Instructions to use ayjays132/Phillnet-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

Phillnet-2 / CodeGuidanceSystem /README.md

ayjays132

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metadata

license: mit
base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
library_name: transformers
tags:
  - code
  - qwen
  - guidance
  - sidecar
  - orchestration

Qwen Coder Guidance Sidecar

This bundle describes a lightweight local coding guidance component for larger model orchestration. It is upload-ready as a Hugging Face model/runtime adapter package. It does not include training data.

It uses Qwen/Qwen2.5-Coder-1.5B-Instruct as a fast sidecar that returns compact JSON guidance instead of final user-facing prose. The larger model can call it before or during implementation to get route decisions, repo facts, patch constraints, risks, and tests.

Output Contract

{
  "route": "plan|patch|critique|test|fix",
  "confidence": 0.0,
  "useful_context": ["short facts the outer model should keep"],
  "plan": ["ordered implementation steps"],
  "patch_rules": ["constraints for the patch"],
  "risks": ["likely bugs or failure modes"],
  "tests": ["commands or cases to run"],
  "next_action": "one concise next action"
}

Runtime

Default model: Qwen/Qwen2.5-Coder-1.5B-Instruct
Included local model path: base_model/
Recommended inference: 4-bit NF4, bf16 compute, CUDA
Cache behavior: disk cache by request hash
Memory behavior: unload after call by default
Package entrypoint: guidance_sidecar.GuidanceEngine
Deterministic tools: route hint, context stats, risk scan, test suggestions, patch rules, simple calculations, Python syntax probe

Use

Run directly from the uploaded/downloaded folder:

python run_guidance.py --task "Plan a safe config-loader refactor" --context "Python package with tests."

Or use the included modeling wrapper:

from modeling_guidance_sidecar import GuidanceSidecar

guide = GuidanceSidecar("./qwen-coder-guidance-sidecar")
result = guide("Refactor the config loader safely", context="Python package with tests.")

The repo also includes the full guidance_sidecar/ runtime package, so it can be used without installing the source project separately.

Larger-Model Route

Outer model gathers repo context.
Sidecar runs deterministic tools and Qwen guidance.
Sidecar returns compact JSON with tool_signals.
Outer model treats that JSON as internal control data.
Outer model patches code, runs tests, and may call sidecar again for critique/fix routing.

The sidecar is designed to help a larger model, not replace it.