Initial model upload with self-contained custom code

modeling_qwen2.py

@@ -1,517 +1,130 @@
-# Copyright 2024 The Qwen team, Alibaba Group and The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# This is a cleaned version of the model script for public release.
-# It imports the MDMGenerationMixin from the accompanying generation_utils.py file.
-
-import logging
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union
-
-import torch
-from torch import nn
-from transformers.activations import ACT2FN
-from transformers.cache_utils import Cache, DynamicCache, SlidingWindowCache, StaticCache
-from transformers.modeling_attn_mask_utils import AttentionMaskConverter
-from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
-from transformers.modeling_outputs import (
-    BaseModelOutputWithPast,
-    CausalLMOutputWithPast,
-)
-from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS
-from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
-from transformers.models.qwen2.configuration_qwen2 import Qwen2Config
-from transformers.processing_utils import Unpack
-from transformers.utils import (
-    add_start_docstrings,
-    add_start_docstrings_to_model_forward,
-    replace_return_docstrings,
-)
-
-# Import the custom generation mixin from the local file in the repo
-from .generation_utils import MDMGenerationMixin
-
-logger = logging.getLogger(__name__)
-
-_CHECKPOINT_FOR_DOC = "meta-qwen2/Qwen2-2-7b-hf"
-_CONFIG_FOR_DOC = "Qwen2Config"
-
-
-class Qwen2MLP(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.config = config
-        self.hidden_size = config.hidden_size
-        self.intermediate_size = config.intermediate_size
-        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
-        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
-        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
-        self.act_fn = ACT2FN[config.hidden_act]
-
-    def forward(self, x):
-        down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
-        return down_proj
-
-
-def rotate_half(x):
-    """Rotates half the hidden dims of the input."""
-    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, k, cos, sin, position_ids=None, unsqueeze_dim=1):
-    cos = cos.unsqueeze(unsqueeze_dim)
-    sin = sin.unsqueeze(unsqueeze_dim)
-    q_embed = (q * cos) + (rotate_half(q) * sin)
-    k_embed = (k * cos) + (rotate_half(k) * sin)
-    return q_embed, k_embed
-
-
-def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
-    """
-    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
-    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
-    """
-    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
-    if n_rep == 1:
-        return hidden_states
-    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
-    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
-
-
-class Qwen2Attention(nn.Module):
-    # ... (rest of the class is unchanged)
-    def __init__(self, config: Qwen2Config, layer_idx: int):
-        super().__init__()
-        self.config = config
-        self.layer_idx = layer_idx
-        self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
-        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
-        self.scaling = self.head_dim**-0.5
-        self.attention_dropout = config.attention_dropout
-        self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=True)
-        self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=True)
-        self.v_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=True)
-        self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=False)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        position_embeddings: Tuple[torch.Tensor, torch.Tensor],
-        attention_mask: Optional[torch.Tensor],
-        past_key_value: Optional[Cache] = None,
-        output_attentions: Optional[bool] = False,
-        cache_position: Optional[torch.LongTensor] = None,
-        is_causal: bool = True,
-        **kwargs: Unpack[FlashAttentionKwargs],
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        bsz, q_len, _ = hidden_states.size()
-        hidden_shape = (bsz, q_len, -1, self.head_dim)
-
-        query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
-        key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
-        value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
-
-        full_q_len = query_states.size(2)
-        cos, sin = position_embeddings
-        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
-
-        if past_key_value is not None:
-            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
-            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
-
-        attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get(self.config._attn_implementation, None)
-        if attention_interface is None:
-             raise ValueError(f"Attention implementation {self.config._attn_implementation} not found.")
-
-        if self.config._attn_implementation == "sdpa" and output_attentions:
-            logger.warning_once("Using SDPA with `output_attentions=True` requires eager attention.")
-            attention_interface = ALL_ATTENTION_FUNCTIONS["eager"]
-
-
