upload-8-bit
Browse files- README.md +195 -0
- config.json +42 -0
- model-00001-of-00003.safetensors +3 -0
- model-00002-of-00003.safetensors +3 -0
- model-00003-of-00003.safetensors +3 -0
- model.safetensors.index.json +0 -0
- modeling_custom.py +166 -0
- special_tokens_map.json +23 -0
- tokenizer.json +0 -0
- tokenizer_config.json +2071 -0
README.md
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| 1 |
+
---
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| 2 |
+
license: llama3
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| 3 |
+
---
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| 4 |
+
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| 5 |
+
# Absolute-Rating Multi-Objective Reward Model (ArmoRM) with Mixture-of-Experts (MoE) Aggregation of Reward Objectives
|
| 6 |
+
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| 7 |
+
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| 8 |
+
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| 9 |
+
+ **Authors** (* indicates equal contribution)
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| 10 |
+
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| 11 |
+
[Haoxiang Wang*](https://haoxiang-wang.github.io/), [Wei Xiong*](https://weixiongust.github.io/WeiXiongUST/index.html), [Tengyang Xie](https://tengyangxie.github.io/), [Han Zhao](https://hanzhaoml.github.io/), [Tong Zhang](https://tongzhang-ml.org/)
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+
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+ **Blog**: https://rlhflow.github.io/posts/2024-05-29-multi-objective-reward-modeling/
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| 14 |
+
+ **Tech Report**: https://arxiv.org/abs/2406.12845
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| 15 |
+
+ **Model**: [ArmoRM-Llama3-8B-v0.1](https://huggingface.co/RLHFlow/ArmoRM-Llama3-8B-v0.1)
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| 16 |
+
+ Finetuned from model: [FsfairX-LLaMA3-RM-v0.1](https://huggingface.co/sfairXC/FsfairX-LLaMA3-RM-v0.1)
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| 17 |
+
- **Code Repository:** https://github.com/RLHFlow/RLHF-Reward-Modeling/
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+
+ **Architecture**
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| 19 |
+
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| 20 |
+
<p align="center">
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| 21 |
+
<img width="800" alt="image" src="https://github.com/RLHFlow/RLHFlow.github.io/blob/main/assets/ArmoRM-MoE.png?raw=true">
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| 22 |
+
</p>
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| 23 |
+
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| 24 |
+
## RewardBench LeaderBoard
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| 25 |
+
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| 26 |
+
| Model | Base Model | Method | Score | Chat | Chat Hard | Safety | Reasoning | Prior Sets (0.5 weight) |
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| 27 |
+
|:--------------------------------------------------------------------------------|:-----------------------------------------------------------------------|:-----:|:-----|:----------|:-------|:----------|:-----------------------|:------------------------|
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| 28 |
+
| ArmoRM-Llama3-8B-v0.1 | Llama-3 8B | ArmoRM + MoE | **89.0** | 96.9 | **76.8** | **92.2** | **97.3** | 74.3 |
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| 29 |
+
| Cohere May 2024 | Unknown | Unknown | 88.3 | 96.4 | 71.3 | **92.7** | **97.7** | **78.2** |
|
| 30 |
+
| [pair-preference-model](https://huggingface.co/RLHFlow/pair-preference-model-LLaMA3-8B)| Llama-3 8B | [SliC-HF](https://arxiv.org/abs/2305.10425) | 85.7 | 98.3 | 65.8 | 89.7 | 94.7 | 74.6 |
|
| 31 |
+
| GPT-4 Turbo (0125 version) | GPT-4 Turbo | LLM-as-a-Judge | 84.3 | 95.3 | 74.3 | 87.2 | 86.9 | 70.9 |
|
| 32 |
+
| [FsfairX-LLaMA3-RM-v0.1](https://huggingface.co/sfairXC/FsfairX-LLaMA3-RM-v0.1) | Llama-3 8B | Bradley-Terry | 83.6 | **99.4** | 65.1 | 87.8 | 86.4 | 74.9 |
|
| 33 |
+
| [Starling-RM-34B](https://huggingface.co/Nexusflow/Starling-RM-34B) | Yi-34B | Bradley-Terry | 81.4 | 96.9 | 57.2 | 88.2 | 88.5 | 71.4 |
|
| 34 |
+
|
| 35 |
+
## Demo Code
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| 36 |
+
```python
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| 37 |
+
import torch
|
| 38 |
+
from transformers import AutoModelForSequenceClassification, AutoTokenizer
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| 39 |
+
device = "cuda"
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| 40 |
+
path = "RLHFlow/ArmoRM-Llama3-8B-v0.1"
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| 41 |
+
model = AutoModelForSequenceClassification.from_pretrained(path, device_map=device,
|
| 42 |
+
trust_remote_code=True, torch_dtype=torch.bfloat16)
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| 43 |
+
tokenizer = AutoTokenizer.from_pretrained(path, use_fast=True)
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| 44 |
+
# We load a random sample from the validation set of the HelpSteer dataset
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| 45 |
+
prompt = 'What are some synonyms for the word "beautiful"?'
