Instructions to use zhijunjunlin/github_cybersecurity_READMEs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zhijunjunlin/github_cybersecurity_READMEs with Transformers:

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

pipe = pipeline("text-generation", model="zhijunjunlin/github_cybersecurity_READMEs")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("zhijunjunlin/github_cybersecurity_READMEs")
model = AutoModelForCausalLM.from_pretrained("zhijunjunlin/github_cybersecurity_READMEs")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use zhijunjunlin/github_cybersecurity_READMEs with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/zhijunjunlin/github_cybersecurity_READMEs

SGLang

How to use zhijunjunlin/github_cybersecurity_READMEs 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 "zhijunjunlin/github_cybersecurity_READMEs" \
    --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": "zhijunjunlin/github_cybersecurity_READMEs",
		"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 "zhijunjunlin/github_cybersecurity_READMEs" \
        --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": "zhijunjunlin/github_cybersecurity_READMEs",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use zhijunjunlin/github_cybersecurity_READMEs with Docker Model Runner:
```
docker model run hf.co/zhijunjunlin/github_cybersecurity_READMEs
```

zhijunjunlin commited on Apr 5, 2024

Commit

2748c46

verified ·

1 Parent(s): f5719fa

Training in progress, step 8000

Browse files

Files changed (2) hide show

model.safetensors +1 -1
runs/Apr05_02-05-27_DESKTOP-RHJ1ESI/events.out.tfevents.1712253943.DESKTOP-RHJ1ESI.208972.0 +2 -2

model.safetensors CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:91f472026076807ded83e445c0a79f4c380a1e95fa83fca6191b1c29bfae33f7
 size 327657928

 version https://git-lfs.github.com/spec/v1
+oid sha256:4c3d7b8c159ee358b8a3dda3f03e8e09b09f01aca58c2f2d7c8a94793c6eeddb
 size 327657928

runs/Apr05_02-05-27_DESKTOP-RHJ1ESI/events.out.tfevents.1712253943.DESKTOP-RHJ1ESI.208972.0 CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:b5175ea8ef26384e348be3feca5fb0f42b9e702033d656ec2be898a59ec23600
-size 8241

 version https://git-lfs.github.com/spec/v1
+oid sha256:ed816211faa7b6893e81f41db8aa4cfab78a74c5e188d12d0ec0ac28d694e4d5
+size 8452