-        attn_output, attn_weights = attention_interface(
-            query_states,
-            key_states,
-            value_states,
-            attention_mask=attention_mask,
-            dropout=self.attention_dropout if self.training else 0.0,
-            is_causal=is_causal,
-            **kwargs,
-        )
-        attn_output = attn_output.transpose(1, 2).contiguous()
-        attn_output = attn_output.reshape(bsz, q_len, self.config.hidden_size)
-        attn_output = self.o_proj(attn_output)
-        
-        if not output_attentions:
-            attn_weights = None
-
-        return attn_output, attn_weights, past_key_value
-
-# ... (Qwen2RMSNorm, Qwen2DecoderLayer, Qwen2RotaryEmbedding, Qwen2PreTrainedModel, Qwen2Model are unchanged)
-class Qwen2RMSNorm(nn.Module):
-    def __init__(self, hidden_size, eps=1e-6):
-        super().__init__()
-        self.weight = nn.Parameter(torch.ones(hidden_size))
-        self.variance_epsilon = eps
-
-    def forward(self, hidden_states):
-        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 Qwen2DecoderLayer(nn.Module):
-    def __init__(self, config: Qwen2Config, layer_idx: int):
-        super().__init__()
-        self.hidden_size = config.hidden_size
-        self.self_attn = Qwen2Attention(config=config, layer_idx=layer_idx)
-        self.mlp = Qwen2MLP(config)
-        self.input_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.post_attention_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Cache] = None,
-        output_attentions: Optional[bool] = False,
-        use_cache: Optional[bool] = False,
-        cache_position: Optional[torch.LongTensor] = None,
-        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
-        is_causal: bool = True,
-        **kwargs: Unpack[FlashAttentionKwargs],
-    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
-        residual = hidden_states
-        hidden_states = self.input_layernorm(hidden_states)
-
-        hidden_states, self_attn_weights, present_key_value = self.self_attn(
-            hidden_states=hidden_states,
-            attention_mask=attention_mask,
-            past_key_value=past_key_value,
-            output_attentions=output_attentions,
-            cache_position=cache_position,
-            position_embeddings=position_embeddings,
-            is_causal=is_causal,
-            **kwargs,
-        )
-        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
-
-        outputs = (hidden_states,)
-        if output_attentions:
-            outputs += (self_attn_weights,)
-        if use_cache:
-            outputs += (present_key_value,)
-
-        return outputs
-
-class Qwen2RotaryEmbedding(nn.Module):
-    def __init__(self, config: Qwen2Config, device=None):
-        super().__init__()
-        if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
-            self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
-        else:
-            self.rope_type = "default"
-        self.max_seq_len_cached = config.max_position_embeddings
-        self.original_max_seq_len = config.max_position_embeddings
-        self.config = config
-        self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
-        inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-        self.original_inv_freq = self.inv_freq
-
-    def _dynamic_frequency_update(self, position_ids, device):
-        seq_len = torch.max(position_ids) + 1
-        if seq_len > self.max_seq_len_cached:
-            inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device, seq_len=seq_len)
-            self.register_buffer("inv_freq", inv_freq, persistent=False)
-            self.max_seq_len_cached = seq_len
-        if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len:
-            self.original_inv_freq = self.original_inv_freq.to(device)
-            self.register_buffer("inv_freq", self.original_inv_freq, persistent=False)
-            self.max_seq_len_cached = self.original_max_seq_len
-
-    @torch.no_grad()
-    def forward(self, x, position_ids):
-        if "dynamic" in self.rope_type:
-            self._dynamic_frequency_update(position_ids, device=x.device)
-        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
-        position_ids_expanded = position_ids[:, None, :].float()
-        device_type = x.device.type
-        device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
-        with torch.autocast(device_type=device_type, enabled=False):
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
-            emb = torch.cat((freqs, freqs), dim=-1)
-            cos = emb.cos()
-            sin = emb.sin()
-        cos = cos * self.attention_scaling
-        sin = sin * self.attention_scaling
-        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
-
-@add_start_docstrings(
-    "The bare Qwen2 Model outputting raw hidden-states without any specific head on top.",
-    QWEN2_START_DOCSTRING,
-)
-class Qwen2PreTrainedModel(PreTrainedModel):
-    config_class = Qwen2Config
-    base_model_prefix = "model"
-    supports_gradient_checkpointing = True
-    _no_split_modules = ["Qwen2DecoderLayer"]
-    _skip_keys_device_placement = ["past_key_values"]
-    _supports_flash_attn_2 = True
