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@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+teaser.png filter=lfs diff=lfs merge=lfs -text

.hfd/last_download_command

@@ -0,0 +1 @@
+REPO_ID=microsoft/LLM2CLIP-Llama-3-8B-Instruct-CC-Finetuned TOOL=aria2c INCLUDE_PATTERNS= EXCLUDE_PATTERNS= DATASET=0 HF_USERNAME= HF_TOKEN= HF_ENDPOINT=https://hf-mirror.com REVISION=main

.hfd/repo_metadata.json

@@ -0,0 +1 @@
+{"_id":"6738ab4f7250a807ef3d09d0","id":"microsoft/LLM2CLIP-Llama-3-8B-Instruct-CC-Finetuned","private":false,"pipeline_tag":"zero-shot-classification","tags":["safetensors","llama","CLIP","LLM2CLIP","zero-shot-classification","custom_code","arxiv:2411.04997","license:apache-2.0","region:us"],"downloads":9501,"likes":36,"modelId":"microsoft/LLM2CLIP-Llama-3-8B-Instruct-CC-Finetuned","author":"microsoft","sha":"97f7d164a1eb89a79e344b21c7a1be7eee265d2e","lastModified":"2024-11-19T16:27:34.000Z","gated":false,"disabled":false,"widgetData":[{"text":"I have a problem with my iphone that needs to be resolved asap!","candidate_labels":"urgent, not urgent, phone, tablet, computer","multi_class":true},{"text":"Last week I upgraded my iOS version and ever since then my phone has been overheating whenever I use your app.","candidate_labels":"mobile, website, billing, account access","multi_class":false},{"text":"A new model offers an explanation for how the Galilean satellites formed around the solar system’s largest world. Konstantin Batygin did not set out to solve one of the solar system’s most puzzling mysteries when he went for a run up a hill in Nice, France. Dr. Batygin, a Caltech researcher, best known for his contributions to the search for the solar system’s missing “Planet Nine,” spotted a beer bottle. At a steep, 20 degree grade, he wondered why it wasn’t rolling down the hill. He realized there was a breeze at his back holding the bottle in place. Then he had a thought that would only pop into the mind of a theoretical astrophysicist: “Oh! This is how Europa formed.” Europa is one of Jupiter’s four large Galilean moons. And in a paper published Monday in the Astrophysical Journal, Dr. Batygin and a co-author, Alessandro Morbidelli, a planetary scientist at the Côte d’Azur Observatory in France, present a theory explaining how some moons form around gas giants like Jupiter and Saturn, suggesting that millimeter-sized grains of hail produced during the solar system’s formation became trapped around these massive worlds, taking shape one at a time into the potentially habitable moons we know today.","candidate_labels":"space & cosmos, scientific discovery, microbiology, robots, archeology","multi_class":true}],"model-index":null,"config":{"architectures":["LlamaEncoderModel"],"auto_map":{"AutoModel":"McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp--modeling_llama_encoder.LlamaEncoderModel"},"model_type":"llama","tokenizer_config":{"bos_token":"<|begin_of_text|>","chat_template":"{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% endif %}","eos_token":"<|end_of_text|>","pad_token":"<|end_of_text|>"}},"cardData":{"license":"apache-2.0","tags":["CLIP","LLM2CLIP"],"pipeline_tag":"zero-shot-classification"},"siblings":[{"rfilename":".gitattributes"},{"rfilename":"README.md"},{"rfilename":"config.json"},{"rfilename":"model-00001-of-00004.safetensors"},{"rfilename":"model-00002-of-00004.safetensors"},{"rfilename":"model-00003-of-00004.safetensors"},{"rfilename":"model-00004-of-00004.safetensors"},{"rfilename":"model.safetensors.index.json"},{"rfilename":"modeling_llama_encoder.py"},{"rfilename":"special_tokens_map.json"},{"rfilename":"teaser.png"},{"rfilename":"tokenizer.json"},{"rfilename":"tokenizer_config.json"}],"spaces":["BronioInt/Lake-1-Pro"],"createdAt":"2024-11-16T14:25:19.000Z","safetensors":{"parameters":{"BF16":7504924672},"total":7504924672},"usedStorage":15009881368}

