RuBit-Llama-63M
This model is a fine-tuned version of 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
Model description
Sample inference code
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
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Model tree for igorktech/RuBit-LLama-63M
Base model
NousResearch/Llama-2-7b-hf