ejbejaranos_-_Llama3-8B-to2B-BitnetDownscaling-4bits

Quantization made by Richard Erkhov.

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Llama3-8B-to2B-BitnetDownscaling - bnb 4bits

• Model creator: https://huggingface.co/ejbejaranos/
• Original model: https://huggingface.co/ejbejaranos/Llama3-8B-to2B-BitnetDownscaling/

Original model description:

library_name: transformers license: apache-2.0 datasets:

• abideen/Cosmopedia-100k-pretrain language:
• en base_model:
• meta-llama/Llama-3.1-8B-Instruct

---

🚀 Llama3-8B-to2B-BitnetDownscaling (from 8B to 2B) Transformation & Training

This project transforms a Llama3 model from 8B parameters to a BitNet architecture with 2B parameters, applying BitLinear layers. Additionally, the model is trained with a predefined dataset and uploaded to Hugging Face for future use.

Features 🌈

• Model Size: 8B parameters 🧠
• Architecture: BitNet 🏗️
• Bitlinear Layers: Reduces weights to values of 1, 0, and -1. ➖
• Optimized for: Fast inference and memory efficiency ⚡

Architecture

LlamaForCausalLM(
  (model): LlamaModel(
    (embed_tokens): Embedding(128256, 4096)
    (layers): ModuleList(
      (0-5): 6 x LlamaDecoderLayer(
        (self_attn): LlamaSdpaAttention(
          (q_proj): BitLinear(in_features=4096, out_features=4096, bias=False)
          (k_proj): BitLinear(in_features=4096, out_features=1024, bias=False)
          (v_proj): BitLinear(in_features=4096, out_features=1024, bias=False)
          (o_proj): BitLinear(in_features=4096, out_features=4096, bias=False)
          (rotary_emb): LlamaRotaryEmbedding()
        )
        (mlp): LlamaMLP(
          (gate_proj): BitLinear(in_features=4096, out_features=14336, bias=False)
          (up_proj): BitLinear(in_features=4096, out_features=14336, bias=False)
          (down_proj): BitLinear(in_features=14336, out_features=4096, bias=False)
          (act_fn): SiLU()
        )
        (input_layernorm): Identity()
        (post_attention_layernorm): LlamaRMSNorm((4096,), eps=1e-05)
      )
    )
    (norm): LlamaRMSNorm((4096,), eps=1e-05)
    (rotary_emb): LlamaRotaryEmbedding()
  )
  (lm_head): Linear(in_features=4096, out_features=128256, bias=False)
)

---

Model Description

<!-- Provide a longer summary of what this model is. -->

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

• Developed by: [email protected] && [email protected]
• Funded by [optional]: ITCL
• Model type: LLama3 8B Tramsformed to Bitnet using Downscaling technique
• Language(s) (NLP): Bitnet
• License: [More Information Needed]
• Finetuned from model [optional]: [More Information Needed]

Requirements 📦

Make sure you have the following libraries installed:

pip install transformers torch huggingface_hub wandb coloredlogs

You can install these dependencies using pip! 🎉

Usage 🔍

Loading the Model

To load the model, you can simply run the following code:

Para usar este modelo, puedes cargarlo desde Hugging Face con el siguiente código:

from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.models.llama.modeling_llama import *
import torch
from torch import nn
import torch.nn.functional as F
import coloredlogs
import logging


coloredlogs.install(level='INFO', fmt='%(asctime)s - %(levelname)s - %(message)s', logger=logging.getLogger())
logger = logging.getLogger(__name__)




HF_TOKEN = "you_api_key_here"

model = "ejbejaranos/Llama3-8B-ITCL-Bitnet1.6B"

# Load a pretrained BitNet model
tokenizer = AutoTokenizer.from_pretrained(model)

model = AutoModelForCausalLM.from_pretrained(
    model,
    token=HF_TOKEN
)

# Establece el pad_token_id
model.config.pad_token_id = tokenizer.eos_token_id

def count_parameters(model):
    # Calculate the number of parameters in billions
    num_params = sum(p.numel() for p in model.parameters() if p.requires_grad) / 10**9
    print(f"Model size: {num_params:.3f}B parameters")
    return int(num_params)

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)
        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():
        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)
        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")


logger.info(f"🔢 Number of parameters in the model after extracting weights: {count_parameters(model)}")
logger.info(f"📏 Reduced model structure:\n{model}")





prompt = "What is the color of sky?"
inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True).to(model.device)
inputs['attention_mask'] = inputs['input_ids'] != model.config.pad_token_id

generate_ids = model.generate(inputs.input_ids, attention_mask=inputs['attention_mask'], max_length=250)
decoded_output = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)

print(decoded_output[0])  # Print the generated response

Performing Inference

Generate text using the model to unleash its power! 💬✨

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Contact 📫

For questions or suggestions, feel free to reach out to me:

• Email: [email protected]
• GitHub: ejbejaranos 🌐