Mistral-7B-Instruct Playwright MCP
A fine-tuned LoRA adapter for Mistral-7B-Instruct-v0.2, optimized for Playwright MCP (Model Context Protocol) tool-calling tasks. This model enables AI assistants to interact with web pages through browser automation tools.
Model Details
Model Description
This is a LoRA (Low-Rank Adaptation) adapter fine-tuned on Mistral-7B-Instruct-v0.2 for Playwright MCP tool-calling tasks. The model has been trained to understand and generate appropriate tool calls for browser automation, including navigation, clicking, form filling, text extraction, and screenshot capture.
- Developed by: [Your Name/Organization]
- Model type: Causal Language Model with LoRA adapter
- Language(s) (NLP): English
- License: Apache 2.0 (inherited from base model)
- Finetuned from model: mistralai/Mistral-7B-Instruct-v0.2
Model Sources
Uses
Direct Use
This model is designed to be used with Playwright MCP for browser automation tasks. It can:
- Navigate to web pages
- Click on elements
- Fill forms
- Extract text from pages
- Take screenshots
- Wait for elements to appear
- Get accessibility trees
Example Usage
hon
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
Load base model
base_model = AutoModelForCausalLM.from_pretrained(
"mistralai/Mistral-7B-Instruct-v0.2",
torch_dtype=torch.float16,
device_map="auto"
)
Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "your-username/finetuned-mistral-7b-playwright-mcp")
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
Use with Playwright MCP tools
messages = [
{"role": "system", "content": "You are an AI assistant with Playwright MCP tools."},
{"role": "user", "content": "Navigate to example.com and get the main heading"}
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)### Out-of-Scope Use
This model is specifically fine-tuned for Playwright MCP tool-calling. It may not perform well for:
- General conversation without tool-calling context
- Tasks unrelated to browser automation
- Non-English languages
Bias, Risks, and Limitations
- The model inherits biases from the base Mistral-7B-Instruct model
- Browser automation can be used for malicious purposes (scraping, automation abuse)
- The model may generate incorrect tool calls that could lead to unintended web interactions
- Always validate and sanitize tool calls before execution in production environments
Recommendations
Users should:
- Implement proper validation and sandboxing for tool execution
- Monitor and log all browser automation activities
- Use rate limiting to prevent abuse
- Respect website terms of service and robots.txt
How to Get Started with the Model
Installation
pip install transformers peft torch### Quick Start
hon
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
Load model with LoRA adapter
base_model = AutoModelForCausalLM.from_pretrained(
"mistralai/Mistral-7B-Instruct-v0.2",
torch_dtype=torch.float16,
device_map="auto"
)
model = PeftModel.from_pretrained(base_model, "your-username/finetuned-mistral-7b-playwright-mcp")
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")## Training Details
Training Data
The model was fine-tuned on a dataset of Playwright MCP tool-calling examples in chat format, including:
- Navigation tasks
- Form interactions
- Text extraction
- Screenshot capture
- Multi-step browser automation workflows
Data format: JSONL files with chat messages (system, user, assistant roles).
Training Procedure
Preprocessing
- Data converted to chat message format
- Full conversations tokenized using Mistral's chat template
- Labels masked so loss is computed only on assistant responses
Training Hyperparameters
- Training regime: FP16 mixed precision
- Epochs: 3
- Learning rate: 2.0e-4
- Batch size: 1 per device
- Gradient accumulation steps: 16
- Effective batch size: 16
- Max sequence length: 1024 tokens
- Warmup steps: 100
- Evaluation strategy: Steps (every 500 steps)
- Save strategy: Steps (every 500 steps, keep best 3)
LoRA Configuration
- Method: LoRA (Low-Rank Adaptation)
- Rank (r): 32
- Alpha: 64
- Dropout: 0.05
- Target modules:
["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]
- Quantization: 8-bit (BitsAndBytes)
Speeds, Sizes, Times
- Adapter size: ~130 MB (adapter_model.safetensors)
- Base model: Mistral-7B-Instruct-v0.2 (~14GB in FP16)
- Training time: ~1-3 hours per epoch (depends on dataset size and hardware)
- Optimized for: 16GB VRAM GPUs
Evaluation
Testing Data, Factors & Metrics
- Testing Data: Held-out test set from training data
- Metrics: Validation loss, generation quality on tool-calling tasks
Results
The model shows improved performance on Playwright MCP tool-calling tasks compared to the base model, with better understanding of tool call structure and browser automation workflows.
Technical Specifications
Model Architecture and Objective
- Architecture: Mistral-7B-Instruct with LoRA adapters
- Objective: Causal language modeling with masked loss on assistant responses
- Parameters: Base model: 7B, Trainable parameters: ~8.4M (LoRA adapters only)
Compute Infrastructure
Hardware
- GPU: NVIDIA GPU with 16GB+ VRAM (tested on various consumer GPUs)
- Memory: Optimized for 16GB VRAM with 8-bit quantization
Software
- Framework: PyTorch, Transformers, PEFT
- Training framework: Hugging Face Transformers Trainer
- PEFT version: 0.18.0
Framework versions
- PEFT 0.18.0
- Transformers (latest)
- PyTorch (with CUDA support)
