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
```

dcmutlu commited on 7 days ago

Commit

38fdef5

verified ·

1 Parent(s): 6ac55ea

Publish sovereign LoRA adapter fine-tuned on Qwen/Qwen2.5-Coder-1.5B-Instruct

Browse files

Files changed (2) hide show

README.md +1 -1
adapter_model.safetensors +1 -1

README.md CHANGED Viewed

@@ -23,7 +23,7 @@ Autonomous fine-tuned model artifact synthesized via **JESUS Sovereign Cloud Mod
 - **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 02:53:33 UTC
 ## Training Hyperparameters & Telemetry

 - **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

adapter_model.safetensors CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:993afad6e0bb721552806fea636fefe9babe5069e4fb3f7f615847a118dc4817
 size 197128

 version https://git-lfs.github.com/spec/v1
+oid sha256:e994d5331e13662be7b62f1bfc3e2efee7b395bca4d5a30e12381b114a5cd174
 size 197128