Instructions to use openvoid/prox-7b-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use openvoid/prox-7b-sft with Transformers:

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

pipe = pipeline("text-generation", model="openvoid/prox-7b-sft")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("openvoid/prox-7b-sft")
model = AutoModelForCausalLM.from_pretrained("openvoid/prox-7b-sft")
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 openvoid/prox-7b-sft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "openvoid/prox-7b-sft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "openvoid/prox-7b-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/openvoid/prox-7b-sft

SGLang

How to use openvoid/prox-7b-sft 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 "openvoid/prox-7b-sft" \
    --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": "openvoid/prox-7b-sft",
		"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 "openvoid/prox-7b-sft" \
        --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": "openvoid/prox-7b-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use openvoid/prox-7b-sft with Docker Model Runner:
```
docker model run hf.co/openvoid/prox-7b-sft
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Prox 7B SFT

By OpenVoid AI

Discord: https://discord.gg/CBDAbKkgNV

Model description

This model is based on Mistral-7b-v0.2 a new base model released by MistralAI on March 23, 2024 but they have not yet published on HuggingFace. It has been fine-tuned on a dataset containing information related to hacking and coding, with the aim of enhancing its performance on tasks within these domains.

Intended uses & limitations

This model is designed to assist with a variety of natural language processing tasks related to hacking and coding, such as:

Code generation
Code explanation and documentation
Answering questions about hacking techniques and cybersecurity
Providing insights and suggestions for coding projects

However, it is important to note that while the model has been fine-tuned on hacking and coding data, it should not be relied upon for critical security applications or used to engage in illegal activities. The model's outputs should be carefully reviewed and verified by experts in the relevant fields before being put into practice.

Training data

The prox-7b model was fine-tuned on a proprietary dataset curated by OpenVoid AI, which consists of a diverse range of hacking and coding-related content.

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
Number of devices: 8
Gradient accumulation steps: 4
Total train batch size: 64
Total eval batch size: 16
Optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
LR scheduler type: cosine
LR scheduler warmup steps: 10
Number of epochs: 4

The training was performed using a distributed multi-GPU setup to accelerate the process and handle the large model size.

Downloads last month: 5

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for openvoid/prox-7b-sft

Quantizations

2 models