README.md

metadata

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

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: ejbejaranos@gmail.com && lidia.andres@itcl.es
• 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! 💬✨

- What role does explainability play in your AI solutions?

How can you ensure that your AI system is able to accurately predict and respond to user inputs?
These are some of the questions that AI developers have been asking themselves in the last few years.
In this section, we will explore some of the key concepts and techniques that AI developers have used to develop in their AI systems.

First, let's consider the importance of understanding the role of AI in AI.
AI systems can be incredibly powerful tools for automating tasks, analyzing data, and identifying patterns.
They can analyze large datasets and identify patterns, trends, and anomalies that might be missed by human analysts.
By analyzing large datasets, AI can help identify patterns and trends that might otherwise go unnoticed.

One of the most significant challenges in AI development is the lack of transparency and accountability.
With AI systems becoming increasingly sophisticated, there is a growing need for transparency and accountability in AI development.
This means that there is a growing need for transparency and accountability in AI development.
However, as AI becomes more sophisticated, it can also lead to unintended consequences, such as job loss or reputational damage.

Contact 📫

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

• Email: ejbejaranos@gmail.com
• GitHub: ejbejaranos 🌐