sixfingerdev
/

SixFinger-8B

@@ -9,202 +9,103 @@ tags:
 - transformers
 - trl
 - unsloth
 ---
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-### Framework versions
-- PEFT 0.18.0

 - transformers
 - trl
 - unsloth
+license: apache-2.0
+datasets:
+- sixfingerdev/turkish-qa-multi-dialog-dataset
+language:
+- tr
+- en
+- zh
 ---
+# SixFinger-8B Adapter for LLaMA 3.1 8B
+This repository contains a **LoRA adapter** for the SixFinger-8B model.
+The adapter allows fine-tuned responses on top of the base model **```unsloth/llama-3.1-8b-bnb-4bit```** without modifying the base weights.
+---
+## Overview
+- **Base Model:** unsloth/llama-3.1-8b-bnb-4bit
+- **Adapter Type:** LoRA
+- **Quantization:** 4-bit (via bitsandbytes)
+- **Purpose:** Enhanced response generation for Turkish/English mixed datasets.
+- **Compatibility:** Use with Hugging Face Transformers + PEFT library.
+---
+## Installation
+Install required dependencies:
+```!pip install transformers accelerate bitsandbytes peft```
+Ensure you have a GPU with sufficient VRAM for 4-bit inference.
+---
+## Loading the Model
+1. **Load the Base Model**
+```from transformers import AutoTokenizer, AutoModelForCausalLM```
+```base_model = AutoModelForCausalLM.from_pretrained(
+    "unsloth/llama-3.1-8b-bnb-4bit",
+    device_map="auto"
+)
+```
+2. **Load the Adapter**
+'from peft import PeftModel'
+'model = PeftModel.from_pretrained('
+'    base_model,'
+'    "sixfingerdev/SixFinger-8B"'
+')'
+3. **Load the Tokenizer**
+'tokenizer = AutoTokenizer.from_pretrained("unsloth/llama-3.1-8b-bnb-4bit")'
+---
+## Example Usage
+Generate text using the adapter:
+'prompt = "Soru: Yapay zeka nedir?\nCevap:"'
+'inputs = tokenizer(prompt, return_tensors="pt")'
+'with torch.no_grad():'
+'    outputs = model.generate('
+'        **inputs,'
+'        max_new_tokens=50,'
+'        do_sample=True,'
+'        temperature=0.7'
+'    )'
+'print(tokenizer.decode(outputs[0], skip_special_tokens=True))'
+---
+## Notes
+- The adapter does **not** modify the base model; it only applies LoRA weights on top.
+- 4-bit quantization significantly reduces VRAM usage. Ensure your GPU supports **bitsandbytes 4-bit operations**.
+- You can merge the adapter into the base model for easier deployment if needed.
+---
+## References
+- [PEFT (Parameter-Efficient Fine-Tuning)](https://huggingface.co/docs/peft/index)
+- [Transformers 4-bit Quantization](https://huggingface.co/docs/transformers/main/en/main_classes/quantization)
+---
+## License
+The adapter and its usage are provided under the terms specified in the repository.
+Ensure compliance with the **base model license** (Meta’s LLaMA).