README.md

@@ -1,3 +1,131 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+tags:
+- CLIP
+- LLM2CLIP
+pipeline_tag: zero-shot-classification
+---
+
+# Modification Notes
+
+This is a modified version of the original Microsoft LLM2CLIP-Llama-3-8B-Instruct-CC-Finetuned model. The main modifications include:
+
+1. **Configuration Updates**: Updated `config.json` to load model from local repo rather than `McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp` 
+2. **Model Code Adaptation**: Modified `modeling_llama_encoder.py` and added `attn_mask_utils.py` to work with newer versions of transformers
+3. **Model Weights**: The model weight files remain unchanged from the original
+
+Original model: https://huggingface.co/microsoft/LLM2CLIP-Llama-3-8B-Instruct-CC-Finetuned
+
+---
+
+<div align="center">
+
+<h2><a href="">LLM2CLIP: Extending the Capability Boundaries of CLIP through Large Language Models</a></h2>
+Weiquan Huang<sup>1*</sup>, Aoqi Wu<sup>1*</sup>, Yifan Yang<sup>2†</sup>, Xufang Luo<sup>2</sup>, Yuqing Yang<sup>2</sup>, Liang Hu<sup>1</sup>, Qi Dai<sup>2</sup>, Xiyang Dai<sup>2</sup>, Dongdong Chen<sup>2</sup>, Chong Luo<sup>2</sup>, Lili Qiu<sup>2</sup>
+
+<sup>1</sup>Tongji Universiy, <sup>2</sup>Microsoft Corporation <br><sup>*</sup>Equal contribution <br><sup>†</sup> Corresponding to: [email protected]
+
+<p><a rel="nofollow" href="https://github.com/microsoft/LLM2CLIP">[📂 GitHub]</a>  <a rel="nofollow" href="https://microsoft.github.io/LLM2CLIP/">[🆕 Blog]</a>  <a rel="nofollow" href="">[📜 LLM2CLIP]</a> 
+</div>
+
+
+In this paper, we propose LLM2CLIP, a novel approach that embraces the power of LLMs to unlock CLIP’s potential. By fine-tuning the LLM in the caption space with contrastive learning, we extract its textual capabilities into the output embeddings, significantly improving the output layer’s textual discriminability. We then design an efficient training process where the fine-tuned LLM acts as a powerful teacher for CLIP’s visual encoder. Thanks to the LLM’s presence, we can now incorporate longer and more complex captions without being restricted by vanilla CLIP text encoder’s context window and ability limitations. Our experiments demonstrate that this approach brings substantial improvements in cross-modal tasks. Our method directly boosted the performance of the previously SOTA EVA02 model by 16.5% on both long-text and short-text retrieval tasks, transforming a CLIP model trained solely on English data into a state-of-the-art cross-lingual model. Moreover, when integrated into mul- timodal training with models like Llava 1.5, it consistently outperformed CLIP across nearly all benchmarks, demonstrating comprehensive performance improvements.
+
+## LLM2CLIP performance
+
+<div align="center">
+  <img src="teaser.png" alt="summary_tab" width="85%">
+</div>
+**It's important to note that all results presented in the paper are evaluated using PyTorch weights. There may be differences in performance when using Hugging Face (hf) models.**
+
+## Model Details
+- **Model Type:** vision foundation model, feature backbone
+- **Pretrain Dataset:** CC3M, CC12M, YFCC15M and Recap-DataComp-1B(30M subset)
+
+
+## Usage
+
+### Huggingface Version
+Image Embeddings
+```python
+from PIL import Image
+from transformers import AutoModel
+from transformers import CLIPImageProcessor
+import torch
+
+image_path = "CLIP.png"
+model_name_or_path = "LLM2CLIP-Openai-L-14-336" # or /path/to/local/LLM2CLIP-Openai-L-14-336
+
+processor = CLIPImageProcessor.from_pretrained("openai/clip-vit-large-patch14-336")
+model = AutoModel.from_pretrained(
+    model_name_or_path, 
+    torch_dtype=torch.float16,
+    trust_remote_code=True).to('cuda').eval()
+
+image = Image.open(image_path)
+input_pixels = processor(images=image, return_tensors="pt").pixel_values.to('cuda')
+
+with torch.no_grad(), torch.cuda.amp.autocast():
+    outputs = model.get_image_features(input_pixels)
+```
+Retrieval
+```python
+from PIL import Image
+from transformers import AutoModel, AutoConfig, AutoTokenizer
+from transformers import CLIPImageProcessor
+import torch
+from llm2vec import LLM2Vec
+import os
+
+os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+
+processor = CLIPImageProcessor.from_pretrained("openai/clip-vit-large-patch14-336")
+model_name_or_path = "microsoft/LLM2CLIP-Openai-L-14-336" # or /path/to/local/LLM2CLIP-Openai-L-14-336
+model = AutoModel.from_pretrained(
+    model_name_or_path, 
+    torch_dtype=torch.bfloat16,
+    trust_remote_code=True).to('cuda').eval()
+
+llm_model_name = 'microsoft/LLM2CLIP-Llama-3-8B-Instruct-CC-Finetuned'
+config = AutoConfig.from_pretrained(
+    llm_model_name, trust_remote_code=True
+)
+llm_model = AutoModel.from_pretrained(llm_model_name, torch_dtype=torch.bfloat16, config=config, trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained(llm_model_name)
+llm_model.config._name_or_path = 'meta-llama/Meta-Llama-3-8B-Instruct' #  Workaround for LLM2VEC
+l2v = LLM2Vec(llm_model, tokenizer, pooling_mode="mean", max_length=512, doc_max_length=512)
+
+captions = ["a diagram", "a dog", "a cat"]
+image_path = "CLIP.png"
+
+image = Image.open(image_path)
+input_pixels = processor(images=image, return_tensors="pt").pixel_values.to('cuda')
+text_features = l2v.encode(captions, convert_to_tensor=True).to('cuda')
+
+with torch.no_grad(), torch.cuda.amp.autocast():
+    image_features = model.get_image_features(input_pixels)
+    text_features = model.get_text_features(text_features)
+
+    image_features /= image_features.norm(dim=-1, keepdim=True)
+    text_features /= text_features.norm(dim=-1, keepdim=True)
+
+    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)
+
+print("Label probs:", text_probs)
+
+```
+
+## BibTeX & Citation
+
+```
+@misc{huang2024llm2clippowerfullanguagemodel,
+      title={LLM2CLIP: Powerful Language Model Unlock Richer Visual Representation}, 
+      author={Weiquan Huang and Aoqi Wu and Yifan Yang and Xufang Luo and Yuqing Yang and Liang Hu and Qi Dai and Xiyang Dai and Dongdong Chen and Chong Luo and Lili Qiu},
+      year={2024},
+      eprint={2411.04997},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2411.04997}, 
+}
+
+```

