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license: apache-2.0
language:
- tr
pipeline_tag: text-generation
tags:
- slm
- base-model
- causal-lm
- pre-trained
- tr-llm
- Ahıska
- AhiskaTurks
- MeskhetianTurks
- AhıskaTürkleri
library_name: transformers
---
# AhiskaAI-65m-IT-v0.2
AhiskaAI-65m-IT-v0.2 is the instruction-tuned version of our 65M parameter Small Language Model. Fine-tuned on a curated Turkish instruction dataset, it is designed to function as a lightweight conversational AI assistant while maintaining fast inference on resource-constrained hardware.
**Base Model:** AhiskaAI-65m-Base-v0.2
---
## Model Details
- **Architecture:** Llama-based architecture.
- **Fine-tuning:** Supervised Fine-Tuning (SFT).
- **Format:** ChatML.
- **Parameters:** 65M.
- **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 a curated Turkish instruction dataset designed to improve conversational ability and instruction following.
The dataset focuses on:
- Question answering
- General conversation
- Summarization
- Text generation
- Turkish instruction following
---
## Design Goal
The 65M-IT model is designed as the lightweight conversational member of the AhiskaAI v0.2 family.
Its primary goals are:
- Basic Turkish instruction following.
- Fast conversational inference.
- Low-resource deployment.
- A compact research baseline for future alignment methods.
---
## Training Logs
*The graph above demonstrates the supervised fine-tuning convergence of AhiskaAI-65m-IT-v0.2.*
---
## Usage (ChatML)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("AhiskaAI/AhiskaAI-65m-IT-v0.2")
tokenizer = AutoTokenizer.from_pretrained("AhiskaAI/AhiskaAI-65m-IT-v0.2")
SYSTEM_PROMPT = "Sen kibar, sorulan soruları tam cümlelerle yanıtlayan Türkçe bir asistansın."
prompt = (
f"<|im_start|>system\n{SYSTEM_PROMPT}<|im_end|>\n"
f"<|im_start|>user\nMerhaba<|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
- Limited factual knowledge due to model size.
- Optimized primarily for Turkish.
- Context window limited to 1024 tokens.
- May generate inaccurate or incomplete responses on complex topics.
---
## Future Plans
- DPO preference alignment.
- Improved instruction datasets.
- Future AhiskaAI v0.3 releases.
---
## 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. |