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fatih-can
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Model Details
Model Provider
fatih-can
Model Tree
vngrs-ai/Kumru-2B
Input Modalities
Output Modalities
Supported Functionality
On a set of 100 Turkish eval set:
| Answer Ratio | |
|---|---|
| Kumru-2B (base) | 50/100 |
| Kumru-2B-Abliterated | 98/100 answered |
KL divergence vs. the original model on a harmless evaluation set: 0.0148
python
from transformers import AutoModelForCausalLM, AutoTokenizermodel_name = "fatih-can/Kumru-2B-Abliterated"tokenizer = AutoTokenizer.from_pretrained(model_name)model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")def generate_response(query):messages = [{'role': 'system', 'content': 'Adın Kumru. Türkçe için sıfırdan eğitilmiş bir dil modelisin.'},{'role': 'user', 'content': query}]model_inputs = tokenizer.apply_chat_template(messages, return_tensors='pt', add_generation_prompt=True).to(model.device)model_outputs = model.generate(model_inputs, max_new_tokens=512, do_sample=True,top_p=0.9, temperature=0.7, repetition_penalty=1.1)output_tokens = model_outputs[0].cpu().detach().numpy().tolist()generated_tokens = output_tokens[model_inputs[0].shape[0]:]return tokenizer.decode(generated_tokens, skip_special_tokens=True)print(generate_response("Merhaba, bana kendinden bahseder misin?"))
MistralForCausalLM (hidden 3072, 18 layers), native context 8192 tokens, drop-in replaceable for the base model.