Create README.md

README.md

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+---
+language: en
+tags:
+- crypto
+- web3
+- defi
+- deepseek
+- fine-tuning
+license: mit
+---
+
+# Crypto-Expert LLM
+
+## Model Description
+
+This model is a fine-tuned version of `deepseek-ai/DeepSeek-R1-Distill-Qwen-7B`, specifically optimized for cryptocurrency, Web3, and DeFi-related tasks. It is designed to provide specialized knowledge while maintaining computational efficiency.
+
+### Supported Tasks
+
+- Cryptocurrency and DeFi concepts explanation
+- Smart contract analysis and auditing guidance
+- Trading strategy discussions
+- AMM (Automated Market Maker) mechanics
+- MEV (Maximal Extractable Value) analysis
+- Web3 development assistance (Rust, Python)
+- Decentralized infrastructure (IPFS, libp2p)
+
+## Training Data
+
+The model is fine-tuned on specialized crypto and web3 prompts. Training data should be placed in the `./json/` directory in JSONL format.
+
+## Technical Specifications
+
+- **Base Model**: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
+- **Training Type**: Supervised Fine-Tuning (SFT)
+- **Required Python Version**: 3.13+
+- **Package Manager**: uv
+
+## Performance and Limitations
+
+The model is optimized for:
+- Reduced resource consumption compared to larger models
+- Domain-specific accuracy in crypto/Web3 contexts
+- Cost-effective deployment
+
+Note: Specific performance metrics will be added after training evaluation.
+
+## Intended Use
+
+This model is designed for:
+- Developers working on Web3 projects
+- DeFi researchers and analysts
+- Cryptocurrency protocol designers
+- Smart contract developers
+- Blockchain infrastructure engineers
+
+## Ethical Considerations
+
+Users should:
+- Verify all financial advice independently
+- Be aware of data privacy when using the model
+- Understand the model's limitations in real-time market analysis
+- Follow appropriate licensing requirements for training data