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AhiskaAI
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AhiskaAI
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To guarantee a structurally sound foundation capable of instruction alignment, the model underwent a rigorous dual-stage training pipeline:
At AhıskaAI, we champion transparency and the indie "Build in Public" spirit. This modern LLaMA variant was hammered out entirely on local, consumer-grade hardware:
Instead of using English-centric tokenizers that aggressively fragment agglutinative Turkish words, this model deploys a custom BPE Tokenizer with a 32,000 vocab size trained entirely from scratch on our raw corpus.
As verified by the core configuration, this model abandons legacy frameworks for a pure LLaMA layout:
LlamaForCausalLMhidden_size): 512intermediate_size): 1376num_hidden_layers): 12num_attention_heads): 8silu)max_position_embeddings): 1024 tokensYou can easily load and run inference using the Hugging Face transformers library. Make sure to use the exact Alpaca prompt template for the best instruction-following results:
python
from transformers import LlamaForCausalLM, AutoTokenizerimport torch model_name = "AhiskaAI/ahiska-llama-65m-alpaca-v0.1" # Load model and custom tokenizermodel = LlamaForCausalLM.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")tokenizer = AutoTokenizer.from_pretrained(model_name) def generate_response(instruction): # Standard Alpaca Prompt Template prompt = f"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:\n" inputs = tokenizer(prompt, return_tensors="pt").to(model.device) with torch.no_grad(): outputs = model.generate( **inputs, max_length=200, do_sample=True, top_k=40, top_p=0.92, temperature=0.6, # Keeps the small LLaMA focused and realistic repetition_penalty=1.15 ) response = tokenizer.decode(outputs[0], skip_special_tokens=True) return response.split("### Response:\n")[-1].strip() # Sample Generationprint(generate_response("Sağlıklı yaşamak için 3 ipucu ver"))from transformers import LlamaForCausalLM, AutoTokenizerimport torch model_name = "AhiskaAI/ahiska-llama-65m-alpaca-v0.1" # Load model and custom tokenizermodel = LlamaForCausalLM.from_pretrained(model_name).to("cuda" if torch.cuda.is_available() else "cpu")tokenizer = AutoTokenizer.from_pretrained(model_name) def generate_response(instruction): # Standard Alpaca Prompt Template prompt = f"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:\n" inputs = tokenizer(prompt, return_tensors="pt").to(model.device) with torch.no_grad(): outputs = model.generate( **inputs, max_length=200, do_sample=True, top_k=40, top_p=0.92, temperature=0.6, # Keeps the small LLaMA focused and realistic repetition_penalty=1.15 ) response = tokenizer.decode(outputs[0], skip_special_tokens=True) return response.split("### Response:\n")[-1].strip() # Sample Generationprint(generate_response("Sağlıklı yaşamak için 3 ipucu ver"))