AhiskaAI

AhiskaAI-308m-Instruct-v0.2

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README

License: apache-2.0

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

The graph above demonstrates the supervised fine-tuning convergence of AhiskaAI-308m-IT-v0.2.

Usage (ChatML Format)

Recommended System Prompt

markdown
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.

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Model Details

Model Provider

AhiskaAI

Model Tree

Base

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Input Modalities

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Output Modalities

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Supported Functionality

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README

License: apache-2.0

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

The graph above demonstrates the supervised fine-tuning convergence of AhiskaAI-308m-IT-v0.2.

Usage (ChatML Format)

Recommended System Prompt

markdown
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.