Image-Text-to-Text
Transformers
Safetensors
qwen3_5
designcoder
tool-use
web-design
sft
conversational
Instructions to use chjdl1816/designcoder-mc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chjdl1816/designcoder-mc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="chjdl1816/designcoder-mc") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("chjdl1816/designcoder-mc") model = AutoModelForMultimodalLM.from_pretrained("chjdl1816/designcoder-mc", device_map="auto") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=256) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use chjdl1816/designcoder-mc with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "chjdl1816/designcoder-mc" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "chjdl1816/designcoder-mc", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/chjdl1816/designcoder-mc
- SGLang
How to use chjdl1816/designcoder-mc 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 "chjdl1816/designcoder-mc" \ --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": "chjdl1816/designcoder-mc", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'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 "chjdl1816/designcoder-mc" \ --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": "chjdl1816/designcoder-mc", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use chjdl1816/designcoder-mc with Docker Model Runner:
docker model run hf.co/chjdl1816/designcoder-mc
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license: apache-2.0
base_model: Qwen/Qwen3.5-4B
library_name: transformers
pipeline_tag: image-text-to-text
tags: [designcoder, tool-use, web-design, sft]
---
# DesignCoder 4B — AdamW step200
Full BF16 tool-using DesignCoder checkpoint, selected from
`xingxm/DesignCoder/designcoder_qwen3.5_4b_adamw_bs256_data41287_step200`.
This is an SFT initialization for DesignCoder RL. Its original weights, tokenizer,
processor and chat template are preserved byte-for-byte, without quantization or LoRA merging.
The source run uses Qwen3.5-4B, AdamW (learning rate 2e-5), global batch 256 and
41,287 DesignCoder v2 trajectories. The export is step200, epoch 1.5066.
The source model card records training on 8 nodes x 8 H20.
## Tool protocol and inference
Use the system prompt and tools in
[DesignCoder runtime](https://github.com/ZitengXue/DesignCoder/tree/main/examples/designcoder/runtime).
Landing: `design_search -> websearch -> HTML/CSS/JS`.
Dashboard: `design_search -> HTML/CSS/JS`.
Calls are written as `<tools>tool_name(...)</tools>`, followed by tool observations.
The final answer contains `<code lang="html">`, `<code lang="css">` and `<code lang="js">` blocks.
The original inference defaults are sampling with temperature 0.6, top_p 0.95,
top_k 20, repetition_penalty 1.0 and thinking enabled. Tool turns allow 1,536
new tokens; the final turn requests 32,768 tokens, capped by remaining room
in the 32,768-token context. Websearch requests four candidates per query.
Use `examples/designcoder/runtime/infer_designcoder.py` or the aligned RL agent;
a plain single-turn prompt does not reproduce the tool protocol.
## Evaluation provenance
The retained 200-case evaluation scores 83.2948 under the explicitly named
`fixed-assets-layout-v1` aggregation. The corresponding 4B Muon step200 scores
76.2119 under the same aggregation. The original judge identity is not recorded.
Older model-card numbers use other aggregation definitions and should not be mixed.
No new benchmark or GPU evaluation is claimed by this upload.
## Replacement and verification
This replaces the mistakenly selected Design.md-only AdamW step12588 checkpoint.
That previous model remains in Git history and the archive tag
`archive-designmd-4b-step12588-20261109`. The new root has one full
`model.safetensors`; it has no adapter or old weight shards.
`upload_manifest.json` records SHA256 values of every copied inference file.
`training_summary.json` records the checkpoint identity and source evidence.
```bash
hf download chjdl1816/designcoder-mc --local-dir models/designcoder-4b-adamw-step200
```
Use a clean local directory when switching from an earlier export.
The model architecture is `Qwen3_5ForConditionalGeneration`.
DesignEvaluator is a separate frozen 9B model and is not included here.
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