uploading tuned model

README.md

@@ -1,3 +1,44 @@
 ---
+language:
+- en
 license: apache-2.0
+library_name: transformers
+tags:
+- gemma
+- text-generation
+- pytorch
+- causal-lm
+- custom-architecture
+datasets:
+- HuggingFaceTB/cosmopedia
+pipeline_tag: text-generation
 ---
+
+# The Original Model: GrownUpBaby-110M 👶➡️👨‍🎓
+
+"Bedtime stories grew up."
+
+**GrownUpBaby-110M** is a compact, Gemma-style causal LLM (110,304,256 parameters) trained from scratch to be a capable storyteller and creative assistant, with strong coherence and thematic control despite its size.
+It's the grown-up counterpart to my earlier **[Exquisique/BabyLangModel](https://huggingface.co/Exquisique/BabyLangModel)**—an LLM with fewer parameters (30M) trained from scratch on TinyStories to generate short, simple narratives for young readers.
+With more room to breathe, GrownUpBaby aims for richer voice, longer arcs, and cleaner pacing—built to read like a **master storyteller** on consumer hardware.
+
+# The Fine-Tuned Model: NPC_brain
+
+Using the Storytelling prowess of GrownUpBaby, I added training data to specialize the model in formulating NPC descriptions and backstories for an NPC Generator! 
+
+In most cases, I took a cool thing, and made it useless for 98% of huggingface. But think of the lore you can create!
+
+## 📚 Training Data
+I trained this model using **Modal** ![alt text](image.png) and a custom dataset found at https://huggingface.co/datasets/mecoffey/npc_dataset
+
+
+## ⚠️ Limitations & Bias
+
+*   **Size:** At 110M parameters, this model has limited "world knowledge" compared to 7B+ models. It is best suited for creative writing and simple instruction following.
+*   **Hallucinations:** It may generate plausible-sounding but factually incorrect information.
+*   **Language:** Trained primarily on English educational and story data.
+
+## 👨‍💻 Author
+
+Trained by **Exquisique** 
+Fine-Tuned by **mecoffey**

config.json

@@ -0,0 +1,25 @@
+{
+  "hidden_size": 768,
+  "num_layers": 12,
+  "intermediate_size": 3072,
+  "num_attention_heads": 12,
+  "num_key_value_heads": 4,
+  "max_position_embeddings": 1024,
+  "vocab_size": 50257,
+  "rope_theta": 10000.0,
+  "rms_norm_eps": 1e-06,
+  "hidden_act": "gelu_pytorch_tanh",
+  "attention_dropout": 0.0,
+  "hidden_dropout": 0.0,
+  "tie_word_embeddings": false,
+  "initializer_range": 0.02,
+  "architectures": [
+    "GemmaForCausalLM"
+  ],
+  "auto_map": {
+    "AutoConfig": "model.GemmaConfig",
+    "AutoModelForCausalLM": "model.GemmaForCausalLM"
+  },
+  "model_type": "gemma_custom",
+  "torch_dtype": "float32"
+}

