Instructions to use OTA-AI/OTA-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OTA-AI/OTA-v1 with Transformers:

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

pipe = pipeline("text-generation", model="OTA-AI/OTA-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OTA-AI/OTA-v1")
model = AutoModelForCausalLM.from_pretrained("OTA-AI/OTA-v1")
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 OTA-AI/OTA-v1 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="OTA-AI/OTA-v1",
	filename="OTA_v1.Q8_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 OTA-AI/OTA-v1 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf OTA-AI/OTA-v1:Q8_0
# Run inference directly in the terminal:
llama-cli -hf OTA-AI/OTA-v1:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf OTA-AI/OTA-v1:Q8_0
# Run inference directly in the terminal:
llama-cli -hf OTA-AI/OTA-v1:Q8_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 OTA-AI/OTA-v1:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf OTA-AI/OTA-v1:Q8_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 OTA-AI/OTA-v1:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf OTA-AI/OTA-v1:Q8_0

Use Docker

docker model run hf.co/OTA-AI/OTA-v1:Q8_0

LM Studio
Jan

vLLM

How to use OTA-AI/OTA-v1 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/OTA-AI/OTA-v1:Q8_0

SGLang

How to use OTA-AI/OTA-v1 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 "OTA-AI/OTA-v1" \
    --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": "OTA-AI/OTA-v1",
		"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 "OTA-AI/OTA-v1" \
        --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": "OTA-AI/OTA-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use OTA-AI/OTA-v1 with Ollama:
```
ollama run hf.co/OTA-AI/OTA-v1:Q8_0
```

Unsloth Studio new

How to use OTA-AI/OTA-v1 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 OTA-AI/OTA-v1 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 OTA-AI/OTA-v1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for OTA-AI/OTA-v1 to start chatting

Pi new

How to use OTA-AI/OTA-v1 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf OTA-AI/OTA-v1:Q8_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": "OTA-v1"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use OTA-AI/OTA-v1 with Docker Model Runner:
```
docker model run hf.co/OTA-AI/OTA-v1:Q8_0
```

Lemonade

How to use OTA-AI/OTA-v1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull OTA-AI/OTA-v1:Q8_0

Run and chat with the model

lemonade run user.OTA-v1-Q8_0

List all available models

lemonade list

Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

OTA-v1

Introduction

OTA-v1 is a specialized Browser Agent Model (BAM) fine-tuned from the Qwen2.5-14B base model. Designed to excel in controlling browser environments, OTA-v1 leverages frameworks like browser-use to perform automated browser tasks with high precision. Unlike traditional instruction-tuned models, OTA-v1 is optimized for reasoning and tool use within browser contexts, making it a powerful tool for web automation and interaction.

Features

Cost-Efficient Deployment:
- Optimized for consumer-grade GPUs (NVIDIA 3090/4090) with 16-bit precision (20GB VRAM) and 4-bit quantization (10GB VRAM)
- Enabling local execution without cloud dependencies
Multi-step Planning Engine:
- Automatically decomposes complex tasks into executable action sequences
- Implements conditional logic for error recovery and retry mechanisms
- Maintains state awareness across browser sessions (tabs/windows)
Precision Tool Utilization:
- Native support for browser agent frameworks (browser-use)
- Automatic detection of interactive elements and form fields
Long-context Optimization:
- Processes full-page DOM structures (up to 128K tokens)
- YARN-enhanced attention patterns for efficient HTML traversal
- Context-aware element resolution within dynamic web applications
Structured Execution: Generates battle-tested tool use instructions with:
- Formatted tool use output under long context
- Self correction based on previous action history

Quickstart

Here provides a code snippet with apply_chat_template to show you how to load the tokenizer and model and how to generate contents.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "OTA-AI/OTA-v1"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Give me a short introduction to large language model."
messages = [
    {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

Citation

If you find our work helpful, feel free to give us a cite.

@misc{OTA-v1,
    title = {OTA-v1: First Browser Agent Model},
    url = {https://huggingface.co/OTA-AI/OTA-v1/},
    author = {Shaoheng Wang, Jianyang Wu},
    month = {March},
    year = {2025}
}

Downloads last month: 26

Safetensors

Model size

15B params

Tensor type

BF16

Model tree for OTA-AI/OTA-v1

Quantizations

2 models