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Model: meldakahramann/animasyon-lora-adapter Source: Original Platform
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README.md
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---
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base_model: unsloth/llama-3-8b-Instruct-bnb-4bit
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- llama
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license: apache-2.0
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language:
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- en
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---
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# Uploaded finetuned model
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- **Developed by:** meldakahramann
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- **License:** apache-2.0
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- **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit
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This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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# Llama-3 8B Turkish Animation Fine-Tuned Model
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Bu model, **Llama-3-8B-Instruct** mimarisi temel alınarak Türkçe animasyon filmleri (olay örgüleri, karakterler, Türkçe seslendirme kadroları ve teknik detaylar) üzerine **Unsloth** ve **LoRA (Low-Rank Adaptation)** teknikleri kullanılarak ince ayar (fine-tuning) yapılmış versiyonudur.
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Model, yanıt vermeden önce iç ses mantığı yürüten bir **Reasoning / Thinking (`<|im_start|>thinking`)** şablonuyla hizalanmıştır.
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---
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## Öne Çıkan Özellikler
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* **Domain Adaptasyonu:** Türkiye'de vizyona girmiş popüler animasyon filmlerinin (Ratatuy, Buz Devri vb.) doğru olay örgüleri ve Türkçe dublaj kadrosu bilgileri kazandırılmıştır.
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* **Akıl Yürütme:** Model yanıt üretmeden önce kullanıcı talebini analiz eden ve ne yapacağını planlayan bir iç ses sürecini (`<|im_start|>thinking ... <|im_end|>`) çalıştırır.
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* **Halüsinasyon Engelleme:** Derinleştirilmiş 250 adımlık eğitim sayesinde uydurma veri üretimi minimuma indirilmiştir.
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---
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## Kullanım
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Modeli `transformers` ve `unsloth` veya doğrudan Hugging Face kütüphaneleri ile kolayca çalıştırabilirsiniz:
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "meldakahramann/animasyon-lora-adapter"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.float16,
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device_map="auto"
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)
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# Örnek Prompt Yapısı
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prompt = "<|im_start|>user\nRatatuy animasyonu neyi anlatıyor, hikayesi nasıl?<|im_end|>\n<|im_start|>assistant\n"
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inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
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outputs = model.generate(
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**inputs,
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max_new_tokens=512,
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temperature=0.2,
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top_p=0.9,
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eos_token_id=tokenizer.convert_tokens_to_ids("<|im_end|>")
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)
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print(tokenizer.decode(outputs[0], skip_special_tokens=False))
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```
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## Örnek Çıktı
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<|im_start|>thinking
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Ratatuy animasyonunun olay örgüsü istendi. Hafızamdaki özet metne sadık kalarak net ve anlaşılır bir anlatım sunmalıyım..
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<|im_end|>
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Remy, koku ve tat alma duyuları olağanüstü derecede gelişmiş, zekası ve yaratıcılığı olan dışlanmış bir faredir. Auguste Gusteau'nun 'Herkes yemek yapabilir' felsefesine inanan Remy, Paris'te sakar çöpçü Linguini ile işbirliği yapar. Linguini'nin aşçı şapkasının altına saklanarak onun hareketlerini yönlendirir ve gurme yemekler hazırlar...
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## Eğitim Parametreleri
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Model, kısıtlı kaynaklarla maksimum performans elde etmek amacıyla **PEFT (LoRA)** ve **Unsloth** kütüphaneleri kullanılarak eğitilmiştir. Detaylı konfigürasyon aşağıda yer almaktadır:
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| Parametre / Konfigürasyon | Değer |
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| :--- | :--- |
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| **Base Model** | `unsloth/llama-3-8b-Instruct-bnb-4bit` |
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| **Eğitim Yöntemi (Method)** | LoRA (PEFT) + SFTTrainer |
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| **Maks. Dizi Uzunluğu (Max Seq Length)** | `2048` |
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| **Eğitim Adım Sayısı (Max Steps)** | `250` |
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| **Öğrenme Oranı (Learning Rate)** | `2e-4` |
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| **Batch Size** | `2` *(Gradient Accumulation Steps: `4` → Effective Batch Size: `8`)* |
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| **Eniyileştirici (Optimizer)** | AdamW 8-bit |
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| **Eklenen Özel Token'lar** | `<|im_start|>`, `<|im_end|>`, `thinking` |
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| **Donanım (Hardware)** | Google Colab T4 GPU (16 GB VRAM) |
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---
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### Konfigürasyon Notları
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* **Özel Token Entegrasyonu:** Modelin akıl yürütme ve sohbet formatına uyum sağlaması için `<|im_start|>`, `<|im_end|>` ve `thinking` etiketleri özel token olarak eklenmiş ve kelime gömme katmanı yeniden boyutlandırılmıştır.
