Akahsizrr
/

toncode-v1

@@ -3,208 +3,57 @@ base_model: unsloth/qwen2.5-coder-7b-instruct-bnb-4bit
 library_name: peft
 pipeline_tag: text-generation
 tags:
-- base_model:adapter:unsloth/qwen2.5-coder-7b-instruct-bnb-4bit
 - lora
-- sft
-- transformers
-- trl
 - unsloth
 ---
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-- PEFT 0.18.1

 library_name: peft
 pipeline_tag: text-generation
 tags:
+- minecraft
+- java
+- spigot
+- papermc
 - lora
 - unsloth
+- qwen2.5-coder
 ---
+# toncode-v1: Minecraft Plugin Coder
+This model is a fine-tuned LoRA adapter for **Qwen2.5-Coder-7B-Instruct**, specialized in generating high-quality Java code for Minecraft server plugins (Spigot/Paper API).
 ## Model Details
+- **Developed by:** Akahsizrr
+- **Model type:** LoRA Adapter (PEFT)
+- **Base Model:** Qwen/Qwen2.5-Coder-7B-Instruct
+- **Language(s):** English, Java (Minecraft Spigot/Paper API)
+- **License:** Apache-2.0
+- **Finetuned from model:** unsloth/qwen2.5-coder-7b-instruct-bnb-4bit
 ## Training Details
+The model was trained using **Unsloth** on a Minecraft-specific dataset containing optimized plugin logic and event handling.
+- **Training Steps:** 100
+- **Optimizer:** AdamW 8-bit
+- **Learning Rate:** 2e-4
+- **Hardware:** 2x NVIDIA T4 (Kaggle)
+- **Batch Size:** 1 (with Gradient Accumulation Steps: 8)
+## How to Get Started
+To use this model, you need to load it as an adapter on top of the base Qwen2.5-Coder model using the `peft` or `unsloth` library.
+```python
+from unsloth import FastLanguageModel
+import torch
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name = "unsloth/qwen2.5-coder-7b-instruct-bnb-4bit",
+    max_seq_length = 2048,
+    load_in_4bit = True,
+)
+# Load your fine-tuned adapter
+model = FastLanguageModel.for_inference(model)
+model.load_adapter("Akahsizrr/toncode-v1")
+# Test prompt
+instruction = "Create a listener that gives a player a Diamond Sword when they first join the server."
+messages = [{"role": "user", "content": instruction}]
+inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to("cuda")
+outputs = model.generate(input_ids=inputs, max_new_tokens=512)
+print(tokenizer.batch_decode(outputs)[0])