attn_mask_utils.py

@@ -0,0 +1,226 @@
+from typing import List, Optional, Tuple, Union
+import torch
+from packaging import version
+import importlib.metadata
+from transformers.modeling_attn_mask_utils import AttentionMaskConverter
+
+from transformers.utils.import_utils import _is_package_available
+
+def is_transformers_attn_greater_or_equal_4_39():
+    if not _is_package_available("transformers"):
+        return False
+
+    return version.parse(importlib.metadata.version("transformers")) >= version.parse(
+        "4.39.0"
+    )
+
+def _prepare_4d_attention_mask_for_sdpa(
+    attention_mask: Optional[torch.Tensor],
+    input_shape: Union[torch.Size, Tuple, List],
+    inputs_embeds: torch.Tensor,
+    past_key_values_length: int,
+    sliding_window: Optional[int] = None,
+):
+    attn_mask_converter = AttentionMaskConverter(is_causal=False, sliding_window=sliding_window)
+
+    key_value_length = input_shape[-1] + past_key_values_length
+    batch_size, query_length = input_shape
+
+    # torch.jit.trace and torchdynamo with fullgraph=True are unable to capture the controlflow `is_causal=attention_mask is None and q_len > 1`
+    # used as an SDPA argument. We keep compatibility with these tracing tools by always using SDPA's `attn_mask` argument in case we are tracing.
+    # TODO: Fix this as well when using torchdynamo with fullgraph=True.
+    is_tracing = torch.jit.is_tracing()
+
+    if attention_mask is not None:
+        if torch.all(attention_mask == 1):
+            if is_tracing:
+                pass
+            elif query_length == 1:
+                # For query_length == 1, causal attention and bi-directional attention are the same.
+                attention_mask = None
+            # Commented out to deal with batch size=1 cases
+            # elif key_value_length == query_length:
+            #     attention_mask = None
+            else:
+                # Unfortunately, for query_length > 1 and key_value_length != query_length, we cannot generally ignore the attention mask, as SDPA causal mask generation
+                # may be wrong. We will set `is_causal=False` in SDPA and rely on Transformers attention_mask instead, hence not setting it to None here.
+                # Reference: https://github.com/pytorch/pytorch/issues/108108
+                pass
+    elif query_length > 1 and key_value_length != query_length:
+        # See the comment above (https://github.com/pytorch/pytorch/issues/108108).
+        # Ugly: we set it to True here to dispatch in the following controlflow to `to_causal_4d`.
+        attention_mask = True
+    elif is_tracing:
+        raise ValueError(
+            'Attention using SDPA can not be traced with torch.jit.trace when no attention_mask is provided. To solve this issue, please either load your model with the argument `attn_implementation="eager"` or pass an attention_mask input when tracing the model.'
+        )
+
+    if attention_mask is None:
+        expanded_4d_mask = None
+    elif attention_mask is True:
+        expanded_4d_mask = attn_mask_converter.to_causal_4d(
+            input_shape[0], input_shape[-1], key_value_length, dtype=inputs_embeds.dtype, device=inputs_embeds.device
+        )
+    else:
+        expanded_4d_mask = attn_mask_converter.to_4d(
+            attention_mask,
+            input_shape[-1],
+            dtype=inputs_embeds.dtype,
+            key_value_length=key_value_length,
+        )
+
+        # From PyTorch 2.1 onwards, F.scaled_dot_product_attention with the memory-efficient attention backend
+        # produces nans if sequences are completely unattended in the attention mask. Details: https://github.com/pytorch/pytorch/issues/110213
+        if query_length > 1:
+            if is_transformers_attn_greater_or_equal_4_39():