model.py

@@ -0,0 +1,540 @@
+# =============================================================================
+# Gemma-Style Model Architecture (110M Parameters)
+# =============================================================================
+"""
+This module implements a Gemma-style transformer architecture with:
+- RMSNorm (Root Mean Square Layer Normalization)
+- RoPE (Rotary Position Embeddings)
+- GeGLU (Gated Linear Unit with GELU activation)
+- GQA (Grouped Query Attention)
+
+Architecture Specifications:
+- hidden_size: 768
+- num_layers: 12
+- intermediate_size: 3072
+- num_attention_heads: 12
+- num_key_value_heads: 4 (GQA ratio of 3:1)
+- vocab_size: 50257 (GPT-2 tokenizer)
+"""
+
+import math
+import logging
+from typing import Optional, Tuple, Dict, Any, Union, List
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch import Tensor
+
+from transformers import PretrainedConfig, PreTrainedModel
+from transformers.modeling_outputs import CausalLMOutputWithPast
+
+logger = logging.getLogger(__name__)
+
+
+class GemmaConfig(PretrainedConfig):
+    """
+    Configuration class for the Gemma-style model.
+    
+    Inherits from HuggingFace PretrainedConfig for compatibility with
+    AutoConfig and the transformers ecosystem.
+    """
+    model_type = "gemma_custom"
+    
+    def __init__(
+        self,
+        hidden_size: int = 768,
+        num_layers: int = 12,
+        intermediate_size: int = 3072,
+        num_attention_heads: int = 12,
+        num_key_value_heads: int = 4,
+        max_position_embeddings: int = 1024,
+        vocab_size: int = 50257,
+        rope_theta: float = 10000.0,
+        rms_norm_eps: float = 1e-6,
+        hidden_act: str = "gelu_pytorch_tanh",
+        attention_dropout: float = 0.0,
+        hidden_dropout: float = 0.0,
+        tie_word_embeddings: bool = True,
+        initializer_range: float = 0.02,
+        **kwargs
+    ):
+        self.hidden_size = hidden_size
+        self.num_layers = num_layers
+        self.intermediate_size = intermediate_size
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.max_position_embeddings = max_position_embeddings
+        self.vocab_size = vocab_size
+        self.rope_theta = rope_theta
+        self.rms_norm_eps = rms_norm_eps
+        self.hidden_act = hidden_act
+        self.attention_dropout = attention_dropout
+        self.hidden_dropout = hidden_dropout
+        self.initializer_range = initializer_range
+        
+        # Derived attributes
+        self.head_dim = self.hidden_size // self.num_attention_heads
+        self.num_key_value_groups = self.num_attention_heads // self.num_key_value_heads
+        
+        super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
+
+
+class RMSNorm(nn.Module):
+    """
+    Root Mean Square Layer Normalization.
+    
+    RMSNorm(x) = x * rsqrt(mean(x^2) + eps) * weight
+    """
+    
+    def __init__(self, hidden_size: int, eps: float = 1e-6):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states: Tensor) -> Tensor:
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+
+class RotaryEmbedding(nn.Module):
+    """Rotary Position Embedding (RoPE)."""
+    
+    def __init__(self, dim: int, max_position_embeddings: int = 1024, theta: float = 10000.0):
+        super().__init__()
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        self.theta = theta
+        
+        inv_freq = 1.0 / (self.theta ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self._set_cos_sin_cache(max_position_embeddings)
+
+    def _set_cos_sin_cache(self, seq_len: int):
+        t = torch.arange(seq_len, dtype=torch.float32)
+        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos(), persistent=False)
+        self.register_buffer("sin_cached", emb.sin(), persistent=False)
+
+    def forward(self, x: Tensor, position_ids: Tensor) -> Tuple[Tensor, Tensor]:
+        seq_len = position_ids.max() + 1
+        if seq_len > self.cos_cached.shape[0]:
+            self._set_cos_sin_cache(seq_len)
+            self.cos_cached = self.cos_cached.to(x.device)
+            self.sin_cached = self.sin_cached.to(x.device)
+        
+        cos = self.cos_cached[position_ids].unsqueeze(2)
+        sin = self.sin_cached[position_ids].unsqueeze(2)
+        return cos.to(x.dtype), sin.to(x.dtype)
+
+
+def rotate_half(x: Tensor) -> Tensor:
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q: Tensor, k: Tensor, cos: Tensor, sin: Tensor) -> Tuple[Tensor, Tensor]:
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class GemmaMLP(nn.Module):
+    """Gemma-style MLP with GeGLU activation."""
+    
+    def __init__(self, config: GemmaConfig):
+        super().__init__()
+        self.gate_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
+        self.act_fn = nn.GELU(approximate="tanh")
