license: artistic-2.0
datasets:
- Siddharth63/biological_dataset
- Siddharth63/clinical_dataset
BitNEt 250 M trained on 7B tokens on PubMed + Clinical dataset
Inference code: from transformers import AutoModelForCausalLM, AutoTokenizer from transformers.models.llama.modeling_llama import *
Load a pretrained BitNet model
model = "Siddharth63/Bitnet-250M" tokenizer = AutoTokenizer.from_pretrained(model) model = AutoModelForCausalLM.from_pretrained(model)
def activation_quant(x): scale = 127.0 / x.abs().max(dim=-1, keepdim=True).values.clamp_(min=1e-5) y = (x * scale).round().clamp_(-128, 127) y = y / scale return y def weight_quant(w): scale = 1.0 / w.abs().mean().clamp_(min=1e-5) u = (w * scale).round().clamp_(-1, 1) u = u / scale return u
class BitLinear(nn.Linear): def forward(self, x): w = self.weight # a weight tensor with shape [d, k] x = x.to(w.device) RMSNorm = LlamaRMSNorm(x.shape[-1]).to(w.device) x_norm = RMSNorm(x) # A trick for implementing Straight−Through−Estimator (STE) using detach() x_quant = x_norm + (activation_quant(x_norm) - x_norm).detach() w_quant = w + (weight_quant(w) - w).detach() y = F.linear(x_quant, w_quant) return y
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 = "Atherosclerosis is" inputs = tokenizer(prompt, return_tensors="pt").to(model.device) generate_ids = model.generate(inputs.input_ids, max_length=50) tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]