---
base_model: NousResearch/Llama-2-7b-hf
tags:
- generated_from_trainer
datasets:
- darulm
model-index:
- name: RuBit-Llama-56M2
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# RuBit-Llama-63M

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. 
From darulm aphorisms, dramaturgy, history, humor, literature domains were sampled

Training on 2_125_871_104 tokens.

Inspired by [abideen/Bitnet-Llama-70M](https://huggingface.co/abideen/Bitnet-Llama-70M)

## Model description

# Sample inference code

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load a pretrained BitNet model
model = "igorktech/RuBit-LLama-63M"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model)

def convert_to_bitnet(model, copy_weights):
    for name, module in model.named_modules():
        # Replace linear layers with BitNet
        if isinstance(module, LlamaSdpaAttention) or isinstance(module, LlamaMLP):
            for child_name, child_module in module.named_children():
                if isinstance(child_module, nn.Linear):
                    bitlinear = BitLinear(child_module.in_features, child_module.out_features, child_module.bias is not None).to(device="cuda:0")
                    if copy_weights:
                        bitlinear.weight = child_module.weight
                        if child_module.bias is not None:
                            bitlinear.bias = child_module.bias
                    setattr(module, child_name, bitlinear)
        # Remove redundant input_layernorms
        elif isinstance(module, LlamaDecoderLayer):
            for child_name, child_module in module.named_children():
                if isinstance(child_module, LlamaRMSNorm) and child_name == "input_layernorm":
                    setattr(module, child_name, nn.Identity().to(device="cuda:0"))
              

convert_to_bitnet(model, copy_weights=True)
model.to(device="cuda:0")

prompt = "Привет"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
generate_ids = model.generate(inputs.input_ids, max_length=100)
tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
```

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0015
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results



### Framework versions

- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1