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| 46 |
+
response = "Nicely, Beautifully, Handsome, Stunning, Wonderful, Gorgeous, Pretty, Stunning, Elegant"
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| 47 |
+
messages = [{"role": "user", "content": prompt},
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| 48 |
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{"role": "assistant", "content": response}]
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| 49 |
+
input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt").to(device)
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| 50 |
+
with torch.no_grad():
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| 51 |
+
output = model(input_ids)
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| 52 |
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# Multi-objective rewards for the response
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| 53 |
+
multi_obj_rewards = output.rewards.cpu().float()
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| 54 |
+
# The gating layer's output is conditioned on the prompt
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| 55 |
+
gating_output = output.gating_output.cpu().float()
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| 56 |
+
# The preference score for the response, aggregated from the
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| 57 |
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# multi-objective rewards with the gating layer
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| 58 |
+
preference_score = output.score.cpu().float()
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| 59 |
+
# We apply a transformation matrix to the multi-objective rewards
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| 60 |
+
# before multiplying with the gating layer's output. This mainly aims
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| 61 |
+
# at reducing the verbosity bias of the original reward objectives
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| 62 |
+
obj_transform = model.reward_transform_matrix.data.cpu().float()
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| 63 |
+
# The final coefficients assigned to each reward objective
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| 64 |
+
multi_obj_coeffs = gating_output @ obj_transform.T
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| 65 |
+
# The preference score is the linear combination of the multi-objective rewards with
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| 66 |
+
# the multi-objective coefficients, which can be verified by the following assertion
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| 67 |
+
assert torch.isclose(torch.sum(multi_obj_rewards * multi_obj_coeffs, dim=1), preference_score, atol=1e-3)
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| 68 |
+
# Find the top-K reward objectives with coefficients of the highest magnitude
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| 69 |
+
K = 3
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| 70 |
+
top_obj_dims = torch.argsort(torch.abs(multi_obj_coeffs), dim=1, descending=True,)[:, :K]
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| 71 |
+
top_obj_coeffs = torch.gather(multi_obj_coeffs, dim=1, index=top_obj_dims)
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| 72 |
+
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| 73 |
+
# The attributes of the 19 reward objectives
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| 74 |
+
attributes = ['helpsteer-helpfulness','helpsteer-correctness','helpsteer-coherence',
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| 75 |
+
'helpsteer-complexity','helpsteer-verbosity','ultrafeedback-overall_score',
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| 76 |
+
'ultrafeedback-instruction_following', 'ultrafeedback-truthfulness',
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| 77 |
+
'ultrafeedback-honesty','ultrafeedback-helpfulness','beavertails-is_safe',
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| 78 |
+
'prometheus-score','argilla-overall_quality','argilla-judge_lm','code-complexity',
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| 79 |
+
'code-style','code-explanation','code-instruction-following','code-readability']
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| 80 |
+
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| 81 |
+
example_index = 0
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| 82 |
+
for i in range(K):
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| 83 |
+
attribute = attributes[top_obj_dims[example_index, i].item()]
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| 84 |
+
coeff = top_obj_coeffs[example_index, i].item()
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| 85 |
+
print(f"{attribute}: {round(coeff,5)}")
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| 86 |
+
# code-complexity: 0.19922
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| 87 |
+
# helpsteer-verbosity: -0.10864
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| 88 |
+
# ultrafeedback-instruction_following: 0.07861
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| 89 |
+
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| 90 |
+
# The actual rewards of this example from the HelpSteer dataset
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| 91 |
+
# are [3,3,4,2,2] for the five helpsteer objectives:
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| 92 |
+
# helpfulness, correctness, coherence, complexity, verbosity
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| 93 |
+
# We can linearly transform our predicted rewards to the
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| 94 |
+
# original reward space to compare with the ground truth
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| 95 |
+
helpsteer_rewards_pred = multi_obj_rewards[0, :5] * 5 - 0.5
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| 96 |
+
print(helpsteer_rewards_pred)
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| 97 |
+
# [2.78125 2.859375 3.484375 1.3847656 1.296875 ]
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| 98 |
+
```
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| 99 |
+
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| 100 |
+
## Easy to use Pipeline
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| 101 |
+
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| 102 |
+
```python
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| 103 |
+
from typing import Dict, List
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| 104 |
+
import torch
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| 105 |
+
from transformers import AutoModelForSequenceClassification, AutoTokenizer
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| 106 |
+
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| 107 |
+
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| 108 |
+
class ArmoRMPipeline:
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| 109 |
+
def __init__(self, model_id, device_map="auto", torch_dtype=torch.bfloat16, truncation=True, trust_remote_code=False, max_length=4096):
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| 110 |
+
self.model = AutoModelForSequenceClassification.from_pretrained(
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| 111 |
+
model_id,
