Instructions to use saucam/Proteus-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use saucam/Proteus-8B with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("saucam/Proteus-8B")
model = AutoModelForCausalLM.from_pretrained("saucam/Proteus-8B")
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 saucam/Proteus-8B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/saucam/Proteus-8B

SGLang

How to use saucam/Proteus-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 "saucam/Proteus-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": "saucam/Proteus-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 "saucam/Proteus-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": "saucam/Proteus-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use saucam/Proteus-8B with Docker Model Runner:
```
docker model run hf.co/saucam/Proteus-8B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

💧 Proteus-8B

Proteus-8B is a merge of the following models using Mergekit:

🧩 Configuration

tokenizer_source: union
embed_slerp: true
name: Proteus-8B
models:
  - model: cognitivecomputations/dolphin-2.9-llama3-8b
    parameters:
      density: 0.5
      weight: 0.4
  - model: NousResearch/Hermes-2-Theta-Llama-3-8B
    parameters:
      density: 0.5
      weight: 0.6
merge_method: dare_ties
base_model: NousResearch/Hermes-2-Theta-Llama-3-8B
parameters:
  int8_mask: true
dtype: bfloat16

Eval Results

Benchmark	Average	arc	gsm8k	hellaswag	mmlu	truthfulqa	winogrande
openllm	70.67	63.48	78.77	82.94	64.71	56.71	77.43

Detailed Results: https://github.com/saucam/model_evals/blob/main/saucam/Proteus-8B/README.md

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "saucam/Proteus-8B"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

Downloads last month: 4

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for saucam/Proteus-8B

NousResearch/Hermes-2-Theta-Llama-3-8B

dphn/dolphin-2.9-llama3-8b

Merge model

this model

Quantizations

1 model