Instructions to use dcmutlu/gordon-ramsay-code-auditor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dcmutlu/gordon-ramsay-code-auditor with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-1.5B-Instruct")
model = PeftModel.from_pretrained(base_model, "dcmutlu/gordon-ramsay-code-auditor")

Transformers

How to use dcmutlu/gordon-ramsay-code-auditor with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="dcmutlu/gordon-ramsay-code-auditor")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("dcmutlu/gordon-ramsay-code-auditor", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use dcmutlu/gordon-ramsay-code-auditor with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dcmutlu/gordon-ramsay-code-auditor"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dcmutlu/gordon-ramsay-code-auditor",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/dcmutlu/gordon-ramsay-code-auditor

SGLang

How to use dcmutlu/gordon-ramsay-code-auditor 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 "dcmutlu/gordon-ramsay-code-auditor" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dcmutlu/gordon-ramsay-code-auditor",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "dcmutlu/gordon-ramsay-code-auditor" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dcmutlu/gordon-ramsay-code-auditor",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use dcmutlu/gordon-ramsay-code-auditor with Docker Model Runner:
```
docker model run hf.co/dcmutlu/gordon-ramsay-code-auditor
```

gordon-ramsay-code-auditor / README.md

dcmutlu

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	---
	base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
	library_name: peft
	pipeline_tag: text-generation
	tags:
	- colab-forge
	- lora
	- peft
	- sovereign-ai
	- transformers
	license: apache-2.0
	language:
	- en
	---

	# gordon-ramsay-code-auditor

	Autonomous fine-tuned model artifact synthesized via JESUS Sovereign Cloud Model Forge (`hf-colab-forge`).

	## Model Details

	- Base Model: [Qwen/Qwen2.5-Coder-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct)
	- Artifact Type: LoRA Adapter Weights
	- Fine-Tuning Method: QLoRA (NF4 4-bit / Double Quantization)
	- Execution Grid: Google Colab GPU (`Cloud Accelerator (NVIDIA)` )
	- Creation Date: 2026-08-30 02:53:43 UTC

	## Training Hyperparameters & Telemetry

	\| Parameter \| Value \|
	\| :--- \| :--- \|
	\| Base Model \| `Qwen/Qwen2.5-Coder-1.5B-Instruct` \|
	\| LoRA Rank ($r$) \| `N/A` \|
	\| LoRA Alpha ($\alpha$) \| `N/A` \|
	\| Epochs \| `N/A` \|
	\| Batch Size \| `N/A` (Gradient Accum: `4`) \|
	\| Learning Rate \| `N/A` \|
	\| Final Loss \| `N/A` \|
	\| Training Duration \| `N/As` \|

	## Quickstart & Inference

	### Python (`transformers` + `peft`)

	```python
	import torch
	from transformers import AutoModelForCausalLM, AutoTokenizer
	from peft import PeftModel

	base_model_id = "Qwen/Qwen2.5-Coder-1.5B-Instruct"
	adapter_id = "dcmutlu/gordon-ramsay-code-auditor"

	tokenizer = AutoTokenizer.from_pretrained(base_model_id)
	base_model = AutoModelForCausalLM.from_pretrained(
	base_model_id,
	torch_dtype=torch.float16,
	device_map="auto"
	)
	model = PeftModel.from_pretrained(base_model, adapter_id)

	prompt = "Triage user intent to the sovereign tool mesh."
	inputs = tokenizer(prompt, return_tensors="pt").to("cuda" if torch.cuda.is_available() else "cpu")
	outputs = model.generate(**inputs, max_new_tokens=256)
	print(tokenizer.decode(outputs[0], skip_special_tokens=True))
	```

	### Apple Silicon / Host Deployment (`llama.cpp` / GGUF)

	If utilizing GGUF weights on macOS (M-Series Metal acceleration):

	```bash
	# Run via llama.cpp
	llama-cli -m gordon-ramsay-code-auditor-Q4_K_M.gguf -p "Triage user intent:" -ngl 99 -c 2048
	```