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Model: AhiskaAI/AhiskaAI-308m-Instruct-v0.2
Source: Original Platform

README.md

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+---
+license: apache-2.0
+language:
+- tr
+pipeline_tag: text-generation
+tags:
+- instruct
+- chat
+- conversational
+- fine-tuned
+- slm
+- llama
+- tr-llm
+- Ahiska
+library_name: transformers
+---
+
+
+# AhiskaAI-308m-IT-v0.2
+
+AhiskaAI-308m-IT-v0.2 is the instruction-tuned version of our 308M parameter Small Language Model. Fine-tuned on 16,000+ curated Turkish instruction-response pairs, it is designed to provide stronger conversational ability and improved instruction following while remaining efficient enough to run on consumer hardware.
+
+**Base Model:** AhiskaAI-308m-Base-v0.2
+
+---
+
+## Model Details
+
+- **Architecture:** Llama-based architecture.
+- **Fine-tuning:** Supervised Fine-Tuning (SFT).
+- **Format:** ChatML.
+- **Parameters:** 308M.
+- **Context Window:** 1024 tokens.
+- **Tokenizer:** Custom BPE Tokenizer (Vocabulary Size: 32,000).
+- **Training Framework:** PyTorch & Transformers.
+- **Hardware:** NVIDIA RTX 4050 6GB Laptop GPU.
+
+---
+
+## Fine-tuning Dataset
+
+The model was fine-tuned using more than **16,000** carefully curated Turkish instruction-response pairs.
+
+The dataset includes tasks such as:
+
+- Question answering
+- General conversation
+- Summarization
+- Text generation
+- Instruction following
+- Basic reasoning
+
+---
+
+## Design Goal
+
+The 308M-IT model serves as the flagship conversational model of the AhiskaAI v0.2 family.
+
+Its primary objectives are:
+
+- Improved Turkish instruction following.
+- Better contextual understanding.
+- More natural conversational responses.
+- A strong research foundation for future preference alignment methods such as DPO.
+
+---
+
+## Training Logs
+
+![Training Loss Curve](training_loss.png)
+
+*The graph above demonstrates the supervised fine-tuning convergence of AhiskaAI-308m-IT-v0.2.*
+
+---
+
+## Usage (ChatML Format)
+
+### Recommended System Prompt
+
+```
+Sen kibar, sorulan soruları tam cümlelerle yanıtlayan Türkçe bir asistansın.
+```
+
+### Example Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("AhiskaAI/AhiskaAI-308m-IT-v0.2")
+tokenizer = AutoTokenizer.from_pretrained("AhiskaAI/AhiskaAI-308m-IT-v0.2")
+
+SYSTEM_PROMPT = "Sen kibar, sorulan soruları tam cümlelerle yanıtlayan Türkçe bir asistansın."
+
+user_query = "Ahıska Türkleri hakkında bilgi verir misin?"
+
+prompt = (
+    f"<|im_start|>system\n{SYSTEM_PROMPT}<|im_end|>\n"
+    f"<|im_start|>user\n{user_query}<|im_end|>\n"
+    f"<|im_start|>assistant\n"
+)
+
+inputs = tokenizer(prompt, return_tensors="pt")
+
+outputs = model.generate(**inputs, max_new_tokens=200)
+
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+---
+
+## Known Limitations
+
+- Optimized primarily for Turkish.
+- Context window is limited to 1024 tokens.
+- Factual accuracy is still limited by model size and pretraining data.
+- May generate incorrect or incomplete responses on complex reasoning tasks.
+
+---
+
+## Future Plans
+
+- Preference alignment using DPO.
+- Larger, higher-quality Turkish datasets.
+- Expanded evaluation benchmarks.
+- Future AhiskaAI v0.3 model family.
+
+---
+
+## About AhiskaAI
+
+AhiskaAI is an independent open-source initiative dedicated to developing efficient Turkish Small Language Models trained completely from scratch.
+
+Follow us on Hugging Face for updates and future releases.