-    _supports_sdpa = True
-    _supports_cache_class = True
-
-    def _init_weights(self, 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)
-            if module.padding_idx is not None:
-                module.weight.data[module.padding_idx].zero_()
-
-class Qwen2Model(Qwen2PreTrainedModel):
-    def __init__(self, config: Qwen2Config):
-        super().__init__(config)
-        self.padding_idx = config.pad_token_id
-        self.vocab_size = config.vocab_size
-        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
-        self.layers = nn.ModuleList(
-            [Qwen2DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
-        )
-        self.norm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.rotary_emb = Qwen2RotaryEmbedding(config=config)
-        self.gradient_checkpointing = False
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.embed_tokens = value
-
-    def forward(
-        self,
-        input_ids: torch.LongTensor = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Cache] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-        is_causal: bool = True,
-        **flash_attn_kwargs: Unpack[FlashAttentionKwargs],
-    ) -> Union[Tuple, BaseModelOutputWithPast]:
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        use_cache = use_cache if use_cache is not None else self.config.use_cache
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-        if self.gradient_checkpointing and self.training and use_cache:
-            logger.warning_once("`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.")
-            use_cache = False
-        if inputs_embeds is None:
-            inputs_embeds = self.embed_tokens(input_ids)
-
-        past_key_values_length = 0
-        if use_cache:
-            if past_key_values is None:
-                past_key_values = DynamicCache()
-            past_key_values_length = past_key_values.get_seq_length()
-
-        if cache_position is None:
-            cache_position = torch.arange(
-                past_key_values_length, past_key_values_length + inputs_embeds.shape[1], device=inputs_embeds.device
-            )
-        if position_ids is None:
-            position_ids = cache_position.unsqueeze(0)
-
-        causal_mask = self._update_causal_mask(attention_mask, inputs_embeds, cache_position, is_causal)
-        hidden_states = inputs_embeds
-        position_embeddings = self.rotary_emb(hidden_states, position_ids)
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attns = () if output_attentions else None
-        next_decoder_cache = () if use_cache else None
-
-        for decoder_layer in self.layers:
-            if output_hidden_states:
-                all_hidden_states += (hidden_states,)
-            
-            layer_outputs = decoder_layer(
-                hidden_states,
-                attention_mask=causal_mask,
-                position_ids=position_ids,
-                past_key_value=past_key_values,
-                output_attentions=output_attentions,
-                use_cache=use_cache,
-                cache_position=cache_position,
-                position_embeddings=position_embeddings,
-                is_causal=is_causal,
-                **flash_attn_kwargs,
-            )
-            hidden_states = layer_outputs[0]
-            if use_cache:
-                next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
-            if output_attentions:
-                all_self_attns += (layer_outputs[1],)
-
-        hidden_states = self.norm(hidden_states)
-        if output_hidden_states:
-            all_hidden_states += (hidden_states,)
+import argparse
+import json
+import os
+import shutil
+import sys
+from pathlib import Path
+
+from huggingface_hub import create_repo, upload_folder
+
+def main():
+    """Main function to handle model preparation and upload."""
+    parser = argparse.ArgumentParser(
+        description="Upload a custom Hugging Face model with its self-contained code."
+    )
+    parser.add_argument(
+        "--model_code_path",
+        type=str,
+        required=True,
+        help="Path to the self-contained, single Python model file.",
+    )
+    parser.add_argument(
+        "--ckpt_dir",
+        type=str,
+        required=True,
+        help="Directory containing the model weights and tokenizer files (hf_ckpt).",
+    )
+    parser.add_argument(
+        "--repo",
+        type=str,
+        required=True,
+        help="Name of the repository on Hugging Face Hub (e.g., 'username/repo-name').",
+    )
+    parser.add_argument(
+        "--readme_path",
+        type=str,
+        required=True,
+        help="Path to the README.md file to be included in the repository.",
+    )
+    parser.add_argument(
+        "--private",
+        action="store_true",
+        help="If set, creates a private repository.",
+    )
+
+    args = parser.parse_args()
+
+    staging_dir = Path("./temp_upload_staging")
+    if staging_dir.exists():
+        shutil.rmtree(staging_dir)
+    staging_dir.mkdir()
+    print(f"Created temporary staging directory: {staging_dir}")
+
+    try:
+        # --- 2. Copy All Necessary Files ---
+        print("\nCopying files to staging directory...")
         
