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 Settings

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 / AgenticScaffold /diff_utils.py

ayjays132

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	import re

	def validate_diff(diff_text: str) -> bool:
	"""
	Surgical Patch Validator.
	Checks for the presence of standard unified diff headers and basic hunk integrity.
	"""
	if not diff_text or not diff_text.strip():
	return False

	# Look for the hunk header: @@ -L,C +L,C @@
	hunk_header_regex = r"^@@ -\d+,\d+ \+\d+,\d+ @@"

	lines = diff_text.splitlines()
	has_header = False

	for line in lines:
	if re.match(hunk_header_regex, line):
	has_header = True
	break

	# Top-tier AGI validation: Also ensure it doesn't just contain deletions if we expect edits
	# But for a general validator, just checking the header is the primary structural requirement.

	return has_header

	def parse_surgical_diff(diff_text: str):
	"""
	Parses a surgical patch into manageable hunks.
	"""
	hunks = []
	current_hunk = None

	hunk_header_regex = r"^@@ -(\d+),(\d+) \+(\d+),(\d+) @@"

	for line in diff_text.splitlines():
	match = re.match(hunk_header_regex, line)
	if match:
	if current_hunk:
	hunks.append(current_hunk)
	current_hunk = {
	"old_start": int(match.group(1)),
	"old_count": int(match.group(2)),
	"new_start": int(match.group(3)),
	"new_count": int(match.group(4)),
	"lines": []
	}
	elif current_hunk:
	current_hunk["lines"].append(line)

	if current_hunk:
	hunks.append(current_hunk)

	return hunks