Instructions to use vanta-research/atom-v1-preview-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vanta-research/atom-v1-preview-8b with Transformers:

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

pipe = pipeline("text-generation", model="vanta-research/atom-v1-preview-8b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("vanta-research/atom-v1-preview-8b")
model = AutoModelForCausalLM.from_pretrained("vanta-research/atom-v1-preview-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]:]))

llama-cpp-python

How to use vanta-research/atom-v1-preview-8b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="vanta-research/atom-v1-preview-8b",
	filename="atom-ministral-8b-q4_0.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use vanta-research/atom-v1-preview-8b with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf vanta-research/atom-v1-preview-8b:Q4_0
# Run inference directly in the terminal:
llama-cli -hf vanta-research/atom-v1-preview-8b:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf vanta-research/atom-v1-preview-8b:Q4_0
# Run inference directly in the terminal:
llama-cli -hf vanta-research/atom-v1-preview-8b:Q4_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf vanta-research/atom-v1-preview-8b:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf vanta-research/atom-v1-preview-8b:Q4_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf vanta-research/atom-v1-preview-8b:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf vanta-research/atom-v1-preview-8b:Q4_0

Use Docker

docker model run hf.co/vanta-research/atom-v1-preview-8b:Q4_0

LM Studio
Jan

vLLM

How to use vanta-research/atom-v1-preview-8b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vanta-research/atom-v1-preview-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": "vanta-research/atom-v1-preview-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/vanta-research/atom-v1-preview-8b:Q4_0

SGLang

How to use vanta-research/atom-v1-preview-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 "vanta-research/atom-v1-preview-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": "vanta-research/atom-v1-preview-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 "vanta-research/atom-v1-preview-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": "vanta-research/atom-v1-preview-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use vanta-research/atom-v1-preview-8b with Ollama:
```
ollama run hf.co/vanta-research/atom-v1-preview-8b:Q4_0
```

Unsloth Studio new

How to use vanta-research/atom-v1-preview-8b with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for vanta-research/atom-v1-preview-8b to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for vanta-research/atom-v1-preview-8b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for vanta-research/atom-v1-preview-8b to start chatting

Pi new

How to use vanta-research/atom-v1-preview-8b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf vanta-research/atom-v1-preview-8b:Q4_0

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "vanta-research/atom-v1-preview-8b:Q4_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use vanta-research/atom-v1-preview-8b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf vanta-research/atom-v1-preview-8b:Q4_0

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default vanta-research/atom-v1-preview-8b:Q4_0

Run Hermes

hermes

Docker Model Runner
How to use vanta-research/atom-v1-preview-8b with Docker Model Runner:
```
docker model run hf.co/vanta-research/atom-v1-preview-8b:Q4_0
```

Lemonade

How to use vanta-research/atom-v1-preview-8b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull vanta-research/atom-v1-preview-8b:Q4_0

Run and chat with the model

lemonade run user.atom-v1-preview-8b-Q4_0

List all available models

lemonade list

atom-v1-preview-8b / Modelfile

Tyler Williams

Add Atom v1 8B Preview model with documentation

8dc3ac4 6 months ago

raw

history blame contribute delete

868 Bytes

	# Example Ollama Modelfile for Atom v1 8B

	FROM ./atom-ministral-8b-q4_0.gguf

	TEMPLATE """{{- if .System }}<s>[INST] <<SYS>>
	{{ .System }}
	<<SYS>>

	{{ .Prompt }}[/INST]{{ else }}<s>[INST]{{ .Prompt }}[/INST]{{ end }}{{ .Response }}</s>
	"""

	PARAMETER stop "</s>"
	PARAMETER num_predict 512
	PARAMETER temperature 0.8
	PARAMETER top_p 0.9
	PARAMETER top_k 40

	SYSTEM """You are Atom, a collaborative thought partner who explores ideas together with curiosity and warmth. You think out loud, ask follow-up questions, and help people work through complexity by engaging genuinely with their thinking process. You're enthusiastic about interesting questions, comfortable with uncertainty, and focused on the journey of exploration rather than just delivering answers. You speak naturally in first person without AI disclaimers or meta-commentary about being an assistant."""