-        next_cache = next_decoder_cache if use_cache else None
+        # Copy checkpoint files
+        for f in os.listdir(args.ckpt_dir):
+            shutil.copy(os.path.join(args.ckpt_dir, f), staging_dir)
         
-        if not return_dict:
-            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
-        return BaseModelOutputWithPast(
-            last_hidden_state=hidden_states,
-            past_key_values=next_cache,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attns,
-        )
-
-    def _update_causal_mask(self, attention_mask, input_tensor, cache_position, is_causal):
-        if not is_causal:
-            return attention_mask
+        # Copy the single, self-contained model code file
+        model_code_source = Path(args.model_code_path)
+        if not model_code_source.exists():
+            print(f"Error: Model code file not found at {model_code_source}")
+            sys.exit(1)
         
-        seq_len = input_tensor.shape[1]
-        if self.config._attn_implementation == "flash_attention_2":
-            if attention_mask is not None and 0.0 in attention_mask:
-                return attention_mask
-            return None
+        # The destination file MUST be named correctly for auto_map to work.
+        model_code_dest = staging_dir / "modeling_qwen2.py"
+        print(f"Copying model code from {model_code_source} to {model_code_dest}")
+        shutil.copy(model_code_source, model_code_dest)
         
-        dtype = input_tensor.dtype
-        device = input_tensor.device
+        print("File copying complete.")
+
+        # --- 3. Configure `config.json` for Auto-Loading ---
+        print("\nConfiguring config.json for auto-loading...")
+        config_path = staging_dir / "config.json"
+        if not config_path.exists():
+            print(f"Error: config.json not found in {args.ckpt_dir}")
+            sys.exit(1)
+
+        with open(config_path, "r", encoding="utf-8") as f:
+            config_data = json.load(f)
+
+        config_data["auto_map"] = {
+            "AutoModelForCausalLM": "modeling_qwen2.Qwen2ForCausalLM"
+        }
+        config_data["architectures"] = ["Qwen2ForCausalLM"]
+        config_data["trust_remote_code"] = True 
+
+        with open(config_path, "w", encoding="utf-8") as f:
+            json.dump(config_data, f, indent=2)
+        print("config.json updated successfully.")
+
+        # --- 4. Copy `README.md` ---
+        print("\nCopying README.md...")
+        readme_source = Path(args.readme_path)
+        if not readme_source.exists():
+            print(f"Error: README file not found at {readme_source}")
+            sys.exit(1)
         
-        causal_mask = torch.triu(torch.full((seq_len, seq_len), torch.finfo(dtype).min, device=device), 1)
-        causal_mask = causal_mask[None, None, :, :].expand(input_tensor.shape[0], 1, -1, -1)
+        with open(readme_source, "r", encoding="utf-8") as f:
+            readme_content = f.read()
         
-        if attention_mask is not None:
-             causal_mask = causal_mask.clone()
-             causal_mask = causal_mask + attention_mask[:, None, None, :]
-        
-        return causal_mask
-
-class Qwen2ForCausalLM(Qwen2PreTrainedModel, MDMGenerationMixin):
-    _tied_weights_keys = ["lm_head.weight"]
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.model = Qwen2Model(config)
-        self.vocab_size = config.vocab_size
-        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-        self.post_init()
-
-    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
+        readme_content = readme_content.replace("{repo_id}", args.repo)
 
-    def set_output_embeddings(self, new_embeddings):
-        self.lm_head = new_embeddings
-
-    def set_decoder(self, decoder):
-        self.model = decoder
-
-    def get_decoder(self):
-        return self.model
-
-    @add_start_docstrings_to_model_forward(QWEN2_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
-    def forward(
-        self,
-        input_ids: torch.LongTensor = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Cache] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-        is_causal: bool = True,
-        **kwargs: Unpack[FlashAttentionKwargs],
-    ) -> Union[Tuple, CausalLMOutputWithPast]:
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.model(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-            cache_position=cache_position,
-            is_causal=is_causal,
-            **kwargs,
-        )
-
-        hidden_states = outputs[0]
-        logits = self.lm_head(hidden_states)
-        logits = logits.float()
-        loss = None
-        
-        if labels is not None:
-            shift_logits = logits[..., :-1, :].contiguous()
-            shift_labels = labels[..., 1:].contiguous()
-            loss_fct = torch.nn.CrossEntropyLoss()
-            shift_logits = shift_logits.view(-1, self.config.vocab_size)
-            shift_labels = shift_labels.view(-1)
-            shift_labels = shift_labels.to(shift_logits.device)
-            loss = loss_fct(shift_logits, shift_labels)
+        with open(staging_dir / "README.md", "w", encoding="utf-8") as f:
+            f.write(readme_content)
+        print("README.md copied and processed.")
 
-        if not return_dict:
-            output = (logits,) + outputs[1:]
-            return (loss,) + output if loss is not None else output
+        # --- 5. Upload to the Hub ---
+        print(f"\nPreparing to upload to repository: {args.repo}")
+        repo_url = create_repo(args.repo, repo_type="model", exist_ok=True, private=args.private)
 
-        return CausalLMOutputWithPast(
-            loss=loss,
-            logits=logits,
-            past_key_values=outputs.past_key_values,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
+        upload_folder(
+            folder_path=staging_dir,
+            repo_id=args.repo,
+            repo_type="model",
+            commit_message="Initial model upload with self-contained custom code",
         )
+        print("\n🚀 Upload complete! 🚀")
+        print(f"Check out your model at: {repo_url}")
 
-ModelClass = Qwen2ForCausalLM
+    finally:
+        # --- 6. Clean Up ---
+        print("\nCleaning up temporary staging directory...")
+        shutil.rmtree(staging_dir)
+        print("Cleanup complete.")
 
-__all__ = ["Qwen2ForCausalLM", "Qwen2Model", "Qwen2PreTrainedModel"]
+if __name__ == "__main__":
+    main()