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Initial release of Alpha Transformer.
Added model weights, configuration files, vocabularies, tokenizer, and inference code.
51M parameter English-French Transformer trained from scratch on OPUS-100.

README.md

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+# Alpha Transformer
+
+A custom **Encoder-Decoder Transformer model trained from scratch** for English → French translation.
+
+Alpha is a 51M parameter Transformer implemented entirely in PyTorch, including:
+- Multi-Head Attention
+- Rotary Positional Embeddings (RoPE)
+- Encoder-Decoder architecture
+- Cross Attention
+- Custom tokenizer
+- Mixed precision training
+- Checkpointing system
+
+The model was trained on the **OPUS-100 English-French translation dataset**.
+
 ---
-license: mit
+
+## Model Overview
+
+| Feature | Value |
+|---|---|
+| Architecture | Encoder-Decoder Transformer |
+| Parameters | 51.26M |
+| Framework | PyTorch |
+| Task | English → French Translation |
+| Dataset | OPUS-100 |
+| Training Samples | ~1M sentences |
+| Embedding Dimension | 256 |
+| Attention Heads | 8 |
+| Feed Forward Dimension | 1024 |
+| Encoder Layers | 3 |
+| Decoder Layers | 3 |
+| Maximum Sequence Length | 64 tokens |
+| Positional Encoding | RoPE |
+
 ---
+
+# Performance
+
+Evaluation on the OPUS-100 test set:
+
+| Metric | Result |
+|---|---|
+| Token Accuracy | 62.8% |
+
+The model was trained from scratch without using pretrained weights.
+
+---
+
+# Architecture
+
+## Encoder
+
+Each encoder block contains:
+
+Input Embeddings
+|
+v
+LayerNorm
+|
+Multi-Head Self Attention + RoPE
+|
+Residual Connection
+|
+LayerNorm
+|
+Feed Forward Network
+|
+Residual Connection
+
+## Decoder
+
+Each decoder block contains:
+
+Target Embeddings
+|
+LayerNorm
+|
+Masked Self Attention + RoPE
+|
+Residual Connection
+|
+LayerNorm
+|
+Cross Attention
+|
+Residual Connection
+|
+LayerNorm
+|
+Feed Forward Network
+|
+Residual Connection
+
+---
+
+# Files
+
+
+Alpha/
+│
+├── config.json
+│ └── Model architecture configuration
+│
+├── model.safetensors
+│ └── Trained model weights
+│
+├── src_vocab.json
+│ └── English vocabulary
+│
+├── tgt_vocab.json
+│ └── French vocabulary
+│
+├── tokenizer.py
+│ └── Custom tokenizer implementation
+│
+├── main.py
+│ └── Transformer architecture
+│
+├── inference.py
+│ └── Translation script
+│
+└── README.md
+
+---
+
+# Installation
+
+Clone the repository:
+
+```bash
+git clone https://github.com/MehdiSkilll24/Alpha.git
+cd Alpha
+
+Install dependencies:
+
+pip install torch safetensors
+
+Usage
+
+Run inference:
+
+python inference.py
+
+Example:
+
+Input:
+> hello, how are you?
+
+Output:
+> bonjour, comment allez-vous ?
+
+
+
+Loading the Model Manually
+import torch
+import json
+from safetensors.torch import load_file
+from main import Transformer
+
+with open("config.json") as f:
+    config = json.load(f)
+
+weights = load_file("model.safetensors")
+
+model = Transformer(
+    src_vocab_size=config["src_vocab_size"],
+    tgt_vocab_size=config["tgt_vocab_size"],
+    d_model=config["d_model"],
+    num_heads=config["num_heads"],
+    d_ff=config["d_ff"]
+)
+
+model.load_state_dict(weights)
+model.eval()
+
+
+Training
+
+The model was trained using:
+
+AdamW optimizer
+Mixed precision training (FP16)
+ReduceLROnPlateau scheduler
+Custom PyTorch training loop
+
+Training configuration:
+
+Learning Rate: 3e-4
+Batch Size: 32
+Epochs: 18
+Dataset
+
+Training data:
+
+OPUS-100 English-French translation dataset.
+
+The dataset contains parallel English/French sentence pairs collected from various sources.
+
+Limitations
+
+This model is designed as an educational and experimental Transformer implementation.
+
+Limitations:
+
+Vocabulary is based on a custom word-level tokenizer
+Limited context length
+No pretrained initialization
+Smaller dataset compared to production translation systems
+Token accuracy does not directly represent BLEU score
+Future Improvements
+
+Possible improvements:
+
+Replace word tokenizer with BPE/SentencePiece
+Increase model depth
+Train on larger datasets
+Add beam search decoding
+Evaluate with BLEU / COMET
+Implement KV-cache for faster inference
+Motivation
+
+Alpha was built to understand and implement the Transformer architecture from first principles.
+
+Rather than using existing libraries, the project implements the core components manually:
+
+Attention calculations
+Positional embeddings
+Encoder/decoder blocks
+Training pipeline
+Model serialization
+
+The goal is to better understand how modern language models are built internally.
+
+Source Code
+
+GitHub:
+
+https://github.com/MehdiSkilll24/Alpha
+
+Model weights:
+
+https://huggingface.co/MehdiSkilll24/Alpha
+
+License
+
+MIT License
+
+
+One suggestion before you upload: add **3-5 real translation examples** from your model. That will make the HF page look 10x more alive. A model card with "62.8% accuracy" is okay; a model card with:
+
+
+English:
+"The weather is beautiful today."
+
+Alpha:
+"Le temps est magnifique aujourd'hui."

