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finetune_rag_t_lite

PEFT
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fine-tuned-rag-t-lite

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  1. .gitattributes +3 -0
  2. README.md +82 -0
  3. adapter_config.json +29 -0
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.gitattributes CHANGED
@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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README.md ADDED
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+ ---
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+ base_model: t-bank-ai/T-lite-instruct-0.1
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+ library_name: peft
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+ tags:
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+ - trl
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+ - sft
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+ - generated_from_trainer
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+ model-index:
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+ - name: tmp
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # tmp
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+
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+ This model is a fine-tuned version of [t-bank-ai/T-lite-instruct-0.1](https://huggingface.co/t-bank-ai/T-lite-instruct-0.1) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.0736
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 8e-06
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+ - train_batch_size: 1
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+ - eval_batch_size: 1
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+ - seed: 42
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+ - gradient_accumulation_steps: 2
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+ - total_train_batch_size: 2
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_steps: 3
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+ - training_steps: 20
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss |
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+ |:-------------:|:------:|:----:|:---------------:|
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+ | 1.0658 | 0.0002 | 1 | 1.0817 |
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+ | 0.9432 | 0.0004 | 2 | 1.0814 |
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+ | 1.1006 | 0.0006 | 3 | 1.0809 |
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+ | 0.8838 | 0.0008 | 4 | 1.0801 |
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+ | 1.1528 | 0.0010 | 5 | 1.0793 |
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+ | 0.8889 | 0.0012 | 6 | 1.0786 |
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+ | 1.1655 | 0.0014 | 7 | 1.0780 |
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+ | 1.0079 | 0.0016 | 8 | 1.0774 |
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+ | 1.1685 | 0.0018 | 9 | 1.0768 |
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+ | 1.1659 | 0.0020 | 10 | 1.0763 |
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+ | 1.1395 | 0.0022 | 11 | 1.0758 |
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+ | 0.9426 | 0.0024 | 12 | 1.0753 |
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+ | 0.9772 | 0.0026 | 13 | 1.0750 |
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+ | 1.3758 | 0.0028 | 14 | 1.0746 |
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+ | 0.7022 | 0.0030 | 15 | 1.0743 |
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+ | 1.5761 | 0.0032 | 16 | 1.0741 |
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+ | 1.2847 | 0.0034 | 17 | 1.0739 |
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+ | 1.1528 | 0.0036 | 18 | 1.0737 |
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+ | 0.8362 | 0.0038 | 19 | 1.0736 |
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+ | 0.9096 | 0.0040 | 20 | 1.0736 |
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+
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+
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+ ### Framework versions
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+
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+ - PEFT 0.13.2
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+ - Transformers 4.45.2
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+ - Pytorch 2.4.0
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+ - Datasets 3.0.1
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+ - Tokenizers 0.20.0
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+ #!/bin/bash
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+ #
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+ # This scripts should be called at the end of each RUN command
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+ #
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+ # Each RUN command creates a new layer that is stored separately.
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+ # At the end of each command, we should ensure we clean up downloaded
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+ # archives and source files used to produce binary to reduce the size
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+ # of the layer.
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+ set -e
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+ # Delete files that pip caches when installing a package.
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tmp_512_54i/__pycache__/_remote_module_non_scriptable.cpython-310.pyc ADDED
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tmp_512_54i/_remote_module_non_scriptable.py ADDED
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1
+ from typing import *
2
+
3
+ import torch
4
+ import torch.distributed.rpc as rpc
5
+ from torch import Tensor
6
+ from torch._jit_internal import Future
7
+ from torch.distributed.rpc import RRef
8
+ from typing import Tuple # pyre-ignore: unused import
9
+
10
+
11
+ module_interface_cls = None
12
+
13
+
14
+ def forward_async(self, *args, **kwargs):
15
+ args = (self.module_rref, self.device, self.is_device_map_set, *args)
16
+ kwargs = {**kwargs}
17
+ return rpc.rpc_async(
18
+ self.module_rref.owner(),
19
+ _remote_forward,
20
+ args,
21
+ kwargs,
22
+ )
23
+
24
+
25
+ def forward(self, *args, **kwargs):
26
+ args = (self.module_rref, self.device, self.is_device_map_set, *args)
27
+ kwargs = {**kwargs}
28
+ ret_fut = rpc.rpc_async(
29
+ self.module_rref.owner(),
30
+ _remote_forward,
31
+ args,
32
+ kwargs,
33
+ )
34
+ return ret_fut.wait()
35
+
36
+
37
+ _generated_methods = [
38
+ forward_async,
39
+ forward,
40
+ ]
41
+
42
+
43
+
44
+
45
+ def _remote_forward(
46
+ module_rref: RRef[module_interface_cls], device: str, is_device_map_set: bool, *args, **kwargs):
47
+ module = module_rref.local_value()
48
+ device = torch.device(device)
49
+
50
+ if device.type != "cuda":
51
+ return module.forward(*args, **kwargs)
52
+
53
+ # If the module is on a cuda device,
54
+ # move any CPU tensor in args or kwargs to the same cuda device.
55
+ # Since torch script does not support generator expression,
56
+ # have to use concatenation instead of
57
+ # ``tuple(i.to(device) if isinstance(i, Tensor) else i for i in *args)``.
58
+ args = (*args,)
59
+ out_args: Tuple[()] = ()
60
+ for arg in args:
61
+ arg = (arg.to(device),) if isinstance(arg, Tensor) else (arg,)
62
+ out_args = out_args + arg
63
+
64
+ kwargs = {**kwargs}
65
+ for k, v in kwargs.items():
66
+ if isinstance(v, Tensor):
67
+ kwargs[k] = kwargs[k].to(device)
68
+
69
+ if is_device_map_set:
70
+ return module.forward(*out_args, **kwargs)
71
+
72
+ # If the device map is empty, then only CPU tensors are allowed to send over wire,
73
+ # so have to move any GPU tensor to CPU in the output.
74
+ # Since torch script does not support generator expression,
75
+ # have to use concatenation instead of
76
+ # ``tuple(i.cpu() if isinstance(i, Tensor) else i for i in module.forward(*out_args, **kwargs))``.
77
+ ret: Tuple[()] = ()
78
+ for i in module.forward(*out_args, **kwargs):
79
+ i = (i.cpu(),) if isinstance(i, Tensor) else (i,)
80
+ ret = ret + i
81
+ return ret
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The diff for this file is too large to render. See raw diff
 
yarn--1718259841469-0.525546751866067/node ADDED
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1
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1
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1
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2
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3
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1
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yarn--1718259995929-0.6710047446011926/yarn ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
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2
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3
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