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AhiskaAI
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Model Details
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AhiskaAI
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Output Modalities
Supported Functionality
Despite its miniature footprint, the model captures core Turkish morphology and factual token paths due to a highly strategic, clean data mixture of a 5.3 GB base corpus:
You can easily load and run text generation using the Hugging Face transformers library.
python
from transformers import GPT2LMHeadModel, AutoTokenizerimport torch model_name = "AhiskaAI/AhiskaAI-25m-Base-v0.1" # Load model and custom Turkish tokenizermodel = GPT2LMHeadModel.from_pretrained(model_name)tokenizer = AutoTokenizer.from_pretrained(model_name) # Configure pad token for GPT-2 architecturetokenizer.pad_token = tokenizer.eos_token # Sample generationprompt = "Ahıska Türkleri,"inputs = tokenizer(prompt, return_tensors="pt") with torch.no_grad(): outputs = model.generate( **inputs, max_length=100, do_sample=True, top_k=50, top_p=0.95, temperature=0.7, no_repeat_ngram_size=2 ) print(tokenizer.decode(outputs[0], skip_special_tokens=True))⚠️ Model Limitations & Intended Use Pure Base Model: This is a raw text completer and has not been aligned using Instruction Fine-Tuning (SFT) or RLHF. It will not act as a conversational chatbot out of the box and might loop sequences if not sampled correctly.
Intended Use: This model is highly receptive to immediate downstream SFT fine-tuning, vocabulary adaptations, or continuation of pre-training checkpoints.
For the aligned chat version, please check out: AhiskaAI-25m-Chat-v0.1-Experimental
from transformers import GPT2LMHeadModel, AutoTokenizerimport torch model_name = "AhiskaAI/AhiskaAI-25m-Base-v0.1" # Load model and custom Turkish tokenizermodel = GPT2LMHeadModel.from_pretrained(model_name)tokenizer = AutoTokenizer.from_pretrained(model_name) # Configure pad token for GPT-2 architecturetokenizer.pad_token = tokenizer.eos_token # Sample generationprompt = "Ahıska Türkleri,"inputs = tokenizer(prompt, return_tensors="pt") with torch.no_grad(): outputs = model.generate( **inputs, max_length=100, do_sample=True, top_k=50, top_p=0.95, temperature=0.7, no_repeat_ngram_size=2 ) print(tokenizer.decode(outputs[0], skip_special_tokens=True))