Instructions to use LLM360/CrystalChat-7B-Web2Code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LLM360/CrystalChat-7B-Web2Code with Transformers:

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

pipe = pipeline("text-generation", model="LLM360/CrystalChat-7B-Web2Code", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("LLM360/CrystalChat-7B-Web2Code", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LLM360/CrystalChat-7B-Web2Code with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LLM360/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": "LLM360/CrystalChat-7B-Web2Code",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LLM360/CrystalChat-7B-Web2Code

SGLang

How to use LLM360/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 "LLM360/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": "LLM360/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 "LLM360/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": "LLM360/CrystalChat-7B-Web2Code",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LLM360/CrystalChat-7B-Web2Code with Docker Model Runner:
```
docker model run hf.co/LLM360/CrystalChat-7B-Web2Code
```

victormiller commited on Oct 22, 2024

Commit

fbe1b69

verified ·

1 Parent(s): 8c3697f

Update README.md

Browse files

Files changed (1) hide show

README.md +5 -4

README.md CHANGED Viewed

@@ -156,11 +156,12 @@ The dataset chosen was created by LLaVA with academic-task-oriented VQA data mix
 **Example 2: Recreate a webpage from an image**
-| ![Image 1](images2/ori.png) | ![Image 2](images2/crystralchat.png) |
-|:----------------------:|:----------------------:|
-| Hand Drawn Webpage  | CrystalChat-Web2Code Rendering |
 **Image 3:** Hand-drawn webpage input to CrystalChat-7B-Web2Code generated output.
@@ -184,7 +185,7 @@ model = AutoModelForCausalLM.from_pretrained(
-## LLM-360
 LLM-360 is an open research lab enabling community-owned AGI through open-source large model research and development.
 Crystal-based Models enables community-owned AGI by creating standards and tools to advance the bleeding edge of LLM capability and empower knowledge transfer, research, and development.

 **Example 2: Recreate a webpage from an image**
+Image 1: Original Webpage
+<center><img src="images2/ori.png" alt="k2 eval table" /></center>
+Image 2: CrystalChat-Web2Code Rendering
+<center><img src="images2/crystalchat.png" alt="k2 eval table" /></center>
 **Image 3:** Hand-drawn webpage input to CrystalChat-7B-Web2Code generated output.
+## LLM360
 LLM-360 is an open research lab enabling community-owned AGI through open-source large model research and development.
 Crystal-based Models enables community-owned AGI by creating standards and tools to advance the bleeding edge of LLM capability and empower knowledge transfer, research, and development.