+
+    def forward(self, x: Tensor) -> Tensor:
+        gate = self.act_fn(self.gate_proj(x))
+        up = self.up_proj(x)
+        return self.down_proj(gate * up)
+
+
+class GemmaAttention(nn.Module):
+    """Grouped Query Attention (GQA) module."""
+    
+    def __init__(self, config: GemmaConfig, layer_idx: int):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = config.head_dim
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = config.num_key_value_groups
+        self.attention_dropout = config.attention_dropout
+        
+        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
+        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
+        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
+        
+        self.rotary_emb = RotaryEmbedding(
+            self.head_dim,
+            max_position_embeddings=config.max_position_embeddings,
+            theta=config.rope_theta
+        )
+
+    def forward(
+        self,
+        hidden_states: Tensor,
+        attention_mask: Optional[Tensor] = None,
+        position_ids: Optional[Tensor] = None,
+    ) -> Tensor:
+        batch_size, seq_len, _ = hidden_states.shape
+        
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+        
+        query_states = query_states.view(batch_size, seq_len, self.num_heads, self.head_dim)
+        key_states = key_states.view(batch_size, seq_len, self.num_key_value_heads, self.head_dim)
+        value_states = value_states.view(batch_size, seq_len, self.num_key_value_heads, self.head_dim)
+        
+        cos, sin = self.rotary_emb(query_states, position_ids)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+        
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+        
+        if self.num_key_value_groups > 1:
+            key_states = key_states.repeat_interleave(self.num_key_value_groups, dim=1)
+            value_states = value_states.repeat_interleave(self.num_key_value_groups, dim=1)
+        
+        scale = 1.0 / math.sqrt(self.head_dim)
+        attn_weights = torch.matmul(query_states, key_states.transpose(-2, -1)) * scale
+        
+        if attention_mask is not None:
+            attn_weights = attn_weights + attention_mask
+        
+        attn_weights = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_weights = F.dropout(attn_weights, p=self.attention_dropout, training=self.training)
+        
+        attn_output = torch.matmul(attn_weights, value_states)
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(batch_size, seq_len, self.hidden_size)
+        
+        return self.o_proj(attn_output)
+
+
+class GemmaDecoderLayer(nn.Module):
+    """Single transformer decoder layer with pre-norm architecture."""
+    
+    def __init__(self, config: GemmaConfig, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.self_attn = GemmaAttention(config, layer_idx)
+        self.mlp = GemmaMLP(config)
+
+    def forward(
+        self,
+        hidden_states: Tensor,
+        attention_mask: Optional[Tensor] = None,
+        position_ids: Optional[Tensor] = None,
+    ) -> Tensor:
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        hidden_states = self.self_attn(hidden_states, attention_mask, position_ids)
+        hidden_states = residual + hidden_states
+        
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+        
+        return hidden_states
+
+
+class GemmaModel(nn.Module):
+    """Core Gemma transformer model (without LM head)."""
+    
+    def __init__(self, config: GemmaConfig):
+        super().__init__()
+        self.config = config
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
+        self.layers = nn.ModuleList([
+            GemmaDecoderLayer(config, layer_idx) 
+            for layer_idx in range(config.num_layers)
+        ])
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.gradient_checkpointing = False
+
+    def forward(
+        self,
+        input_ids: Tensor,
+        attention_mask: Optional[Tensor] = None,
+        position_ids: Optional[Tensor] = None,
+    ) -> Tensor:
+        batch_size, seq_len = input_ids.shape
+        hidden_states = self.embed_tokens(input_ids)
+        
+        if position_ids is None:
+            position_ids = torch.arange(seq_len, device=input_ids.device).unsqueeze(0).expand(batch_size, -1)
+        
+        causal_mask = self._create_causal_mask(seq_len, hidden_states.device, hidden_states.dtype)
+        
+        for layer in self.layers:
+            if self.gradient_checkpointing and self.training:
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    layer, hidden_states, causal_mask, position_ids, use_reentrant=False
+                )
+            else:
+                hidden_states = layer(hidden_states, causal_mask, position_ids)
