Text Generation
Transformers
Diffusers
Safetensors
English
gpt_oss
phillnet-2
gpt-oss
multimodal
image-generation
video-generation
speech
audio
custom-code
conversational
custom_code
Instructions to use ayjays132/Phillnet-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ayjays132/Phillnet-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ayjays132/Phillnet-2", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ayjays132/Phillnet-2", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("ayjays132/Phillnet-2", trust_remote_code=True) 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
- vLLM
How to use ayjays132/Phillnet-2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ayjays132/Phillnet-2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ayjays132/Phillnet-2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ayjays132/Phillnet-2
- SGLang
How to use ayjays132/Phillnet-2 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 "ayjays132/Phillnet-2" \ --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": "ayjays132/Phillnet-2", "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 "ayjays132/Phillnet-2" \ --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": "ayjays132/Phillnet-2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use ayjays132/Phillnet-2 with Docker Model Runner:
docker model run hf.co/ayjays132/Phillnet-2
| export class SchemaValidator { | |
| /** | |
| * Validates data against a JSON schema. | |
| * @returns null if valid, or a string describing the error if invalid. | |
| */ | |
| static validate(schema: any, data: any): string | null { | |
| if (!schema) return null; | |
| // Handle cases where schema is just a string (name of type) | |
| const type = typeof schema === 'string' ? schema : schema.type; | |
| if (type === 'object') { | |
| if (typeof data !== 'object' || data === null || Array.isArray(data)) { | |
| return `Expected object, got ${typeof data}`; | |
| } | |
| const properties = schema.properties || {}; | |
| const required = schema.required || []; | |
| // Check required properties | |
| for (const req of required) { | |
| if (!(req in data)) { | |
| return `Missing required property: ${req}`; | |
| } | |
| } | |
| // Recursively validate properties | |
| for (const key in data) { | |
| if (properties[key]) { | |
| const error = this.validate(properties[key], data[key]); | |
| if (error) return `${key}: ${error}`; | |
| } | |
| } | |
| } else if (type === 'array') { | |
| if (!Array.isArray(data)) { | |
| return `Expected array, got ${typeof data}`; | |
| } | |
| if (schema.items) { | |
| for (let i = 0; i < data.length; i++) { | |
| const error = this.validate(schema.items, data[i]); | |
| if (error) return `item[${i}]: ${error}`; | |
| } | |
| } | |
| } else if (type === 'string') { | |
| if (typeof data !== 'string') return `Expected string, got ${typeof data}`; | |
| if (schema.enum && !schema.enum.includes(data)) { | |
| return `Value "${data}" not in enum [${schema.enum.join(', ')}]`; | |
| } | |
| } else if (type === 'number' || type === 'integer') { | |
| if (typeof data !== 'number') return `Expected number, got ${typeof data}`; | |
| } else if (type === 'boolean') { | |
| if (typeof data !== 'boolean') return `Expected boolean, got ${typeof data}`; | |
| } | |
| return null; | |
| } | |
| } | |