's license.