+        
+        return self.norm(hidden_states)
+
+    def _create_causal_mask(self, seq_len: int, device: torch.device, dtype: torch.dtype) -> Tensor:
+        mask = torch.full((seq_len, seq_len), float("-inf"), device=device, dtype=dtype)
+        mask = torch.triu(mask, diagonal=1)
+        return mask.unsqueeze(0).unsqueeze(0)
+
+
+class GemmaForCausalLM(PreTrainedModel):
+    """
+    Gemma model with language modeling head for causal text generation.
+    
+    Inherits from HuggingFace PreTrainedModel for full compatibility with
+    AutoModelForCausalLM and the transformers ecosystem.
+    """
+    config_class = GemmaConfig
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["GemmaDecoderLayer"]
+    _supports_param_buffer_assignment = False  # Fix for accelerate weight loading
+    
+    def __init__(self, config: GemmaConfig):
+        super().__init__(config)
+        self.config = config
+        
+        self.model = GemmaModel(config)
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        
+        if config.tie_word_embeddings:
+            self.lm_head.weight = self.model.embed_tokens.weight
+        
+        self.post_init()
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path, *args, **kwargs):
+        """
+        Custom from_pretrained that properly loads weights for this custom model.
+        This overrides the default behavior to ensure weights are loaded correctly.
+        Supports both .safetensors and .bin formats.
+        """
+        import os
+        from huggingface_hub import hf_hub_download
+        
+        # Get config
+        trust_remote_code = kwargs.pop("trust_remote_code", True)
+        torch_dtype = kwargs.pop("torch_dtype", None)
+        device_map = kwargs.pop("device_map", None)
+        
+        # Load config
+        config = cls.config_class.from_pretrained(
+            pretrained_model_name_or_path, 
+            trust_remote_code=trust_remote_code,
+            **kwargs
+        )
+        
+        # Create model
+        model = cls(config)
+        
+        # Find weight file (try safetensors first, then pytorch_model.bin)
+        weight_file = None
+        if os.path.isdir(pretrained_model_name_or_path):
+            # Check local directory
+            safetensors_path = os.path.join(pretrained_model_name_or_path, "model.safetensors")
+            bin_path = os.path.join(pretrained_model_name_or_path, "pytorch_model.bin")
+            
+            if os.path.exists(safetensors_path):
+                weight_file = safetensors_path
+            elif os.path.exists(bin_path):
+                weight_file = bin_path
+            else:
+                logger.warning(f"No weight file found in {pretrained_model_name_or_path}")
+                return model
+        else:
+            # Download from hub (try safetensors first)
+            try:
+                weight_file = hf_hub_download(
+                    repo_id=pretrained_model_name_or_path,
+                    filename="model.safetensors"
+                )
+            except Exception:
+                weight_file = hf_hub_download(
+                    repo_id=pretrained_model_name_or_path,
+                    filename="pytorch_model.bin"
+                )
+        
+        # Load weights based on file format
+        if weight_file.endswith(".safetensors"):
+            try:
+                from safetensors.torch import load_file
+                state_dict = load_file(weight_file)
+            except ImportError:
+                logger.warning("safetensors not installed, trying torch.load instead")
+                state_dict = torch.load(weight_file, map_location="cpu")
+        else:
+            state_dict = torch.load(weight_file, map_location="cpu")
+        
+        model.load_state_dict(state_dict, strict=False)
+        
+        # Handle dtype and device
+        if torch_dtype is not None:
+            model = model.to(torch_dtype)
+        if device_map == "auto":
+            if torch.cuda.is_available():
+                model = model.to("cuda")
+        elif device_map is not None:
+            model = model.to(device_map)
+        
+        return model
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def _init_weights(self, module: nn.Module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+
+    def num_parameters(self, only_trainable: bool = False) -> int:
+        return sum(p.numel() for p in self.parameters() if not only_trainable or p.requires_grad)
+
+    def forward(
+        self,
+        input_ids: Tensor,
+        attention_mask: Optional[Tensor] = None,
+        position_ids: Optional[Tensor] = None,
+        labels: Optional[Tensor] = None,
+        return_dict: Optional[bool] = None,
+        **kwargs
