Instructions to use openvoid/Prox-Llama-3-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use openvoid/Prox-Llama-3-8B with Transformers:

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

pipe = pipeline("text-generation", model="openvoid/Prox-Llama-3-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("openvoid/Prox-Llama-3-8B")
model = AutoModelForCausalLM.from_pretrained("openvoid/Prox-Llama-3-8B", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use openvoid/Prox-Llama-3-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "openvoid/Prox-Llama-3-8B"
# 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-Llama-3-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/openvoid/Prox-Llama-3-8B

SGLang

How to use openvoid/Prox-Llama-3-8B 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-Llama-3-8B" \
    --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-Llama-3-8B",
		"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-Llama-3-8B" \
        --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-Llama-3-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use openvoid/Prox-Llama-3-8B with Docker Model Runner:
```
docker model run hf.co/openvoid/Prox-Llama-3-8B
```

preemware commited on Jun 20, 2024

Commit

d75a41a

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+---
+license: apache-2.0
+language:
+- en
+tags:
+- code
+- cybersecurity
+- penetration testing
+- hacking
+- code
+- uncensored
+---
+# Prox-Llama-3-8B
+By [OpenVoid](https://openvoid.ai)
+<img src="https://cdn.openvoid.ai/images/prox-llama3.png" width="500" />
+## Model Description
+Prox-Llama-3-8B is a uncensored fine-tune of Meta-Llama-3-8B-Instruct, tailored for specialized applications in code generation and cybersecurity.
+## Intended Uses & Limitations
+Designed for tasks related to hacking and coding:
+- Code generation
+- Code explanation and documentation
+- Answering questions on hacking techniques and cybersecurity
+- Providing coding project insights
+Review and verify outputs carefully, especially for critical applications. Expert validation is recommended to avoid biased or inconsistent content. Use responsibly and ethically, complying with applicable laws and regulations to prevent misuse for malicious purposes.
+## Training Data
+The model was fine-tuned on a proprietary dataset from OpenVoid, featuring high-quality text data related to coding, cybersecurity, and hacking. Extensive filtering and preprocessing ensured data quality and relevance.
+## Evaluation
+- **HumanEval v1.0**: pass@1: 0.561
+- **EvalPlus v1.1**: pass@1: 0.500
+- **AGIEval**: 40.74
+- **GPT4All+**: 70.17
+- **TruthfulQA+**: 51.15
+- **Bigbench+**: 44.12
+- **Average**: 51.55
+## How to Use the Model
+### Using Transformers
+Example of using Prox-Llama-3-8B with the Transformers library:
+```python
+import transformers
+import torch
+model_id = "openvoid/Prox-Llama-3-8B"
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model_id,
+    model_kwargs={"torch_dtype": torch.bfloat16},
+    device_map="auto",
+)
+messages = [
+    {"role": "system", "content": "You are Prox."},
+    {"role": "user", "content": "Who are you?"},
+]
+terminators = [
+    pipeline.tokenizer.eos_token_id,
+    pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
+]
+outputs = pipeline(
+    messages,
+    max_new_tokens=256,
+    eos_token_id=terminators,
+    do_sample=True,
+    temperature=0.6,
+    top_p=0.9,
+)
+print(outputs[0]["generated_text"][-1])
+```