config.json

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+{
+    "model_name": "Alpha-Transformer",
+    "model_type": "encoder-decoder-transformer",
+
+    "d_model": 256,
+    "num_heads": 8,
+    "d_ff": 1024,
+
+    "num_encoder_layers": 3,
+    "num_decoder_layers": 3,
+
+    "src_vocab_size": 51278,
+    "tgt_vocab_size": 63558,
+
+    "max_seq_length": 64,
+    "rope_max_seq_length": 1024,
+
+    "positional_encoding": "RoPE",
+    "activation": "ReLU",
+    "normalization": "Pre-LayerNorm",
+
+    "num_parameters": 51262022,
+
+    "dataset": "OPUS-100",
+    "task": "English-French machine translation",
+
+    "framework": "PyTorch",
+
+    "pad_token_id": 0,
+    "unk_token_id": 1,
+    "bos_token_id": 2,
+    "eos_token_id": 3
+}

inference.py

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+import torch
+from main import Transformer
+from tokenizer import Tokenizer
+import json
+from safetensors.torch import load_file
+
+with open("config.json") as f:
+    config = json.load(f)
+
+
+def rebuild_tokenizer(word_to_idx):
+    tok = Tokenizer()
+
+    tok.word_to_idx = word_to_idx
+
+    tok.idx_to_word = {
+        int(idx): word
+        for word, idx in word_to_idx.items()
+    }
+
+    return tok
+
+
+def translate(model, src_tokenizer, tgt_tokenizer, sentence, device, max_len=50):
+    model.eval()
+
+    src_ids = src_tokenizer.encode(sentence)
+    src_tensor = torch.tensor([src_ids]).to(device)
+
+    decoder_input = torch.tensor(
+        [[tgt_tokenizer.word_to_idx["<bos>"]]]
+    ).to(device)
+
+    with torch.no_grad():
+
+        for _ in range(max_len):
+
+            logits = model(src_tensor, decoder_input)
+
+            next_token = logits[:, -1, :].argmax(dim=-1).item()
+
+            decoder_input = torch.cat(
+                [
+                    decoder_input,
+                    torch.tensor([[next_token]]).to(device)
+                ],
+                dim=1
+            )
+
+            if next_token == tgt_tokenizer.word_to_idx["<eos>"]:
+                break
+
+    output_ids = decoder_input[0].tolist()
+
+    return tgt_tokenizer.decode(output_ids)
+
+
+def main():
+
+    device = torch.device(
+        "cuda" if torch.cuda.is_available() else "cpu"
+    )
+
+
+    # Load vocabularies
+    with open("src_vocab.json", "r", encoding="utf-8") as f:
+        src_vocab = json.load(f)
+
+    with open("tgt_vocab.json", "r", encoding="utf-8") as f:
+        tgt_vocab = json.load(f)
+
+
+    # Rebuild tokenizers
+    src_tokenizer = rebuild_tokenizer(src_vocab)
+    tgt_tokenizer = rebuild_tokenizer(tgt_vocab)
+
+
+    # Load config
+    with open("config.json", "r", encoding="utf-8") as f:
+        config = json.load(f)
+
+
+    # Build model
+    model = Transformer(
+    src_vocab_size=config["src_vocab_size"],
+    tgt_vocab_size=config["tgt_vocab_size"],
+    d_model=config["d_model"],
+    num_heads=config["num_heads"],
+    d_ff=config["d_ff"]
+    ).to(device)
+
+
+    # Load weights
+    weights = load_file(
+        "model.safetensors"
+    )
+
+    model.load_state_dict(weights)
+
+    model.eval()
+
+
+    print("Type an English sentence (or 'quit' to exit):")
+
+    while True:
+
+        sentence = input("> ")
+
+        if sentence.lower() == "quit":
+            break
+
+        translation = translate(
+            model,
+            src_tokenizer,
+            tgt_tokenizer,
+            sentence,
+            device
+        )
+
+        print(f"Translation: {translation}")
+
+
+if __name__ == "__main__":
+    main()

