62 lines
1.9 KiB
Markdown
62 lines
1.9 KiB
Markdown
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---
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license: apache-2.0
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language:
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- tr
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pipeline_tag: text-generation
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tags:
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- gpt2
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- slm
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- base-model
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- causal-lm
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- pre-trained
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- tr-llm
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- Ahıska
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- AhiskaTurks
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- MeskhetianTurks
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- AhıskaTürkleri
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library_name: transformers
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---
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# AhiskaAI-134m-IT-v0.2
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AhiskaAI-134m-IT-v0.2 is the instruction-tuned version of our 134M parameter Small Language Model. This model has been fine-tuned on 16,000+ high-quality, curated Turkish instruction-response pairs to function as a helpful and conversational AI assistant.
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**Base Model:** [AhiskaAI-134m-Base-v0.2](https://huggingface.co/AhiskaAI/AhiskaAI-134m-Base-v0.2)
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## Model Details
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- **Architecture:** Llama-based architecture.
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- **Fine-tuning:** Supervised Fine-Tuning (SFT) on 16k+ instruction pairs.
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- **Format:** ChatML.
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- **Parameters:** 134M.
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- **Hardware:** Trained on NVIDIA RTX 4050 Laptop GPU.
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## Training Logs
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## Usage (ChatML Format)
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This model is optimized for chat interactions. Please use the following ChatML structure for best results:
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## Recommended System Prompt
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To get the best performance, use the following system prompt:
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"Sen kibar, sorulan soruları tam cümlelerle yanıtlayan Türkçe bir asistansın."
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## Example Usage
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```
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("AhiskaAI/AhiskaAI-134m-IT-v0.2")
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tokenizer = AutoTokenizer.from_pretrained("AhiskaAI/AhiskaAI-134m-IT-v0.2")
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SYSTEM_PROMPT = "Sen kibar, sorulan soruları tam cümlelerle yanıtlayan Türkçe bir asistansın."
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user_query = "Ahıska Türkleri hakkında bilgi verir misin?"
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prompt = (
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f"<|im_start|>system\n{SYSTEM_PROMPT}<|im_end|>\n"
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f"<|im_start|>user\n{user_query}<|im_end|>\n"
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f"<|im_start|>assistant\n"
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)
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inputs = tokenizer(prompt, return_tensors="pt")
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outputs = model.generate(**inputs, max_new_tokens=200)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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