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llama-embed-nemotron-8b

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#3
by ybabakhin - opened
base: refs/heads/main
←
from: refs/pr/3
Discussion Files changed
+14
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Files changed (6) hide show
  1. .ipynb_checkpoints/config-checkpoint.json +0 -40
  2. .ipynb_checkpoints/llama_bidirectional_model-checkpoint.py +0 -54
  3. .ipynb_checkpoints/special_tokens_map-checkpoint.json +0 -17
  4. .ipynb_checkpoints/tokenizer_config-checkpoint.json +0 -2063
  5. LICENSE +13 -13
  6. README.md +1 -1
.ipynb_checkpoints/config-checkpoint.json DELETED
@@ -1,40 +0,0 @@
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- {
2
- "architectures": [
3
- "LlamaBidirectionalModel"
4
- ],
5
- "attention_bias": false,
6
- "attention_dropout": 0.0,
7
- "auto_map": {
8
- "AutoConfig": "llama_bidirectional_model.LlamaBidirectionalConfig",
9
- "AutoModel": "llama_bidirectional_model.LlamaBidirectionalModel"
10
- },
11
- "bos_token_id": 128000,
12
- "eos_token_id": 128001,
13
- "head_dim": 128,
14
- "hidden_act": "silu",
15
- "hidden_size": 4096,
16
- "initializer_range": 0.02,
17
- "intermediate_size": 14336,
18
- "max_position_embeddings": 131072,
19
- "mlp_bias": false,
20
- "model_type": "llama_bidirec",
21
- "num_attention_heads": 32,
22
- "num_hidden_layers": 32,
23
- "num_key_value_heads": 8,
24
- "pooling": "avg",
25
- "pretraining_tp": 1,
26
- "rms_norm_eps": 1e-05,
27
- "rope_scaling": {
28
- "factor": 8.0,
29
- "high_freq_factor": 4.0,
30
- "low_freq_factor": 1.0,
31
- "original_max_position_embeddings": 8192,
32
- "rope_type": "llama3"
33
- },
34
- "rope_theta": 500000.0,
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- "tie_word_embeddings": false,
36
- "torch_dtype": "bfloat16",
37
- "transformers_version": "4.51.0",
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- "use_cache": false,
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- "vocab_size": 128256
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- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
.ipynb_checkpoints/llama_bidirectional_model-checkpoint.py DELETED
@@ -1,54 +0,0 @@
1
- import torch
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- from transformers.cache_utils import Cache
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- from transformers.modeling_attn_mask_utils import _prepare_4d_attention_mask
4
- from transformers.models.llama.configuration_llama import LlamaConfig
5
- from transformers.models.llama.modeling_llama import LlamaModel
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-
7
-
8
- class LlamaBidirectionalConfig(LlamaConfig):
9
- model_type = "llama_bidirec"
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-
11
- def __init__(self, pooling="avg", temperature=1.0, **kwargs):
12
- self.pooling = pooling
13
- self.temperature = temperature
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-
15
- super().__init__(**kwargs)
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-
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-
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- class LlamaBidirectionalModel(LlamaModel):
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- config_class = LlamaBidirectionalConfig
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-
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- def __init__(self, config: LlamaConfig):
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- super().__init__(config)
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-
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- for layer in self.layers:
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- layer.self_attn.is_causal = False
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-
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- def _update_causal_mask(
28
- self,
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- attention_mask: torch.Tensor,
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- input_tensor: torch.Tensor,
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- cache_position: torch.Tensor,
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- past_key_values: Cache,
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- output_attentions: bool,
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- ):
35
- assert self.config._attn_implementation in [
36
- "flash_attention_2",
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- "eager",
38
- ], (
39
- f"Unsupported attention implementation: "
40
- f"{self.config._attn_implementation}, "
41
- f"only support flash_attention_2 or eager"
42
- )
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-
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- if self.config._attn_implementation == "flash_attention_2":
45
- if attention_mask is not None and (attention_mask == 0.0).any():
46
- return attention_mask
47
- return None
48
- elif self.config._attn_implementation == "eager":
49
- # Generates bi-directional attention.