main.py

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+import torch.nn as nn
+import torch
+
+class RoPE(nn.Module):
+    def __init__(self, dim, max_seq_len=1024):
+        super().__init__()
+
+        theta = 10000
+
+        freqs = 1.0 / (theta ** (torch.arange(0, dim, 2).float() / dim))
+
+        positions = torch.arange(max_seq_len)
+
+        angles = torch.outer(positions, freqs)
+
+        self.register_buffer(
+            "cos", 
+            angles.cos()
+        )
+
+        self.register_buffer(
+            "sin", 
+            angles.sin()
+        )
+    
+    def rotate_half(self, x):
+        x1 = x[..., ::2]
+        x2 = x[..., 1::2]
+
+        return torch.stack(
+            (-x2,x1),
+            dim=-1
+        ).flatten(-2)
+    
+    def forward(self, x):
+        
+        seq_len = x.shape[-2]
+        cos = self.cos[:seq_len]
+        sin = self.sin[:seq_len]
+
+        cos = torch.repeat_interleave(cos, 2, dim=-1)
+        sin = torch.repeat_interleave(sin, 2, dim=-1)
+
+        return x*cos + self.rotate_half(x)*sin
+
+class MHA(nn.Module):
+    def __init__(self, d_model, num_heads):
+        super().__init__()
+        super().__init__()
+
+        self.num_heads = num_heads
+        self.d_model = d_model
+        
+        self.Q = nn.Linear(d_model, d_model)
+        self.K = nn.Linear(d_model, d_model)
+        self.V = nn.Linear(d_model, d_model)
+        self.W_o = nn.Linear(d_model, d_model)
+
+        self.d_k = d_model // num_heads
+        self.rope = RoPE(self.d_k)
+
+    def forward(self, x, kv_input=None, mask=None):
+        batch_size = x.size(0)
+        # x.shape = (2, 3, 64)  64 dictates that there will be 64 Q, K and V PER TOKEN 
+        
+        if kv_input is None:
+            kv_input = x
+  
+        Q = self.Q(x) # we do q_1 = XWq = 'W_1'*'e1' + ... + 'W_64'*'e64' W_i being the weight at i ... until we comp q_64, if no kv_input, it means we're doing encoding
+        K = self.K(kv_input) # ,, ,, 
+        V = self.V(kv_input) # ,, ,, 
+        # We do this to ensure all Q,K and V's matrices have access to the input embeddings
+        
+        # Manual length extraction to avoid mismatch in size from encoder/decoder
+        q_seq_length = x.shape[1]
+        kv_seq_length = kv_input.shape[1]
+
+        # We then split all this data through different heads so each one goes for a raneg window for each token (split ranges -> specialized heads)
+        Q = Q.view(batch_size, q_seq_length, self.num_heads, self.d_k)
+        K = K.view(batch_size, kv_seq_length, self.num_heads, self.d_k)
+        V = V.view(batch_size, kv_seq_length, self.num_heads, self.d_k)
+
+        # So we get (batch, heads, seq_len, d_k) last 2 dims are the ones that get multiplied by Pytorch
+        # The reason we put seq_len, d_k is because we gotta multiply them indep. of how many heads or batches, what matters is the consistent output per head per batch
+        # Also, since we're computing relationships between tokens, there must be seq_len and d_k, so we can compare all tokens with all tokens for every head in every batch
+        Q = Q.transpose(1,2)                   
+        K = K.transpose(1,2)
+        V = V.transpose(1,2)
+
+        Q = self.rope(Q)
+        K = self.rope(K)
+        
+        scores = Q @ K.transpose(-2, -1) / self.d_k**0.5   #we swap d_k with seq_len so we get #seq_len * #seq_len matrix as a result
+
+        if mask is not None:
+            scores += mask
+        
+        attn_weights = torch.softmax(scores, dim=-1)
+        output = attn_weights @ V # contiguous vector of shape (batch, heads, seq, d_k)
+        output = output.transpose(1,2).contiguous().view(batch_size, q_seq_length, self.d_model) # transposing makes it breaks contiguousy, so we add .contiguous() and then .view() to reshape it with concatenation
+        output = self.W_o(output) # expressing the new shape correctly accross heads
+
+        return output
+    
+class MLP(nn.Module):
+    
+    def __init__(self, d_model, d_ff):
+        super().__init__()
+        self.fc1 = nn.Linear(d_model, d_ff)
+        self.relu = nn.ReLU()
+        self.fc2 = nn.Linear(d_ff, d_model)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.relu(x)
+        x = self.fc2(x)
+
+        return x
+    
+
+class EncoderBlock(nn.Module):
+    def __init__(self, d_model, num_heads, d_ff):
+        super().__init__()
+        self.mha = MHA(d_model, num_heads)
+        self.norm1 = nn.LayerNorm(d_model)
+        self.norm2 = nn.LayerNorm(d_model)
+        self.mlp = MLP(d_model, d_ff)
+
+    def forward(self, x):
+        normed = self.norm1(x)
+        attn_out = self.mha(normed)
+        x = x + attn_out
+
+        normed2 = self.norm2(x)
+        mlp_out = self.mlp(normed2)
+        x = x + mlp_out
+
+        return x
+    
+class DecoderBlock(nn.Module):
+    def __init__(self, d_model, num_heads, d_ff):
+        super().__init__()
+        self.self_attn = MHA(d_model, num_heads)
+        self.cross_attn = MHA(d_model, num_heads)
+        self.norm1 = nn.LayerNorm(d_model)
+        self.norm2 = nn.LayerNorm(d_model)
+        self.norm3 = nn.LayerNorm(d_model)
+        self.mlp = MLP(d_model, d_ff)
+
+    def forward(self, x, encoder_output):
+        causal_mask = torch.triu(torch.full((x.shape[1], x.shape[1]), float('-inf'), device=x.device), diagonal=1)
+        normed = self.norm1(x)
+        attn_out = self.self_attn(normed, mask = causal_mask)
+        x = x + attn_out
+
+        normed2 = self.norm2(x)
+        cross_out = self.cross_attn(normed2, encoder_output)
+        x = x + cross_out
+
+        normed3 = self.norm3(x)
+        mlp_out = self.mlp(normed3)
+        x = x + mlp_out
+
+        return x
+    
+class Transformer(nn.Module):
+    def __init__(self, src_vocab_size, tgt_vocab_size, d_model, num_heads, d_ff):
+        super().__init__()
+
+        self.src_embedding = nn.Embedding(src_vocab_size, d_model)
+        self.tgt_embedding = nn.Embedding(tgt_vocab_size, d_model)
+        self.encoder = nn.ModuleList([EncoderBlock(d_model, num_heads, d_ff) for _ in range(3)])
+        self.decoder = nn.ModuleList([DecoderBlock(d_model, num_heads, d_ff) for _ in range(3)])
+        self.output_proj = nn.Linear(d_model, tgt_vocab_size)
+
+    def forward(self, src_tokens, tgt_tokens):
+        src = self.src_embedding(src_tokens)
+        tgt = self.tgt_embedding(tgt_tokens)
+
+        for block in self.encoder:
+            src = block(src)
+        
+        encoder_output = src
+        
+        for block in self.decoder:
+            tgt = block(tgt, encoder_output)
+
+        logits = self.output_proj(tgt)
+        return logits
+
+

