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")

# pip install -U transformers accelerate
# 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 / adapter_config.json

dcmutlu

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

6ac55ea verified about 2 months ago

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530 Bytes

	{
	"base_model_name_or_path": "Qwen/Qwen2.5-Coder-1.5B-Instruct",
	"bias": "none",
	"fan_in_fan_out": false,
	"inference_mode": true,
	"init_lora_weights": true,
	"layers_pattern": null,
	"layers_to_transform": null,
	"lora_alpha": 32,
	"lora_dropout": 0.05,
	"modules_to_save": null,
	"peft_type": "LORA",
	"r": 16,
	"rank_pattern": {},
	"revision": null,
	"target_modules": [
	"q_proj",
	"k_proj",
	"v_proj",
	"o_proj",
	"gate_proj",
	"up_proj",
	"down_proj"
	],
	"task_type": "CAUSAL_LM"
	}