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* **Hafıza Optimizasyonu:** QLoRA (4-bit quantization) ve AdamW 8-bit optimizer kullanılarak T4 GPU üzerindeki VRAM kullanımı minimum seviyede tutulmuştur.
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---
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# Türkçe MMLU Benchmark Değerlendirme & Karşılaştırma Raporu
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Bu model kartı, fine-tune edilmiş modelimizin Türkçe MMLU (Massive Multitask Language Understanding) benchmark testindeki performansını, türetildiği taban model ve farklı bir mimariye sahip açık kaynaklı bir model ile karşılaştırmalı olarak sunmaktadır.
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## Değerlendirme Metodolojisi
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* **Benchmark Veri Seti:** `alibayram/yapay_zeka_turkce_mmlu_model_cevaplari` (6,200 Soru)
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* **Değerlendirme Yöntemi:** Zero-shot prompting & Anlamsal Benzerlik (`paraphrase-multilingual-mpnet-base-v2`) destekli yanıt doğrulama
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* **Donanım / Yapılandırma:** BitsAndBytes 4-bit NF4 Kuantizasyonu
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---
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## Karşılaştırmalı Sonuçlar Tablosu
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| Model Türü | Model Reposu / Adı | Toplam Soru | Doğru Cevap | Başarı Oranı (Accuracy) | Değerlendirme Süresi | Soru Başına Ort. Süre |
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| :--- | :--- | :---: | :---: | :---: | :---: | :---: |
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| **Fine-Tuned Model (Bizim Modelimiz)** | `meldakahramann/animasyon-lora-adapter` | 6200 | **1261** | **%20.34** | 2797.9 sn (~46.6 dk) | ~0.45 sn |
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| **Base Model** | `unsloth/llama-3-8b-bnb-4bit` | 6200 | **1244** | **%20.06** | 2700.9 sn (~45.0 dk) | ~0.43 sn |
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| **Karşılaştırma Modeli (3. Model)** | `unsloth/Qwen2.5-7B-Instruct-bnb-4bit` | 6200 | 1179 | %19.01 | **2168.3 sn (~36.1 dk)** | **~0.35 sn** |
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---
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## Detaylı Analiz ve Değerlendirmeler
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### 1. Fine-Tuning Başarısı (Doğruluk Oranı)
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* Fine-tune ettiğimiz modelimiz, **1261 doğru yanıt (%20.34 başarı oranı)** elde ederek test edilen modeller arasında **en yüksek doğruluğa** ulaşmıştır.
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* Modelimiz, türetildiği **Base Llama-3-8B** modeline kıyasla **17 soru daha fazla** doğru yanıtlayarak başarısını %20.06'dan **%20.34'e** yükseltmiştir. Bu durum yapılan ince ayar (fine-tuning) sürecinin Türkçe anlama ve yanıt verme kapasitesine olumlu katkı sağladığını kanıtlamaktadır.
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### 2. Çıkarım (Inference) Hızı ve Performans
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* **Dengeli Çıkarım:** Fine-tuned modelimiz (2797.9 sn) ile Base model (2700.9 sn) benzer çıkarım süreleri sergilemiştir. Her iki model de soru başına ortalama ~0.43-0.45 saniye yanıt süresiyle stabil bir performans göstermiştir.
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* **Hız Farkı (Qwen 2.5):** `Qwen2.5-7B-Instruct` modeli 2168.3 saniye (~36.1 dakika) ile en hızlı çıkarımı gerçekleştirmiş olsa da doğruluk oranında (%19.01) Llama-3 tabanlı modellerin gerisinde kalmıştır.
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### 3. Genel Değerlendirme
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* Uygulanan fine-tuning işlemi, modelin doğruluk performansını artırırken hız tarafında belirgin bir maliyet/yavaşlama yaratmamıştır.
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* Llama-3 mimarisi üzerine inşa edilen modelimiz, Türkçe MMLU testinde hem base versiyonuna hem de Qwen 2.5 alternatifine karşı doğruluk açısından üstünlük sağlamıştır.
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{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>
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'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>
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' }}{% endif %}
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config.json
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config.json
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{
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"_name_or_path": "unsloth/llama-3-8b",
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 128000,
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"eos_token_id": 128001,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 8192,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"pad_token_id": 128255,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"rope_theta": 500000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.44.2",
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"unsloth_version": "2024.9",
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"use_cache": true,
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"vocab_size": 128256
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}
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"_from_model_config": true,
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"pad_token_id": 128001,
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"transformers_version": "4.40.0"
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}
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||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
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oid sha256:aec10bbc9adc012f849d726f5a08298be90181ea08e150dafb76edecea8a797d
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size 17210521
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2097
tokenizer_config.json
Normal file
2097
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user