Image-Text-to-Text
PEFT
Safetensors
MLX
GGUF
English
ui-grounding
screen-grounding
browser-agent
claude-computer-use
codex
browser-use
skyvern
hybrid-ai
compound-ai
specialist-model
lora
ollama
apple-silicon
qwen3-vl
gpt-4v-alternative
cost-effective-ai
conversational
Instructions to use renezander030/browserground with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use renezander030/browserground with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-VL-2B-Instruct") model = PeftModel.from_pretrained(base_model, "renezander030/browserground") - MLX
How to use renezander030/browserground with MLX:
# Make sure mlx-vlm is installed # pip install --upgrade mlx-vlm from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config # Load the model model, processor = load("renezander030/browserground") config = load_config("renezander030/browserground") # Prepare input image = ["http://images.cocodataset.org/val2017/000000039769.jpg"] prompt = "Describe this image." # Apply chat template formatted_prompt = apply_chat_template( processor, config, prompt, num_images=1 ) # Generate output output = generate(model, processor, formatted_prompt, image) print(output) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
- Pi new
How to use renezander030/browserground with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "renezander030/browserground"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "renezander030/browserground" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use renezander030/browserground with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "renezander030/browserground"
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 renezander030/browserground
Run Hermes
hermes
v0.2 — Tier 2 LoRA r32, 26k mixed-domain examples incl. browser, ScreenSpot-v2 60.0%
ad37a63 verified | { | |
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| "alpha_pattern": {}, | |
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| "auto_mapping": null, | |
| "base_model_name_or_path": "Qwen/Qwen3-VL-2B-Instruct", | |
| "bias": "none", | |
| "corda_config": null, | |
| "ensure_weight_tying": false, | |
| "eva_config": null, | |
| "exclude_modules": null, | |
| "fan_in_fan_out": false, | |
| "inference_mode": true, | |
| "init_lora_weights": true, | |
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| "layers_pattern": null, | |
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| "loftq_config": {}, | |
| "lora_alpha": 64, | |
| "lora_bias": false, | |
| "lora_dropout": 0.05, | |
| "lora_ga_config": null, | |
| "megatron_config": null, | |
| "megatron_core": "megatron.core", | |
| "modules_to_save": null, | |
| "peft_type": "LORA", | |
| "peft_version": "0.19.1", | |
| "qalora_group_size": 16, | |
| "r": 32, | |
| "rank_pattern": {}, | |
| "revision": null, | |
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| "k_proj", | |
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| "target_parameters": null, | |
| "task_type": "CAUSAL_LM", | |
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| "use_bdlora": null, | |
| "use_dora": false, | |
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| } |