Text Generation
Transformers
Safetensors
designcoder
ui-generation
front-end
html
css
javascript
code-generation
full-sft
Instructions to use xingxm/DesignCoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xingxm/DesignCoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="xingxm/DesignCoder")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("xingxm/DesignCoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use xingxm/DesignCoder with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "xingxm/DesignCoder" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "xingxm/DesignCoder", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/xingxm/DesignCoder
- SGLang
How to use xingxm/DesignCoder 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 "xingxm/DesignCoder" \ --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": "xingxm/DesignCoder", "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 "xingxm/DesignCoder" \ --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": "xingxm/DesignCoder", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use xingxm/DesignCoder with Docker Model Runner:
docker model run hf.co/xingxm/DesignCoder
Add designcoder_evaluator_qwen3.5_9b_adamw_bs256_data37847_step100/tokenizer.json
Browse files
.gitattributes
CHANGED
|
@@ -44,3 +44,4 @@ designcoder_qwen3.5_4b_muon_bs256_data41287_step200/tokenizer.json filter=lfs di
|
|
| 44 |
designcoder_qwen3.5_9b_adamw_bs256_data41287_step200/tokenizer.json filter=lfs diff=lfs merge=lfs -text
|
| 45 |
designcoder_qwen3.8_27b_adamw_bs128_data41287_step400/tokenizer.json filter=lfs diff=lfs merge=lfs -text
|
| 46 |
designcoder_evaluator_qwen3.8_27b_adamw_bs256_data37847_step296/tokenizer.json filter=lfs diff=lfs merge=lfs -text
|
|
|
|
|
|
| 44 |
designcoder_qwen3.5_9b_adamw_bs256_data41287_step200/tokenizer.json filter=lfs diff=lfs merge=lfs -text
|
| 45 |
designcoder_qwen3.8_27b_adamw_bs128_data41287_step400/tokenizer.json filter=lfs diff=lfs merge=lfs -text
|
| 46 |
designcoder_evaluator_qwen3.8_27b_adamw_bs256_data37847_step296/tokenizer.json filter=lfs diff=lfs merge=lfs -text
|
| 47 |
+
designcoder_evaluator_qwen3.5_9b_adamw_bs256_data37847_step100/tokenizer.json filter=lfs diff=lfs merge=lfs -text
|
designcoder_evaluator_qwen3.5_9b_adamw_bs256_data37847_step100/tokenizer.json
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:06b9509352d2af50381ab2247e083b80d32d5c0aba91c272ca9ff729b6a0e523
|
| 3 |
+
size 19989325
|