Text Generation
MLX
English
structured-generation
parallel-decoding
constrained-decoding
apple-silicon
classification
json
Instructions to use botp/Qwen-2.5-1B-RLCD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use botp/Qwen-2.5-1B-RLCD with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("botp/Qwen-2.5-1B-RLCD") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use botp/Qwen-2.5-1B-RLCD with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "botp/Qwen-2.5-1B-RLCD" --prompt "Once upon a time"
- Atomic Chat
File size: 530 Bytes
84f0c1f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | """
Entry point to run the FastAPI server persistently.
"""
import sys
import os
import uvicorn
# Ensure project root is in PYTHONPATH
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
if __name__ == "__main__":
host = os.environ.get("HOST", "0.0.0.0" if "SPACE_ID" in os.environ else "127.0.0.1")
port = int(os.environ.get("PORT", 7860 if "SPACE_ID" in os.environ else 8000))
uvicorn.run(
"server.app:app",
host=host,
port=port,
log_level="info"
)
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