Create README.md

README.md

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+---
+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.
+
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6419c2f6b4adb0e101b17b6c/X6O_WbSqbdOWjhTm0tWU1.png)
+
+## 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
+
+```bash
+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:** 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:
+
+```bash
+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:
+```python
+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! 💬✨
+
+```text
+- 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](https://github.com/ejbejaranos) 🌐