+                expanded_4d_mask = AttentionMaskConverter._unmask_unattended(
+                    expanded_4d_mask, min_dtype=torch.finfo(inputs_embeds.dtype).min
+                )
+            else:
+                expanded_4d_mask = AttentionMaskConverter._unmask_unattended(
+                    expanded_4d_mask, attention_mask, unmasked_value=0.0
+                )
+
+    return expanded_4d_mask
+
+
+def _prepare_4d_attention_mask(
+    attention_mask: Optional[torch.Tensor],
+    input_shape: Union[torch.Size, Tuple, List],
+    inputs_embeds: torch.Tensor,
+    past_key_values_length: int,
+    sliding_window: Optional[int] = None,
+):
+    attn_mask_converter = AttentionMaskConverter(is_causal=False, sliding_window=sliding_window)
+
+    key_value_length = input_shape[-1] + past_key_values_length
+
+    # 4d mask is passed through the layers
+    if attention_mask is not None:
+        attention_mask = attn_mask_converter.to_4d(
+            attention_mask, input_shape[-1], key_value_length=key_value_length, dtype=inputs_embeds.dtype
+        )
+    else:
+        attention_mask = attn_mask_converter.to_causal_4d(
+            input_shape[0], input_shape[-1], key_value_length, dtype=inputs_embeds.dtype, device=inputs_embeds.device
+        )
+
+    return attention_mask
+
+
+def _prepare_4d_causal_attention_mask(
+    attention_mask: Optional[torch.Tensor],
+    input_shape: Union[torch.Size, Tuple, List],
+    inputs_embeds: torch.Tensor,
+    past_key_values_length: int,
+    sliding_window: Optional[int] = None,
+):
+    attn_mask_converter = AttentionMaskConverter(is_causal=False, sliding_window=sliding_window)
+
+    key_value_length = input_shape[-1] + past_key_values_length
+
+    # 4d mask is passed through the layers
+    if attention_mask is not None:
+        attention_mask = attn_mask_converter.to_4d(
+            attention_mask, input_shape[-1], key_value_length=key_value_length, dtype=inputs_embeds.dtype
+        )
+    else:
+        attention_mask = attn_mask_converter.to_causal_4d(
+            input_shape[0], input_shape[-1], key_value_length, dtype=inputs_embeds.dtype, device=inputs_embeds.device
+        )
+
+    return attention_mask
+
+
+def _prepare_4d_causal_attention_mask_for_sdpa(
+    attention_mask: Optional[torch.Tensor],
+    input_shape: Union[torch.Size, Tuple, List],
+    inputs_embeds: torch.Tensor,
+    past_key_values_length: int,
+    sliding_window: Optional[int] = None,
+):
+    """
+    Prepares the correct `attn_mask` argument to be used by `torch.nn.functional.scaled_dot_product_attention`.
+
+    In case no token is masked in the `attention_mask` argument, we simply set it to `None` for the cases `query_length == 1` and
+    `key_value_length == query_length`, and rely instead on SDPA `is_causal` argument to use causal/non-causal masks,
+    allowing to dispatch to the flash attention kernel (that can otherwise not be used if a custom `attn_mask` is passed).
+    """
+    attn_mask_converter = AttentionMaskConverter(is_causal=False, sliding_window=sliding_window)
+
+    key_value_length = input_shape[-1] + past_key_values_length
+    batch_size, query_length = input_shape
+
+    # torch.jit.trace, symbolic_trace and torchdynamo with fullgraph=True are unable to capture the controlflow `is_causal=attention_mask is None and q_len > 1`
+    # used as an SDPA argument. We keep compatibility with these tracing tools by always using SDPA's `attn_mask` argument in case we are tracing.
+    # TODO: Fix this as well when using torchdynamo with fullgraph=True.
+    is_tracing = torch.jit.is_tracing() or isinstance(inputs_embeds, torch.fx.Proxy)
+
+    if attention_mask is not None:
+        # 4d mask is passed through
+        if len(attention_mask.shape) == 4:
