Instructions to use IFM/CrystalChat-7B-Web2Code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use IFM/CrystalChat-7B-Web2Code with Transformers:

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

pipe = pipeline("text-generation", model="IFM/CrystalChat-7B-Web2Code", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("IFM/CrystalChat-7B-Web2Code", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use IFM/CrystalChat-7B-Web2Code with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "IFM/CrystalChat-7B-Web2Code"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IFM/CrystalChat-7B-Web2Code",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/IFM/CrystalChat-7B-Web2Code

SGLang

How to use IFM/CrystalChat-7B-Web2Code 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 "IFM/CrystalChat-7B-Web2Code" \
    --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": "IFM/CrystalChat-7B-Web2Code",
		"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 "IFM/CrystalChat-7B-Web2Code" \
        --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": "IFM/CrystalChat-7B-Web2Code",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use IFM/CrystalChat-7B-Web2Code with Docker Model Runner:
```
docker model run hf.co/IFM/CrystalChat-7B-Web2Code
```

victormiller commited on Oct 22, 2024

Commit

cb39d5c

verified ·

1 Parent(s): b666a36

Update README.md

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@@ -148,26 +148,19 @@ The dataset chosen was created by LLaVA with academic-task-oriented VQA data mix
-Example 1:
 | ![Image 1](images2/handdrawn.png) | ![Image 2](images2/crystal.png) |
 |:----------------------:|:----------------------:|
-| Description of Image 1  | Description of Image 2 |
-<p float="left">
-  <img src="images2/handdrawn.png" alt="Image 1" width="45%" />
-  <img src="images2/crystal.png" alt="Image 2" width="45%" />
-</p>
-**Image 1:** Original Input Image.
-<center></center>
-**Image 2:** CrystalChat-7B-Web2Code model output.
-Example 2:
-<center><img src="hand_draw1_.png" alt="CrsytalChat-7B model generated output"/></center>
 **Image 3:** Hand-drawn webpage input to CrystalChat-7B-Web2Code generated output.

+**Example 1: Hand drawn images**
 | ![Image 1](images2/handdrawn.png) | ![Image 2](images2/crystal.png) |
 |:----------------------:|:----------------------:|
+| Hand Drawn Webpage  | CrystalChat-Web2Code Rendering |
+**Example 2: Recreate a webpage from an image**
+| ![Image 1](images2/handdrawn.png) | ![Image 2](images2/crystal.png) |
+|:----------------------:|:----------------------:|
+| Hand Drawn Webpage  | CrystalChat-Web2Code Rendering |
 **Image 3:** Hand-drawn webpage input to CrystalChat-7B-Web2Code generated output.