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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metadata

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
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`)

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):

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