+            expected_shape = (input_shape[0], 1, input_shape[1], key_value_length)
+            if tuple(attention_mask.shape) != expected_shape:
+                raise ValueError(
+                    f"Incorrect 4D attention_mask shape: {tuple(attention_mask.shape)}; expected: {expected_shape}."
+                )
+            else:
+                # if the 4D mask has correct shape - invert it and fill with negative infinity
+                inverted_mask = 1.0 - attention_mask.to(inputs_embeds.dtype)
+                attention_mask = inverted_mask.masked_fill(
+                    inverted_mask.to(torch.bool), torch.finfo(inputs_embeds.dtype).min
+                )
+                return attention_mask
+
+        elif not is_tracing and torch.all(attention_mask == 1):
+            if query_length == 1:
+                # For query_length == 1, causal attention and bi-directional attention are the same.
+                attention_mask = None
+            # Commented out to deal with batch size=1 cases
+            # elif key_value_length == query_length:
+            #     attention_mask = None
+            else:
+                # Unfortunately, for query_length > 1 and key_value_length != query_length, we cannot generally ignore the attention mask, as SDPA causal mask generation
+                # may be wrong. We will set `is_causal=False` in SDPA and rely on Transformers attention_mask instead, hence not setting it to None here.
+                # Reference: https://github.com/pytorch/pytorch/issues/108108
+                pass
+    elif query_length > 1 and key_value_length != query_length:
+        # See the comment above (https://github.com/pytorch/pytorch/issues/108108).
+        # Ugly: we set it to True here to dispatch in the following controlflow to `to_causal_4d`.
+        attention_mask = True
+    elif is_tracing:
+        raise ValueError(
+            'Attention using SDPA can not be traced with torch.jit.trace when no attention_mask is provided. To solve this issue, please either load your model with the argument `attn_implementation="eager"` or pass an attention_mask input when tracing the model.'
+        )
+
+    if attention_mask is None:
+        expanded_4d_mask = None
+    elif attention_mask is True:
+        expanded_4d_mask = attn_mask_converter.to_causal_4d(
+            input_shape[0], input_shape[-1], key_value_length, dtype=inputs_embeds.dtype, device=inputs_embeds.device
+        )
+    else:
+        expanded_4d_mask = attn_mask_converter.to_4d(
+            attention_mask,
+            input_shape[-1],
+            dtype=inputs_embeds.dtype,
+            key_value_length=key_value_length,
+        )
+
+        # From PyTorch 2.1 onwards, F.scaled_dot_product_attention with the memory-efficient attention backend
+        # produces nans if sequences are completely unattended in the attention mask. Details: https://github.com/pytorch/pytorch/issues/110213
+        #
+        # This fix is not applied in case we are tracing with torch.jit.trace or symbolic_trace, as _unmask_unattended has a data-dependent
+        # controlflow that can not be captured properly.
+        # TODO: _unmask_unattended does not work either with torch.compile when using fullgraph=True. We should find a way to detect this case.
+        if query_length > 1 and not is_tracing:
+            if is_transformers_attn_greater_or_equal_4_39():
+                expanded_4d_mask = AttentionMaskConverter._unmask_unattended(
+                    expanded_4d_mask, min_dtype=torch.finfo(inputs_embeds.dtype).min
+                )
+            else:
+                expanded_4d_mask = AttentionMaskConverter._unmask_unattended(
+                    expanded_4d_mask, attention_mask, unmasked_value=0.0
+                )
+
+    return expanded_4d_mask

