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
File size: 1,104 Bytes
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"alora_invocation_tokens": null,
"alpha_pattern": {},
"arrow_config": null,
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"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,
"layer_replication": null,
"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,
"target_modules": [
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"v_proj",
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],
"target_parameters": null,
"task_type": "CAUSAL_LM",
"trainable_token_indices": null,
"use_bdlora": null,
"use_dora": false,
"use_qalora": false,
"use_rslora": false
} |