Update README.md

README.md

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----
-license: llama2
-library_name: peft
-tags:
-- solana
-- rust
-- anchor
-- smart-contracts
-- finance
-- crypto
-- unsloth
-- codellama
-base_model: codellama/CodeLlama-7B-Instruct-hf
-datasets:
-- synthetic-solana-anchor-10k
-language:
-- en
----
-
-# Solana-CodeLlama-7B-v1 (Anchor Specialized)
-
-## Overview
-**Solana-CodeLlama-7B-v1** is a domain-specialized language model fine-tuned for writing production-ready **Solana Smart Contracts** using the **Anchor Framework**.
-
-While general coding models (like GPT-4 or standard CodeLlama) often hallucinate outdated syntax or struggle with Rust's strict ownership rules, this model was trained on a **high-purity synthetic dataset** of 10,000 algorithmic examples, focusing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:
-*   **Anchor Macros:** Correct usage of `#[derive(Accounts)]`, `#[program]`, `#[account]`.
-*   **Security Constraints:** Proper PDA seed validation and constraint checks (e.g., `#[account(mut, seeds = [...], bump)]`).
-*   **Rust & SPL Tokens:** Accurate CPI calls to the SPL Token program.
-
-## Performance & Benchmarks
-The model was evaluated against the base `CodeLlama-7B-Instruct` model on a specific "Solana Hold-Out Set".
-
-| Metric | Base Model (Zero-Shot) | **Solana-CodeLlama-7B-v1** (Ours) |
-| :--- | :---: | :---: |
-| **Accuracy (Validation)** | ~35% (Hallucinates Python/Solidtiy) | **97.26%** |
-| **Accounts Struct** | ❌ FAIL | ✅ PASS |
-| **Context Validation** | ❌ FAIL | ✅ PASS |
-| **PDA Initialization** | ❌ FAIL | ✅ PASS |
-| **SPL Token Transfer** | ❌ FAIL | ✅ PASS |
-
-*> "The model didn't just learn; it absorbed the syntax structure instantly, dropping loss to 0.02 in < 2 epochs."*
-
-## Dataset
-*   **Source:** 100% Synthetic (Algorithmic Generation).
-*   **Size:** 10,000 Verified Examples.
-*   **Methodology:** We utilized a "Textbook Quality" approach, generating examples with perfect compile-ready logic rather than scraping noisy GitHub repositories.
-
-## Usage
-
-### 1. Using Unsloth (Fastest)
-```python
-from unsloth import FastLanguageModel
-
-model, tokenizer = FastLanguageModel.from_pretrained(
-    model_name = "your-username/Solana-CodeLlama-7B-v1",
-    max_seq_length = 2048,
-    dtype = None,
-    load_in_4bit = True,
-)
-
-prompt = """Write a Solana Anchor program to initialize a user vault."""
-# ... Apply chat template ...
-```
-
-### 2. Using GGUF (Ollama / LM Studio)
-This model is available in GGUF format for local deployment on consumer hardware (MacBook M1/M2/M3, NVIDIA RTX 3060/4090/5090).
-*   `Solana-CodeLlama-7B-v1.Q4_K_M.gguf` (Recommended for 8GB+ RAM)
-*   `Solana-CodeLlama-7B-v1.Q8_0.gguf` (High Precision)
-
-## Training Details
-*   **Hardware:** NVIDIA RTX 5090 (24GB VRAM).
-*   **Framework:** Unsloth (Open Source).
-*   **Precision:** Mixed Precision (BF16).
-*   **LoRA Rank:** 16.
-*   **Batch Size:** 8 (Effective).
-
-## License
-Based on CodeLlama (Llama 2 Community License).
-
----
-*Fine-tuned with ❤️ using [Unsloth](https://github.com/unslothai/unsloth).*
+---
+license: llama2
+library_name: peft
+tags:
+- solana
+- rust
+- anchor
+- smart-contracts
+- finance
+- crypto
+- unsloth
+- codellama
+base_model: codellama/CodeLlama-7B-Instruct-hf
+datasets:
+- synthetic-solana-anchor-10k
+language:
+- en
+---
+
+# Solana-CodeLlama-7B-v1 (Anchor Specialized)
+
+## Overview
+**Solana-CodeLlama-7B-v1** is a domain-specialized language model fine-tuned for writing production-ready **Solana Smart Contracts** using the **Anchor Framework**.
+
+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:
+*   **Anchor Macros:** Correct usage of `#[derive(Accounts)]`, `#[program]`, `#[account]`.
+*   **Security Constraints:** Proper PDA seed validation and constraint checks (e.g., `#[account(mut, seeds = [...], bump)]`).
+*   **Rust & SPL Tokens:** Accurate CPI calls to the SPL Token program.
+
+## Performance & Benchmarks
+The model was evaluated against the base `CodeLlama-7B-Instruct` model on a specific "Solana Hold-Out Set".
+
+| Metric | Base Model (Zero-Shot) | **Solana-CodeLlama-7B-v1**  |
+| :--- | :---: | :---: |
+| **Accuracy (Validation)** | ~35% (Hallucinates Python/Solidtiy) | **97.26%** |
+| **Accounts Struct** | ❌ FAIL | ✅ PASS |
+| **Context Validation** | ❌ FAIL | ✅ PASS |
+| **PDA Initialization** | ❌ FAIL | ✅ PASS |
+| **SPL Token Transfer** | ❌ FAIL | ✅ PASS |
+
+*> "The model didn't just learn; it absorbed the syntax structure instantly, dropping loss to 0.02 in < 2 epochs."*
+
+## Dataset
+*   **Source:** 100% Synthetic (Algorithmic Generation).
+*   **Size:** 10,000 Verified Examples.
+*   **Methodology:** We utilized a "Textbook Quality" approach, generating examples with perfect compile-ready logic rather than scraping noisy GitHub repositories.
+
+## Usage
+
+### 1. Using Unsloth (Fastest)
+```python
+from unsloth import FastLanguageModel
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name = "your-username/Solana-CodeLlama-7B-v1",
+    max_seq_length = 2048,
+    dtype = None,
+    load_in_4bit = True,
+)
+
+prompt = """Write a Solana Anchor program to initialize a user vault."""
+# ... Apply chat template ...
+```
+
+### 2. Using GGUF (Ollama / LM Studio)
+This model is available in GGUF format for local deployment on consumer hardware (MacBook M1/M2/M3, NVIDIA RTX 3060/4090/5090).
+*   `Solana-CodeLlama-7B-v1.Q4_K_M.gguf` (Recommended for 8GB+ RAM)
+*   `Solana-CodeLlama-7B-v1.Q8_0.gguf` (High Precision)
+
+## Training Details
+*   **Hardware:** NVIDIA RTX 5090 (32GB VRAM).
+*   **Framework:** Unsloth (Open Source).
+*   **Precision:** Mixed Precision (BF16).
+*   **LoRA Rank:** 16.
+*   **Batch Size:** 8 (Effective).
+
+## License
+Based on CodeLlama (Llama 2 Community License).
+
+---
+*Fine-tuned with ❤️ using [Unsloth](https://github.com/unslothai/unsloth).*