config.json

@@ -0,0 +1,31 @@
+{
+  "_name_or_path": "meta-llama/Meta-Llama-3-8B-Instruct",
+  "architectures": [
+    "LlamaEncoderModel"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0,
+  "auto_map": {
+    "AutoModel": "modeling_llama_encoder.LlamaEncoderModel"
+  },
+  "bos_token_id": 128000,
+  "eos_token_id": 128001,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 8192,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 500000,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.40.2",
+  "use_cache": true,
+  "vocab_size": 128256
+}

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+}

modeling_llama_encoder.py

@@ -0,0 +1,177 @@
+from typing import List, Optional, Tuple, Union
+import torch
+from transformers import LlamaModel, LlamaPreTrainedModel
+from transformers.models.llama.modeling_llama import LlamaDecoderLayer, LlamaRMSNorm, LlamaConfig, LlamaMLP, LlamaAttention, LlamaRotaryEmbedding
+from transformers.utils import logging
+from torch import nn
+import torch.nn.functional as F
+from transformers.modeling_outputs import BaseModelOutputWithPast
+from transformers.cache_utils import Cache, DynamicCache
+from .attn_mask_utils import _prepare_4d_attention_mask_for_sdpa, _prepare_4d_attention_mask
+
+logger = logging.get_logger(__name__)
+
+class ModifiedLlamaAttention(LlamaAttention):
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.is_causal = False
+
+
+class ModifiedLlamaDecoderLayer(LlamaDecoderLayer):
+    def __init__(self, config: LlamaConfig, layer_idx: int):
+        nn.Module.__init__(self)
+        self.hidden_size = config.hidden_size
+
+        self.self_attn = ModifiedLlamaAttention(config=config, layer_idx=layer_idx)
+
+        self.mlp = LlamaMLP(config)
+        self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+
+class LlamaEncoderModel(LlamaModel):
+    def __init__(self, config):
+        LlamaPreTrainedModel.__init__(self, 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(
+            [ModifiedLlamaDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self._use_sdpa = config._attn_implementation == "sdpa"
+        self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
+        self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.rotary_emb = LlamaRotaryEmbedding(config=config)
+
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = 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,
+    ) -> 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
+
+        # retrieve input_ids and inputs_embeds
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+        elif input_ids is not None:
+            batch_size, seq_length = input_ids.shape[:2]
+        elif inputs_embeds is not None:
+            batch_size, seq_length = inputs_embeds.shape[:2]
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning_once(
+                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                )
+                use_cache = False
+
+        past_key_values_length = 0
+        if use_cache:
+            use_legacy_cache = not isinstance(past_key_values, Cache)
+            if use_legacy_cache:
+                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+            past_key_values_length = past_key_values.get_usable_length(seq_length)
+
+        if position_ids is None:
+            device = input_ids.device if input_ids is not None else inputs_embeds.device
+            position_ids = torch.arange(
+                past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
+            )
+            position_ids = position_ids.unsqueeze(0)
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        if self._use_flash_attention_2:
+            # 2d mask is passed through the layers
+            attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
+        elif self._use_sdpa and not output_attentions:
+            # output_attentions=True can not be supported when using SDPA, and we fall back on
+            # the manual implementation that requires a 4D causal mask in all cases.
+            attention_mask = _prepare_4d_attention_mask_for_sdpa(
+                attention_mask,
+                (batch_size, seq_length),
+                inputs_embeds,
+                past_key_values_length,
+            )
+        else:
+            # 4d mask is passed through the layers
+            attention_mask = _prepare_4d_attention_mask(
+                attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
+            )
+
+        # embed positions
+        hidden_states = inputs_embeds
+
+        # create position embeddings to be shared across the decoder layers
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = None
+
+        for decoder_layer in self.layers:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    use_cache,
+                    position_embeddings=position_embeddings,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_values,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                    position_embeddings=position_embeddings,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=past_key_values if use_cache else None,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )

