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
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101858b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | import argparse
import json
from pathlib import Path
from guidance_sidecar import GuidanceEngine, GuidanceSettings
def main() -> None:
parser = argparse.ArgumentParser(description="Local Hugging Face coding guidance sidecar")
subparsers = parser.add_subparsers(dest="command", required=True)
guide_parser = subparsers.add_parser("guide", help="Ask the local guidance model for structured coding help")
guide_parser.add_argument("--task", required=True)
guide_parser.add_argument("--context", default="")
guide_parser.add_argument("--project", action="store_true")
guide_parser.add_argument("--root", default=".")
guide_parser.add_argument("--model", default=None)
guide_parser.add_argument("--adapter", default=None)
guide_parser.add_argument("--bundle", default=None, help="Path to an HF bundle containing base_model/")
guide_parser.add_argument("--keep-loaded-seconds", type=int, default=None)
guide_parser.add_argument("--no-cache", action="store_true")
smoke_parser = subparsers.add_parser("smoke", help="Run one guidance request and save JSON result")
smoke_parser.add_argument("--task", default="Add input validation and tests for a Python function without changing public API.")
smoke_parser.add_argument("--output", default="./runtime/guidance_smoke_result.json")
args = parser.parse_args()
if args.command == "guide":
if args.bundle:
engine = GuidanceEngine.from_bundle(args.bundle)
else:
engine = GuidanceEngine(
model_name=args.model,
adapter_dir=args.adapter,
keep_loaded_seconds=args.keep_loaded_seconds,
)
result = (
engine.advise_project(args.task, root=args.root, use_cache=not args.no_cache)
if args.project
else engine.advise(args.task, args.context, use_cache=not args.no_cache)
)
print(json.dumps(result, indent=2))
elif args.command == "smoke":
engine = GuidanceEngine(GuidanceSettings(max_new_tokens=384))
result = engine.advise_project(args.task)
output = Path(args.output)
output.parent.mkdir(parents=True, exist_ok=True)
output.write_text(json.dumps(result, indent=2), encoding="utf-8")
print(json.dumps(result, indent=2))
if __name__ == "__main__":
main()
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