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4533ddea34e9a72f8cd42c11d2933ed5cd0363dbc4f67c9adf9a226afa779cce
+size 206241552

tokenizer.py

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+import re
+from collections import Counter
+
+class Tokenizer:
+    def __init__(self, min_freq = 5):
+        self.min_freq = min_freq
+
+        self.word_to_idx = {}
+        self.idx_to_word = {}
+
+        self.special_tokens = {
+            "<pad>" : 0, 
+            "<unk>" : 1, 
+            "<bos>" : 2, 
+            "<eos>" : 3, 
+        }
+
+    def __len__(self):
+        return len(self.word_to_idx)
+
+    def tokenize(self, text):
+        
+        # raw text -> list of words
+
+        text = text.lower()
+
+        # split words and punctuations
+
+        tokens = re.findall(r"\w+|[^\w\s]", text)
+
+        return tokens
+    
+    def build_vocab(self, texts):
+
+        # create word -> id mapping
+
+        counter = Counter()
+
+        for text in texts:
+            tokens = self.tokenize(text)
+            counter.update(tokens)
+
+        self.word_to_idx = self.special_tokens.copy()
+
+        for word, freq in sorted(counter.items()):
+            if freq >= self.min_freq:
+                if word not in self.word_to_idx:
+                    self.word_to_idx[word] = len(self.word_to_idx)
+        
+        self.idx_to_word = {
+            idx: word for word, idx in self.word_to_idx.items()
+        }
+
+    def encode(self, text):
+
+        # text -> ids 
+        text = text.lower()
+
+        tokens = self.tokenize(text)
+
+        ids = [
+            self.word_to_idx.get(
+                token,
+                self.word_to_idx["<unk>"]
+            )
+            for token in tokens
+        ]
+
+        ids = (
+            [self.word_to_idx["<bos>"]]
+            + ids
+            +[self.word_to_idx["<eos>"]]
+        )
+        
+        return ids
+    
+    def decode(self, ids):
+        words = []
+
+        for idx in ids:
+
+            word = self.idx_to_word.get(
+                idx,
+                "<unk>"
+            )
+
+            if word not in [
+                "<pad>",
+                "<bos>",
+                "<eos>",
+                "<unk>"
+            ]:
+                words.append(word)
+
+        return " ".join(words)