special_tokens_map.json

@@ -0,0 +1,17 @@
+{
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|end_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<|end_of_text|>"
+}

tokenizer_config.json

@@ -0,0 +1,2064 @@
+{
+  "added_tokens_decoder": {
+    "128000": {
+      "content": "<|begin_of_text|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128001": {
+      "content": "<|end_of_text|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128002": {
+      "content": "<|reserved_special_token_0|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128003": {
+      "content": "<|reserved_special_token_1|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128004": {
+      "content": "<|reserved_special_token_2|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128005": {
+      "content": "<|reserved_special_token_3|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128006": {
+      "content": "<|start_header_id|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128007": {
+      "content": "<|end_header_id|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128008": {
+      "content": "<|reserved_special_token_4|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128009": {
+      "content": "<|eot_id|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128010": {
+      "content": "<|reserved_special_token_5|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128011": {
+      "content": "<|reserved_special_token_6|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128012": {
+      "content": "<|reserved_special_token_7|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128013": {
+      "content": "<|reserved_special_token_8|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128014": {
+      "content": "<|reserved_special_token_9|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128015": {
+      "content": "<|reserved_special_token_10|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128016": {
+      "content": "<|reserved_special_token_11|>",
+      "lstrip": false,
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+      "single_word": false,
+      "special": true
+    },
+    "128017": {
+      "content": "<|reserved_special_token_12|>",
+      "lstrip": false,
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+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128018": {
+      "content": "<|reserved_special_token_13|>",
+      "lstrip": false,
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+      "rstrip": false,
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+      "special": true
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+    "128019": {
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+      "special": true
+    },
+    "128242": {
+      "content": "<|reserved_special_token_237|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128243": {
+      "content": "<|reserved_special_token_238|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128244": {
+      "content": "<|reserved_special_token_239|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128245": {
+      "content": "<|reserved_special_token_240|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128246": {
+      "content": "<|reserved_special_token_241|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128247": {
+      "content": "<|reserved_special_token_242|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128248": {
+      "content": "<|reserved_special_token_243|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128249": {
+      "content": "<|reserved_special_token_244|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128250": {
+      "content": "<|reserved_special_token_245|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128251": {
+      "content": "<|reserved_special_token_246|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128252": {
+      "content": "<|reserved_special_token_247|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128253": {
+      "content": "<|reserved_special_token_248|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128254": {
+      "content": "<|reserved_special_token_249|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128255": {
+      "content": "<|reserved_special_token_250|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<|begin_of_text|>",
+  "chat_template": "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% endif %}",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|end_of_text|>",
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<|end_of_text|>",
+  "padding_side": "left",
+  "tokenizer_class": "PreTrainedTokenizerFast"
+}