Instructions to use epsilon3/Qwen-2.5-1B-RLCD-Fast with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use epsilon3/Qwen-2.5-1B-RLCD-Fast with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="epsilon3/Qwen-2.5-1B-RLCD-Fast")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("epsilon3/Qwen-2.5-1B-RLCD-Fast", device_map="auto")

MLX

How to use epsilon3/Qwen-2.5-1B-RLCD-Fast 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("epsilon3/Qwen-2.5-1B-RLCD-Fast")

prompt = "Once upon a time in"
text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

vLLM

How to use epsilon3/Qwen-2.5-1B-RLCD-Fast with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "epsilon3/Qwen-2.5-1B-RLCD-Fast"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "epsilon3/Qwen-2.5-1B-RLCD-Fast",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/epsilon3/Qwen-2.5-1B-RLCD-Fast

SGLang

How to use epsilon3/Qwen-2.5-1B-RLCD-Fast 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 "epsilon3/Qwen-2.5-1B-RLCD-Fast" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "epsilon3/Qwen-2.5-1B-RLCD-Fast",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "epsilon3/Qwen-2.5-1B-RLCD-Fast" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "epsilon3/Qwen-2.5-1B-RLCD-Fast",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

MLX LM

How to use epsilon3/Qwen-2.5-1B-RLCD-Fast with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Generate some text
mlx_lm.generate --model "epsilon3/Qwen-2.5-1B-RLCD-Fast" --prompt "Once upon a time"

Docker Model Runner
How to use epsilon3/Qwen-2.5-1B-RLCD-Fast with Docker Model Runner:
```
docker model run hf.co/epsilon3/Qwen-2.5-1B-RLCD-Fast
```
Atomic Chat

Qwen-2.5-1B-RLCD-Fast / server /main.py

epsilon3

Add tree-attention fast path and CUDA/M4 benchmarks

f36843f verified 26 days ago

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550 Bytes

	"""
	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"
	)