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--- |
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language: |
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- ru |
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- en |
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base_model: NousResearch/Llama-2-7b-hf |
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tags: |
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- generated_from_trainer |
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- bitnet |
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- llama |
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- rulm |
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- darulm |
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datasets: |
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- dichspace/darulm |
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library_name: transformers |
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model-index: |
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- name: RuBit-Llama-56M2 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# RuBit-Llama-63M |
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This model is a fine-tuned version of [NousResearch/Llama-2-7b-hf](https://huggingface.co/NousResearch/Llama-2-7b-hf) on the darulm dataset. |
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From darulm aphorisms, dramaturgy, history, humor, literature domains were sampled |
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Training on 2_125_871_104 tokens. |
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Inspired by [abideen/Bitnet-Llama-70M](https://huggingface.co/abideen/Bitnet-Llama-70M) |
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## Model description |
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# Sample inference code |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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# Load a pretrained BitNet model |
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model = "igorktech/RuBit-LLama-63M" |
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tokenizer = AutoTokenizer.from_pretrained(model) |
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model = AutoModelForCausalLM.from_pretrained(model) |
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def convert_to_bitnet(model, copy_weights): |
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for name, module in model.named_modules(): |
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# Replace linear layers with BitNet |
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if isinstance(module, LlamaSdpaAttention) or isinstance(module, LlamaMLP): |
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for child_name, child_module in module.named_children(): |
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if isinstance(child_module, nn.Linear): |
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bitlinear = BitLinear(child_module.in_features, child_module.out_features, child_module.bias is not None).to(device="cuda:0") |
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if copy_weights: |
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bitlinear.weight = child_module.weight |
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if child_module.bias is not None: |
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bitlinear.bias = child_module.bias |
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setattr(module, child_name, bitlinear) |
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# Remove redundant input_layernorms |
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elif isinstance(module, LlamaDecoderLayer): |
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for child_name, child_module in module.named_children(): |
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if isinstance(child_module, LlamaRMSNorm) and child_name == "input_layernorm": |
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setattr(module, child_name, nn.Identity().to(device="cuda:0")) |
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convert_to_bitnet(model, copy_weights=True) |
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model.to(device="cuda:0") |
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prompt = "Привет" |
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device) |
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generate_ids = model.generate(inputs.input_ids, max_length=100) |
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tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] |
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``` |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0015 |
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- train_batch_size: 64 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 128 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 0.1 |
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- num_epochs: 2 |
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- mixed_precision_training: Native AMP |
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### Training results |
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### Framework versions |
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- Transformers 4.40.0 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.0 |
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- Tokenizers 0.19.1 |
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