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        
+        hidden_states = self.model(input_ids, attention_mask, position_ids)
+        logits = self.lm_head(hidden_states)
+        
+        loss = None
+        if labels is not None:
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            loss_fct = nn.CrossEntropyLoss()
+            loss = loss_fct(
+                shift_logits.view(-1, self.config.vocab_size),
+                shift_labels.view(-1)
+            )
+        
+        if not return_dict:
+            output = (logits,)
+            return ((loss,) + output) if loss is not None else output
+        
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=None,
+            hidden_states=None,
+            attentions=None,
+        )
+
+    def prepare_inputs_for_generation(
+        self, input_ids, past_key_values=None, attention_mask=None, **kwargs
+    ):
+        if past_key_values is not None:
+            input_ids = input_ids[:, -1:]
+        
+        position_ids = kwargs.get("position_ids", None)
+        if attention_mask is not None and position_ids is None:
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -1].unsqueeze(-1)
+        
+        return {
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+            "position_ids": position_ids,
+            "past_key_values": past_key_values,
+        }
+
+    def generate(
+        self,
+        input_ids: Tensor,
+        max_new_tokens: int = 50,
+        temperature: float = 1.0,
+        top_k: int = 50,
+        top_p: float = 0.95,
+        do_sample: bool = True,
+        eos_token_id: Optional[int] = None,
+        **kwargs
+    ) -> Tensor:
+        """Custom generate method for simple autoregressive generation."""
+        self.eval()
+        
+        for _ in range(max_new_tokens):
+            with torch.no_grad():
+                outputs = self.forward(input_ids)
+                next_token_logits = outputs.logits[:, -1, :] / temperature
+            
+            if top_k > 0:
+                indices_to_remove = next_token_logits < torch.topk(next_token_logits, top_k)[0][..., -1, None]
+                next_token_logits[indices_to_remove] = float("-inf")
+            
+            if top_p < 1.0:
+                sorted_logits, sorted_indices = torch.sort(next_token_logits, descending=True)
+                cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
+                sorted_indices_to_remove = cumulative_probs > top_p
+                sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
+                sorted_indices_to_remove[..., 0] = 0
+                indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
+                next_token_logits[indices_to_remove] = float("-inf")
+            
+            if do_sample:
+                probs = F.softmax(next_token_logits, dim=-1)
+                next_token = torch.multinomial(probs, num_samples=1)
+            else:
+                next_token = torch.argmax(next_token_logits, dim=-1, keepdim=True)
+            
+            input_ids = torch.cat([input_ids, next_token], dim=-1)
+            
+            if eos_token_id is not None and (next_token == eos_token_id).all():
+                break
+        
+        return input_ids
+
+
+def create_model_from_config(config_dict: Optional[Dict[str, Any]] = None) -> GemmaForCausalLM:
+    """Factory function to create a model from a configuration dictionary."""
+    if config_dict is None:
+        config = GemmaConfig()
+    else:
+        config = GemmaConfig(**{k: v for k, v in config_dict.items() if hasattr(GemmaConfig, k) or k in ['hidden_size', 'num_layers', 'intermediate_size', 'num_attention_heads', 'num_key_value_heads', 'max_position_embeddings', 'vocab_size', 'rope_theta', 'rms_norm_eps', 'hidden_act', 'attention_dropout', 'hidden_dropout', 'tie_word_embeddings', 'initializer_range']})
+    
+    model = GemmaForCausalLM(config)
+    logger.info(f"Created model with {model.num_parameters():,} parameters")
+    return model

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:35cc7233b55b81823e54b1b752418618c88f7117dfca30896ab8dbfcaabc57c5
+size 362058272

requirements.txt

@@ -0,0 +1,9 @@
+torch
+transformers
+datasets
+safetensors
+accelerate
+modal
+pathlib
+gradio
+huggingface-hub

special_tokens_map.json

@@ -0,0 +1,6 @@
+{
+  "bos_token": "<|endoftext|>",
+  "eos_token": "<|endoftext|>",
+  "unk_token": "<|endoftext|>",
+  "pad_token": "<|endoftext|>"
+}

tokenizer_config.json

@@ -0,0 +1,12 @@
+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": "<|endoftext|>",
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "is_local": false,
+  "model_max_length": 1024,
+  "pad_token": null,
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "<|endoftext|>"
+}