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--- |
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license: llama2 |
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library_name: peft |
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tags: |
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- solana |
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- rust |
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- anchor |
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- smart-contracts |
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- finance |
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- crypto |
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- unsloth |
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- codellama |
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base_model: codellama/CodeLlama-7B-Instruct-hf |
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datasets: |
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- synthetic-solana-anchor-10k |
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language: |
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- en |
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--- |
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# Solana-CodeLlama-7B-v1 (Anchor Specialized) |
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## Overview |
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**Solana-CodeLlama-7B-v1** is a domain-specialized language model fine-tuned for writing production-ready **Solana Smart Contracts** using the **Anchor Framework**. |
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While general coding models (like GPT-4 or standard CodeLlama) often hallucinate outdated syntax or struggle with Rust's strict ownership rules, this model was trained on a **high-purity synthetic dataset** of 10,000 algorithmic examples, focusing specifically on: |
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* **Anchor Macros:** Correct usage of `#[derive(Accounts)]`, `#[program]`, `#[account]`. |
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* **Security Constraints:** Proper PDA seed validation and constraint checks (e.g., `#[account(mut, seeds = [...], bump)]`). |
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* **Rust & SPL Tokens:** Accurate CPI calls to the SPL Token program. |
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## Performance & Benchmarks |
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The model was evaluated against the base `CodeLlama-7B-Instruct` model on a specific "Solana Hold-Out Set". |
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| Metric | Base Model (Zero-Shot) | **Solana-CodeLlama-7B-v1** | |
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| :--- | :---: | :---: | |
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| **Accuracy (Validation)** | ~35% (Hallucinates Python/Solidtiy) | **97.26%** | |
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| **Accounts Struct** | ❌ FAIL | ✅ PASS | |
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| **Context Validation** | ❌ FAIL | ✅ PASS | |
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| **PDA Initialization** | ❌ FAIL | ✅ PASS | |
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| **SPL Token Transfer** | ❌ FAIL | ✅ PASS | |
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*> "The model didn't just learn; it absorbed the syntax structure instantly, dropping loss to 0.02 in < 2 epochs."* |
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## Dataset |
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* **Source:** 100% Synthetic (Algorithmic Generation). |
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* **Size:** 10,000 Verified Examples. |
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* **Methodology:** We utilized a "Textbook Quality" approach, generating examples with perfect compile-ready logic rather than scraping noisy GitHub repositories. |
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## Usage |
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### 1. Using Unsloth (Fastest) |
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```python |
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from unsloth import FastLanguageModel |
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model, tokenizer = FastLanguageModel.from_pretrained( |
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model_name = "your-username/Solana-CodeLlama-7B-v1", |
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max_seq_length = 2048, |
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dtype = None, |
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load_in_4bit = True, |
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) |
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prompt = """Write a Solana Anchor program to initialize a user vault.""" |
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# ... Apply chat template ... |
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``` |
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### 2. Using GGUF (Ollama / LM Studio) |
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This model is available in GGUF format for local deployment on consumer hardware (MacBook M1/M2/M3, NVIDIA RTX 3060/4090/5090). |
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* `Solana-CodeLlama-7B-v1.Q4_K_M.gguf` (Recommended for 8GB+ RAM) |
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* `Solana-CodeLlama-7B-v1.Q8_0.gguf` (High Precision) |
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## Training Details |
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* **Hardware:** NVIDIA RTX 5090 (32GB VRAM). |
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* **Framework:** Unsloth (Open Source). |
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* **Precision:** Mixed Precision (BF16). |
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* **LoRA Rank:** 16. |
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* **Batch Size:** 8 (Effective). |
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## License |
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Based on CodeLlama (Llama 2 Community License). |
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--- |
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*Fine-tuned with ❤️ using [Unsloth](https://github.com/unslothai/unsloth).* |
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