--- datasets: - TinyStories language: - en base_model: - timmyd69/Lama2-7b-chat-hf-oslo-merged-model-Q2_K-GGUF --- Leap0 Model ### Model Description This is the Leap0 model, designed for text generation tasks. It leverages the GPT-2 tokenizer and architecture but is specifically trained on the Tiny Stories dataset. ## Model Architecture - **Model Type**: Custom GPT-2 - **Number of Layers**: 8 - **Number of Heads**: 8 - **Embedding Size**: 768 - **Block Size**: 768 - **Vocabulary Size**: 50257 - **Dropout Rate**: 0.1 - **Attention Mechanism**: Causal Self-Attention - **Encoding**: GPT-2 Tokenizer ## Training Details - **Dataset**: Tiny Stories ## How to Use # change the input as per your desired string """ import torch import json from transformers import GPT2Tokenizer from safetensors.torch import load_file import os import math import time import inspect from dataclasses import dataclass import torch import torch.nn as nn from torch.nn import functional as F from datasets import load_dataset # Load the dataset dataset = load_dataset("hellaswag", trust_remote_code=True) print(dataset) # Define the CausalSelfAttention class class CausalSelfAttention(nn.Module): def __init__(self, config): super().__init__() assert config.n_embd % config.n_head == 0 self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd) self.c_proj = nn.Linear(config.n_embd, config.n_embd) self.c_proj.NANOGPT_SCALE_INIT = 1 self.n_head = config.n_head self.n_embd = config.n_embd def forward(self, x): B, T, C = x.size() qkv = self.c_attn(x) q, k, v = qkv.split(self.n_embd, dim=2) k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) y = F.scaled_dot_product_attention(q, k, v, is_causal=True) y = y.transpose(1, 2).contiguous().view(B, T, C) y = self.c_proj(y) return y # Define the MLP class class MLP(nn.Module): def __init__(self, config): super().__init__() self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd) self.gelu = nn.GELU(approximate='tanh') self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd) self.c_proj.NANOGPT_SCALE_INIT = 1 def forward(self, x): x = self.c_fc(x) x = self.gelu(x) x = self.c_proj(x) return x # Define the Block class class Block(nn.Module): def __init__(self, config): super().__init__() self.ln_1 = nn.LayerNorm(config.n_embd) self.attn = CausalSelfAttention(config) self.ln_2 = nn.LayerNorm(config.n_embd) self.mlp = MLP(config) def forward(self, x): x = x + self.attn(self.ln_1(x)) x = x + self.mlp(self.ln_2(x)) return x # Define the GPTConfig class @dataclass class GPTConfig: block_size: int = 768 vocab_size: int = 50257 n_layer: int = 8 n_head: int = 8 n_embd: int = 768 dropout: float = 0.1 model_type: str = "custom_gpt" def to_dict(self): return self.__dict__ @classmethod def from_dict(cls, config_dict): return cls(**config_dict) # Define the GPT class class GPT(nn.Module): def __init__(self, config): super().__init__() self.config = config self.transformer = nn.ModuleDict(dict( wte=nn.Embedding(config.vocab_size, config.n_embd), wpe=nn.Embedding(config.block_size, config.n_embd), h=nn.ModuleList([Block(config) for _ in range(config.n_layer)]), ln_f=nn.LayerNorm(config.n_embd), )) self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False) # Weight sharing scheme self.transformer.wte.weight = self.lm_head.weight # Initialize parameters self.apply(self._init_weights) def _init_weights(self, module): if isinstance(module, nn.Linear): std = 0.02 if hasattr(module, 'NANOGPT_SCALE_INIT'): std *= (2 * self.config.n_layer) ** -0.5 torch.nn.init.normal_(module.weight, mean=0.0, std=std) if module.bias is not None: torch.nn.init.zeros_(module.bias) elif isinstance(module, nn.Embedding): torch.nn.init.normal_(module.weight, mean=0.0, std=0.02) def forward(self, idx, targets=None): B, T = idx.size() assert T <= self.config.block_size, f"Cannot forward sequence of length {T}, block size is only {self.config.block_size}" pos = torch.arange(0, T, dtype=torch.long, device=idx.device) pos_emb = self.transformer.wpe(pos) tok_emb = self.transformer.wte(idx) x = tok_emb + pos_emb for block in self.transformer.h: x = block(x) x = self.transformer.ln_f(x) logits = self.lm_head(x) loss = None if targets is not None: loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1)) return logits, loss # Manually specify the paths to the config and model files config_path = "/home/nll-workstation/Desktop/config.json" model_path = "/home/nll-workstation/Desktop/model.safetensors" # Load the configuration from the specified JSON file with open(config_path, "r") as f: config_dict = json.load(f) config = GPTConfig.from_dict(config_dict) # Load the model weights from the specified .safetensors file tensors = load_file(model_path) # Instantiate the model with the loaded config model = GPT(config) # Load the state dict (weights) into the model model.load_state_dict(tensors, strict=False) # Set the model to evaluation mode model.eval() # Load the tokenizer (same tokenizer used during training) tokenizer = GPT2Tokenizer.from_pretrained("gpt2") # Prepare input text and tokenize it input_text = "once upon a time in the village of " input_ids = tokenizer.encode(input_text, return_tensors="pt") # Run inference (forward pass) through the model logits, _ = model(input_ids) # Forward pass, extract logits from the tuple # Get predicted token IDs by taking the argmax of logits predicted_ids = torch.argmax(logits, dim=-1) # Convert predicted token IDs to text output_text = tokenizer.decode(predicted_ids[0], skip_special_tokens=True) # Print input and output print("Input Text:", input_text) print("Output Text:", output_text) """