Instructions to use braindao/Enhanced-Slither-Auditor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use braindao/Enhanced-Slither-Auditor with Transformers:

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

pipe = pipeline("text-generation", model="braindao/Enhanced-Slither-Auditor")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("braindao/Enhanced-Slither-Auditor")
model = AutoModelForCausalLM.from_pretrained("braindao/Enhanced-Slither-Auditor")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use braindao/Enhanced-Slither-Auditor with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "braindao/Enhanced-Slither-Auditor"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "braindao/Enhanced-Slither-Auditor",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/braindao/Enhanced-Slither-Auditor

SGLang

How to use braindao/Enhanced-Slither-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 "braindao/Enhanced-Slither-Auditor" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "braindao/Enhanced-Slither-Auditor",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "braindao/Enhanced-Slither-Auditor" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "braindao/Enhanced-Slither-Auditor",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use braindao/Enhanced-Slither-Auditor with Docker Model Runner:
```
docker model run hf.co/braindao/Enhanced-Slither-Auditor
```

Enhanced Slither Auditor

This model is a fine-tuned version of teknium/OpenHermes-2.5-Mistral-7B on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.1923

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-06
train_batch_size: 2
eval_batch_size: 2
seed: 42
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 4
total_train_batch_size: 16
total_eval_batch_size: 4
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
1.1498	0.0	1	1.1953
0.321	0.1	31	0.3176
0.2693	0.2	62	0.2712
0.2701	0.31	93	0.2523
0.27	0.41	124	0.2362
0.2244	0.51	155	0.2284
0.2227	0.61	186	0.2260
0.2167	0.71	217	0.2171
0.2098	0.81	248	0.2082
0.1842	0.92	279	0.2047
0.1917	1.02	310	0.2013
0.1639	1.12	341	0.1982
0.1835	1.22	372	0.1968
0.1666	1.32	403	0.1953
0.1694	1.43	434	0.1932
0.1461	1.53	465	0.1929
0.1535	1.63	496	0.1927
0.1419	1.73	527	0.1925
0.1612	1.83	558	0.1923
0.1857	1.93	589	0.1923

Framework versions

Transformers 4.34.1
Pytorch 2.0.1+cu118
Datasets 2.14.6
Tokenizers 0.14.1

Downloads last month: 7

Model tree for braindao/Enhanced-Slither-Auditor

Base model

mistralai/Mistral-7B-v0.1

Finetuned

teknium/OpenHermes-2.5-Mistral-7B

Finetuned

(85)

this model

braindao
/

Enhanced-Slither-Auditor

Enhanced Slither Auditor

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for braindao/Enhanced-Slither-Auditor

Space using braindao/Enhanced-Slither-Auditor 1