62 lines
1.9 KiB
Markdown
62 lines
1.9 KiB
Markdown
---
|
||
license: apache-2.0
|
||
language:
|
||
- tr
|
||
pipeline_tag: text-generation
|
||
tags:
|
||
- gpt2
|
||
- slm
|
||
- base-model
|
||
- causal-lm
|
||
- pre-trained
|
||
- tr-llm
|
||
- Ahıska
|
||
- AhiskaTurks
|
||
- MeskhetianTurks
|
||
- AhıskaTürkleri
|
||
library_name: transformers
|
||
---
|
||
|
||
# AhiskaAI-134m-IT-v0.2
|
||
|
||
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.
|
||
|
||
**Base Model:** [AhiskaAI-134m-Base-v0.2](https://huggingface.co/AhiskaAI/AhiskaAI-134m-Base-v0.2)
|
||
|
||
## Model Details
|
||
- **Architecture:** Llama-based architecture.
|
||
- **Fine-tuning:** Supervised Fine-Tuning (SFT) on 16k+ instruction pairs.
|
||
- **Format:** ChatML.
|
||
- **Parameters:** 134M.
|
||
- **Hardware:** Trained on NVIDIA RTX 4050 Laptop GPU.
|
||
|
||
## Training Logs
|
||
|
||
|
||
## Usage (ChatML Format)
|
||
This model is optimized for chat interactions. Please use the following ChatML structure for best results:
|
||
|
||
## Recommended System Prompt
|
||
To get the best performance, use the following system prompt:
|
||
"Sen kibar, sorulan soruları tam cümlelerle yanıtlayan Türkçe bir asistansın."
|
||
|
||
## Example Usage
|
||
```
|
||
from transformers import AutoModelForCausalLM, AutoTokenizer
|
||
|
||
model = AutoModelForCausalLM.from_pretrained("AhiskaAI/AhiskaAI-134m-IT-v0.2")
|
||
tokenizer = AutoTokenizer.from_pretrained("AhiskaAI/AhiskaAI-134m-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))
|
||
``` |