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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	---
	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

	```json
	{
	"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:

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

	Or use the included modeling wrapper:

	```python
	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

	1. Outer model gathers repo context.
	2. Sidecar runs deterministic tools and Qwen guidance.
	3. Sidecar returns compact JSON with `tool_signals`.
	4. Outer model treats that JSON as internal control data.
	5. 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.