Text Generation
Transformers
Safetensors
English
lfm2
cybersecurity
security
ner
threat-intelligence
phishing
http
fine-tuned
small-language-model
conversational
Eval Results (legacy)
Instructions to use YoungHungGayGymBoy/Cyber-Prime-1-2.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YoungHungGayGymBoy/Cyber-Prime-1-2.6B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="YoungHungGayGymBoy/Cyber-Prime-1-2.6B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("YoungHungGayGymBoy/Cyber-Prime-1-2.6B") model = AutoModelForCausalLM.from_pretrained("YoungHungGayGymBoy/Cyber-Prime-1-2.6B", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.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=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use YoungHungGayGymBoy/Cyber-Prime-1-2.6B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "YoungHungGayGymBoy/Cyber-Prime-1-2.6B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "YoungHungGayGymBoy/Cyber-Prime-1-2.6B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/YoungHungGayGymBoy/Cyber-Prime-1-2.6B
- SGLang
How to use YoungHungGayGymBoy/Cyber-Prime-1-2.6B 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 "YoungHungGayGymBoy/Cyber-Prime-1-2.6B" \ --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": "YoungHungGayGymBoy/Cyber-Prime-1-2.6B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "YoungHungGayGymBoy/Cyber-Prime-1-2.6B" \ --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": "YoungHungGayGymBoy/Cyber-Prime-1-2.6B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use YoungHungGayGymBoy/Cyber-Prime-1-2.6B with Docker Model Runner:
docker model run hf.co/YoungHungGayGymBoy/Cyber-Prime-1-2.6B

- Xet hash:
- db7b08af230657b2ff6f64f4917d9253c12b952aab076d533393336cdb2eb219
- Size of remote file:
- 296 kB
- SHA256:
- 4ab0a6cc65d9c4740a8cd8ce69e0f8c16e52f0261cbcff11cc8f1fb813484930
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