108 lines
3.3 KiB
Markdown
108 lines
3.3 KiB
Markdown
---
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language:
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- tr
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- en
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license: apache-2.0
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base_model: Qwen/Qwen2.5-14B-Instruct
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tags:
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- turkish
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- qwen2
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- instruction-tuned
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- sft
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- qlora
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- tr
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- reasoning
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- conversational
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- low-resource
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- turkish-nlp
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datasets:
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- ogulcanaydogan/Turkish-LLM-v10-Training
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pipeline_tag: text-generation
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model-index:
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- name: Turkish-LLM-14B-Instruct
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results:
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- task:
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type: text-generation
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dataset:
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name: MMLU-TR
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type: custom
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metrics:
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- name: accuracy
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type: acc
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value: 0.5977
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---
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# Turkish-LLM-14B-Instruct
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A Turkish-enhanced 14B model fine-tuned from Qwen2.5-14B-Instruct with QLoRA on 242K Turkish instruction examples.
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Part of the [Turkish LLM Family](https://huggingface.co/collections/ogulcanaydogan/turkish-llm-family-69b303b4ef1c36caffca4e94).
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## Highlights
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- **14B parameters** - strong performance with moderate hardware requirements
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- **Outperforms base** on MMLU-TR (+0.30 vs Qwen2.5-14B-Instruct)
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- **Live demo** - [Try it on Spaces](https://huggingface.co/spaces/ogulcanaydogan/Turkish-LLM-14B-Chat)
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- **GGUF available** - [Q4/Q5/Q8/F16 quantizations](https://huggingface.co/ogulcanaydogan/Turkish-LLM-14B-Instruct-GGUF)
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## Benchmark Results
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| Benchmark | Base (Qwen2.5-14B) | **Ours** | Delta |
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|:---|:---:|:---:|:---:|
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| **MMLU-TR** | 0.5947 | **0.5977** | **+0.30** |
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## Quick Start
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### With Ollama
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```bash
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ollama run hf.co/ogulcanaydogan/Turkish-LLM-14B-Instruct-GGUF:Q4_K_M
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```
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### With Transformers
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("ogulcanaydogan/Turkish-LLM-14B-Instruct", torch_dtype="auto", device_map="auto")
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tokenizer = AutoTokenizer.from_pretrained("ogulcanaydogan/Turkish-LLM-14B-Instruct")
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messages = [
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{"role": "system", "content": "Sen yardimci bir Turkce asistansin."},
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{"role": "user", "content": "Yapay zekanin egitim sektorundeki etkilerini acikla."}
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]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer([text], return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=512)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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## Training Details
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| Parameter | Value |
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|:---|:---|
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| Base Model | Qwen/Qwen2.5-14B-Instruct |
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| Method | QLoRA (4-bit NF4) |
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| LoRA rank / alpha | 32 / 64 |
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| Learning rate | 1e-5 |
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| Dataset | 242K Turkish instruction examples |
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## Turkish LLM Family
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| Model | Size | MMLU-TR | GGUF |
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|:---|:---:|:---:|:---|
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| [Turkish-LLM-7B](https://huggingface.co/ogulcanaydogan/Turkish-LLM-7B-Instruct) | 7B | - | [Download](https://huggingface.co/ogulcanaydogan/Turkish-LLM-7B-Instruct-GGUF) |
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| **[Turkish-LLM-14B](https://huggingface.co/ogulcanaydogan/Turkish-LLM-14B-Instruct)** | **14B** | **0.5977** | [Download](https://huggingface.co/ogulcanaydogan/Turkish-LLM-14B-Instruct-GGUF) |
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| [Turkish-LLM-32B](https://huggingface.co/ogulcanaydogan/Turkish-LLM-32B-Instruct) | 32B | 0.6564 | [Download](https://huggingface.co/ogulcanaydogan/Turkish-LLM-32B-Instruct-GGUF) |
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## Citation
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```bibtex
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@misc{aydogan2026turkishllm,
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title={Turkish LLM Family: Open-Source Turkish Language Models},
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author={Ogulcan Aydogan},
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year={2026},
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url={https://huggingface.co/collections/ogulcanaydogan/turkish-llm-family-69b303b4ef1c36caffca4e94}
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}
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```
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