50
- causal_mask = _prepare_4d_attention_mask(
51
- attention_mask,
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- dtype=input_tensor.dtype,
53
- )
54
- return causal_mask
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
.ipynb_checkpoints/special_tokens_map-checkpoint.json DELETED
@@ -1,17 +0,0 @@
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- {
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- "bos_token": {
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- "content": "<|begin_of_text|>",
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- "lstrip": false,
5
- "normalized": false,
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- "rstrip": false,
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- "single_word": false
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- },
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- "eos_token": {
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- "content": "<|end_of_text|>",
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- "lstrip": false,
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- "normalized": false,
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- "rstrip": false,
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- "single_word": false
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- },
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- "pad_token": "<|end_of_text|>"
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- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
.ipynb_checkpoints/tokenizer_config-checkpoint.json DELETED
@@ -1,2063 +0,0 @@
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- {
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- "added_tokens_decoder": {
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- "128000": {
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- "content": "<|begin_of_text|>",
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- "lstrip": false,
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  EXCEPT AS PROHIBITED BY APPLICABLE LAW, IN NO EVENT AND UNDER NO LEGAL THEORY, WHETHER IN TORT (INCLUDING NEGLIGENCE), CONTRACT, OR OTHERWISE SHALL ANY LICENSOR BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES ARISING OUT OF OR RELATED TO THIS LICENSE, THE USE OR INABILITY TO USE THE WORK (INCLUDING BUT NOT LIMITED TO LOSS OF GOODWILL, BUSINESS INTERRUPTION, LOST PROFITS OR DATA, COMPUTER FAILURE OR MALFUNCTION, OR ANY OTHER DAMAGES OR LOSSES), EVEN IF THE LICENSOR HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.
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  “Licensor” means any person or entity that distributes its Work.
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  “Work” means (a) the original work of authorship made available under this license, which may include software, documentation, or other files, and (b) any additions to or derivative works thereof that are made available under this license.
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  THE WORK IS PROVIDED “AS IS” WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WARRANTIES OR CONDITIONS OF
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  EXCEPT AS PROHIBITED BY APPLICABLE LAW, IN NO EVENT AND UNDER NO LEGAL THEORY, WHETHER IN TORT (INCLUDING NEGLIGENCE), CONTRACT, OR OTHERWISE SHALL ANY LICENSOR BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES ARISING OUT OF OR RELATED TO THIS LICENSE, THE USE OR INABILITY TO USE THE WORK (INCLUDING BUT NOT LIMITED TO LOSS OF GOODWILL, BUSINESS INTERRUPTION, LOST PROFITS OR DATA, COMPUTER FAILURE OR MALFUNCTION, OR ANY OTHER DAMAGES OR LOSSES), EVEN IF THE LICENSOR HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.
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  Governing Terms for `llama-embed-nemotron-8b` model: [Customized NSCLv1](https://huggingface.co/nvidia/llama-embed-nemotron-8b/blob/main/LICENSE) <br>
31
- Additional Information: [Llama-3.1 Community License Agreement](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct/blob/main/LICENSE) for meta-llama/Llama-3.1-8B. [Acceptable Use Policy](llama.meta.com/llama3_1/use-policy). Built with Llama.
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  For each dataset a user elects to use, the user is responsible for checking if the dataset license is fit for the intended purpose.
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  For non-commercial or research purposes only <br>
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  Governing Terms for `llama-embed-nemotron-8b` model: [Customized NSCLv1](https://huggingface.co/nvidia/llama-embed-nemotron-8b/blob/main/LICENSE) <br>
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+ Additional Information: [Llama-3.1 Community License Agreement](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct/blob/main/LICENSE) for meta-llama/Llama-3.1-8B. [Acceptable Use Policy](https://llama.meta.com/llama3_1/use-policy). Built with Llama.
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  For each dataset a user elects to use, the user is responsible for checking if the dataset license is fit for the intended purpose.
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