Instructions to use qazimbhat1/Crystal-based-MLLM-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use qazimbhat1/Crystal-based-MLLM-7B with Transformers:

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

pipe = pipeline("text-generation", model="qazimbhat1/Crystal-based-MLLM-7B", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("qazimbhat1/Crystal-based-MLLM-7B", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use qazimbhat1/Crystal-based-MLLM-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "qazimbhat1/Crystal-based-MLLM-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "qazimbhat1/Crystal-based-MLLM-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/qazimbhat1/Crystal-based-MLLM-7B

SGLang

How to use qazimbhat1/Crystal-based-MLLM-7B 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 "qazimbhat1/Crystal-based-MLLM-7B" \
    --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": "qazimbhat1/Crystal-based-MLLM-7B",
		"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 "qazimbhat1/Crystal-based-MLLM-7B" \
        --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": "qazimbhat1/Crystal-based-MLLM-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use qazimbhat1/Crystal-based-MLLM-7B with Docker Model Runner:
```
docker model run hf.co/qazimbhat1/Crystal-based-MLLM-7B
```

qazimbhat1 commited on Jul 7, 2024

Commit

39f20db

verified ·

1 Parent(s): aa3ff79

Update README_code_model.md

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README_code_model.md +5 -4

README_code_model.md CHANGED Viewed

@@ -196,9 +196,10 @@ We believe in a future where artificial general intelligence (AGI) is created by
 **BibTeX:**
 ```bibtex
-@article{
-      title={},
-      author={},
-      year={},
 }
 ```

 **BibTeX:**
 ```bibtex
+@article{yun2024web2code,
+  title={Web2Code: A Large-scale Webpage-to-Code Dataset and Evaluation Framework for Multimodal LLMs},
+  author={Yun, Sukmin and Lin, Haokun and Thushara, Rusiru and Bhat, Mohammad Qazim and Wang, Yongxin and Jiang, Zutao and Deng, Mingkai and Wang, Jinhong and Tao, Tianhua and Li, Junbo and others},
+  journal={arXiv preprint arXiv:2406.20098},
+  year={2024}
 }
 ```