Uezurii/collabhaven-ai-enhanced

CollabHaven AI Enhanced v1.0

Model Description

CollabHaven AI Enhanced is a specialized language model fine-tuned for music collaboration, creative assistance, and community interaction on the CollabHaven platform. Built on TinyLlama-1.1B-Chat with LoRA adapters, this model understands CollabHaven's ecosystem, Audius integration, and provides comprehensive music creation support.

Key Features

🎵 Music Creation Assistance

• Lyric writing and song structure guidance
• Chord progression suggestions
• Melody composition support
• Beat and rhythm recommendations
• Mixing and mastering advice
• Sound design tips
• Music marketing strategies
• Artist bio creation

🤝 CollabHaven Platform Integration

• Deep understanding of CollabHaven's collaborative features
• Audius platform relationship awareness
• Community guidelines and culture knowledge
• Project creation and collaboration workflows
• Real-time collaboration support

🎯 Specialized Knowledge

• CollabHaven official website: https://collabhvn.com
• Audius profile: https://audius.co/collabhavenr
• AI Studio tools and capabilities
• Music industry best practices
• Creative workflow optimization

Technical Specifications

• Base Model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
• Fine-tuning Method: LoRA (Low-Rank Adaptation)
• Training Dataset: 200 enhanced examples (185 general + 15 CollabHaven-specific)
• Training Steps: 150
• Max Length: 512 tokens
• Model Size: ~1.1B parameters + LoRA adapters

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

# Load the model
base_model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
model = PeftModel.from_pretrained(base_model, "collabhaven/collabhaven-ai-enhanced")
tokenizer = AutoTokenizer.from_pretrained("collabhaven/collabhaven-ai-enhanced")

# Generate response
prompt = "What is CollabHaven and how can it help with music collaboration?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Deployment

This model is optimized for serverless deployment and can be easily integrated into:

• Supabase Edge Functions
• Vercel Edge Runtime
• AWS Lambda
• Azure Functions
• Google Cloud Functions

License

Apache 2.0

Contact

For questions about CollabHaven AI:

• Website: https://collabhvn.com
• Audius: https://audius.co/collabhavenr

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This model is specifically designed for the CollabHaven music collaboration platform and its community.