Instructions to use finalform/rewrite_plan_Qwen3-Coder-30B-A3B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use finalform/rewrite_plan_Qwen3-Coder-30B-A3B-Instruct with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-Coder-30B-A3B-Instruct") model = PeftModel.from_pretrained(base_model, "finalform/rewrite_plan_Qwen3-Coder-30B-A3B-Instruct") - Transformers
How to use finalform/rewrite_plan_Qwen3-Coder-30B-A3B-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="finalform/rewrite_plan_Qwen3-Coder-30B-A3B-Instruct") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("finalform/rewrite_plan_Qwen3-Coder-30B-A3B-Instruct", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use finalform/rewrite_plan_Qwen3-Coder-30B-A3B-Instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "finalform/rewrite_plan_Qwen3-Coder-30B-A3B-Instruct" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "finalform/rewrite_plan_Qwen3-Coder-30B-A3B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/finalform/rewrite_plan_Qwen3-Coder-30B-A3B-Instruct
- SGLang
How to use finalform/rewrite_plan_Qwen3-Coder-30B-A3B-Instruct 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 "finalform/rewrite_plan_Qwen3-Coder-30B-A3B-Instruct" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "finalform/rewrite_plan_Qwen3-Coder-30B-A3B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "finalform/rewrite_plan_Qwen3-Coder-30B-A3B-Instruct" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "finalform/rewrite_plan_Qwen3-Coder-30B-A3B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use finalform/rewrite_plan_Qwen3-Coder-30B-A3B-Instruct with Docker Model Runner:
docker model run hf.co/finalform/rewrite_plan_Qwen3-Coder-30B-A3B-Instruct
| {"current_steps": 5, "total_steps": 56, "loss": 1.071, "lr": 0.0004, "epoch": 0.35714285714285715, "percentage": 8.93, "elapsed_time": "0:11:36", "remaining_time": "1:58:19"} | |
| {"current_steps": 10, "total_steps": 56, "loss": 0.7765, "lr": 0.00036862745098039214, "epoch": 0.7142857142857143, "percentage": 17.86, "elapsed_time": "0:21:30", "remaining_time": "1:38:58"} | |
| {"current_steps": 15, "total_steps": 56, "loss": 0.6828, "lr": 0.0003294117647058824, "epoch": 1.0714285714285714, "percentage": 26.79, "elapsed_time": "0:31:22", "remaining_time": "1:25:46"} | |
| {"current_steps": 20, "total_steps": 56, "loss": 0.5171, "lr": 0.00029019607843137256, "epoch": 1.4285714285714286, "percentage": 35.71, "elapsed_time": "0:41:25", "remaining_time": "1:14:33"} | |
| {"current_steps": 25, "total_steps": 56, "loss": 0.4798, "lr": 0.00025098039215686274, "epoch": 1.7857142857142856, "percentage": 44.64, "elapsed_time": "0:51:03", "remaining_time": "1:03:18"} | |
| {"current_steps": 25, "total_steps": 56, "eval_loss": 0.48145776987075806, "epoch": 1.7857142857142856, "percentage": 44.64, "elapsed_time": "0:55:47", "remaining_time": "1:09:10"} | |
| {"current_steps": 30, "total_steps": 56, "loss": 0.42, "lr": 0.00021176470588235295, "epoch": 2.142857142857143, "percentage": 53.57, "elapsed_time": "1:07:03", "remaining_time": "0:58:07"} | |
| {"current_steps": 35, "total_steps": 56, "loss": 0.3501, "lr": 0.00017254901960784316, "epoch": 2.5, "percentage": 62.5, "elapsed_time": "1:16:20", "remaining_time": "0:45:48"} | |
| {"current_steps": 40, "total_steps": 56, "loss": 0.3359, "lr": 0.00013333333333333334, "epoch": 2.857142857142857, "percentage": 71.43, "elapsed_time": "1:26:09", "remaining_time": "0:34:27"} | |
| {"current_steps": 45, "total_steps": 56, "loss": 0.2673, "lr": 9.411764705882353e-05, "epoch": 3.2142857142857144, "percentage": 80.36, "elapsed_time": "1:35:41", "remaining_time": "0:23:23"} | |
| {"current_steps": 50, "total_steps": 56, "loss": 0.2373, "lr": 5.490196078431373e-05, "epoch": 3.571428571428571, "percentage": 89.29, "elapsed_time": "1:44:57", "remaining_time": "0:12:35"} | |
| {"current_steps": 50, "total_steps": 56, "eval_loss": 0.458571195602417, "epoch": 3.571428571428571, "percentage": 89.29, "elapsed_time": "1:50:06", "remaining_time": "0:13:12"} | |
| {"current_steps": 55, "total_steps": 56, "loss": 0.25, "lr": 1.568627450980392e-05, "epoch": 3.928571428571429, "percentage": 98.21, "elapsed_time": "2:01:14", "remaining_time": "0:02:12"} | |
| {"current_steps": 56, "total_steps": 56, "epoch": 4.0, "percentage": 100.0, "elapsed_time": "2:04:31", "remaining_time": "0:00:00"} | |