Instructions to use Royal-lobster/Slither-LLM-Auditor-LoRA-Adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Royal-lobster/Slither-LLM-Auditor-LoRA-Adapter with Transformers:

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

pipe = pipeline("text-generation", model="Royal-lobster/Slither-LLM-Auditor-LoRA-Adapter")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Royal-lobster/Slither-LLM-Auditor-LoRA-Adapter")
model = AutoModelForCausalLM.from_pretrained("Royal-lobster/Slither-LLM-Auditor-LoRA-Adapter")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Royal-lobster/Slither-LLM-Auditor-LoRA-Adapter with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Royal-lobster/Slither-LLM-Auditor-LoRA-Adapter

SGLang

How to use Royal-lobster/Slither-LLM-Auditor-LoRA-Adapter 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 "Royal-lobster/Slither-LLM-Auditor-LoRA-Adapter" \
    --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": "Royal-lobster/Slither-LLM-Auditor-LoRA-Adapter",
		"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 "Royal-lobster/Slither-LLM-Auditor-LoRA-Adapter" \
        --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": "Royal-lobster/Slither-LLM-Auditor-LoRA-Adapter",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Royal-lobster/Slither-LLM-Auditor-LoRA-Adapter with Docker Model Runner:
```
docker model run hf.co/Royal-lobster/Slither-LLM-Auditor-LoRA-Adapter
```

Slither Auditor

This model is a fine-tuned version of Phind/Phind-CodeLlama-34B-v2 on the Royal-lobster/Slither-Audited-Solidity-QA dataset.

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 6
gradient_accumulation_steps: 3
total_train_batch_size: 18
total_eval_batch_size: 6
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 10
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss
0.2069	0.1	20	nan
0.0986	0.21	40	nan
0.1101	0.31	60	nan
0.072	0.41	80	nan
0.1258	0.52	100	nan
0.0675	0.62	120	nan
0.0728	0.72	140	nan
0.115	0.83	160	nan
0.0769	0.93	180	nan
0.0609	1.03	200	nan
0.0881	1.14	220	nan
0.0674	1.24	240	nan
0.0476	1.34	260	nan
0.0259	1.45	280	nan
0.0534	1.55	300	nan
0.0449	1.65	320	nan
0.0325	1.76	340	nan
0.03	1.86	360	nan
0.0416	1.96	380	nan

Framework versions

Transformers 4.35.0.dev0
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.14.1

Downloads last month: 7

Model tree for Royal-lobster/Slither-LLM-Auditor-LoRA-Adapter

Base model

Phind/Phind-CodeLlama-34B-v2

Quantized

(7)

this model