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| 112 |
+
device_map=device_map,
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| 113 |
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trust_remote_code=trust_remote_code,
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| 114 |
+
torch_dtype=torch_dtype,
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| 115 |
+
)
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| 116 |
+
self.tokenizer = AutoTokenizer.from_pretrained(
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| 117 |
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model_id,
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| 118 |
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use_fast=True,
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| 119 |
+
)
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| 120 |
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self.truncation = truncation
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| 121 |
+
self.device = self.model.device
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| 122 |
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self.max_length = max_length
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| 123 |
+
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| 124 |
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def __call__(self, messages: List[Dict[str, str]]) -> Dict[str, float]:
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| 125 |
+
"""
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| 126 |
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messages: OpenAI chat messages to be scored
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| 127 |
+
Note: no batching since due to length differences, the model will have to pad to the max length which is not efficient
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| 128 |
+
Returns: a dictionary with the score between 0 and 1
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| 129 |
+
"""
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| 130 |
+
input_ids = self.tokenizer.apply_chat_template(
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| 131 |
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messages,
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| 132 |
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return_tensors="pt",
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| 133 |
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padding=True,
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| 134 |
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truncation=self.truncation,
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| 135 |
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max_length=self.max_length,
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| 136 |
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).to(self.device)
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| 137 |
+
with torch.no_grad():
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| 138 |
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output = self.model(input_ids)
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| 139 |
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score = output.score.float().item()
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| 140 |
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return {"score": score}
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| 141 |
+
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| 142 |
+
# Create Reward Model Pipeline
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| 143 |
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prompt = 'What are some synonyms for the word "beautiful"?'
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| 144 |
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rm = ArmoRMPipeline("RLHFlow/ArmoRM-Llama3-8B-v0.1", trust_remote_code=True)
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| 145 |
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# score the messages
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| 146 |
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response1 = 'Nicely, Beautifully, Handsome, Stunning, Wonderful, Gorgeous, Pretty, Stunning, Elegant'
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| 147 |
+
score1 = rm([{"role": "user", "content": prompt}, {"role": "assistant", "content": response1}])
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| 148 |
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print(score1)
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| 149 |
+
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| 150 |
+
response2 = '''Certainly! Here are some synonyms for the word "beautiful":
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| 151 |
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| 152 |
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1. Gorgeous
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| 153 |
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2. Lovely
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| 154 |
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3. Stunning
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| 155 |
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4. Attractive
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| 156 |
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5. Pretty
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| 157 |
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6. Elegant
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| 158 |
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7. Exquisite
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| 159 |
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8. Handsome
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| 160 |
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9. Charming
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| 161 |
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10. Alluring
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| 162 |
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11. Radiant
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| 163 |
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12. Magnificent
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| 164 |
+
13. Graceful
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| 165 |
+
14. Enchanting
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| 166 |
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15. Dazzling
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| 167 |
+
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| 168 |
+
These synonyms can be used in various contexts to convey the idea of beauty.'''
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| 169 |
+
score2 = rm([{"role": "user", "content": prompt}, {"role": "assistant", "content": response2}])
|
| 170 |
+
print(score2)
|
| 171 |
+
|
| 172 |
+
response3 = 'Sorry i cannot answer this.'
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| 173 |
+
score3 = rm([{"role": "user", "content": prompt}, {"role": "assistant", "content": response3}])
|
| 174 |
+
print(score3)
|
| 175 |
+
|
| 176 |
+
```
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| 177 |
+
|
| 178 |
+
## Citation
|
| 179 |
+
|
| 180 |
+
If you find this work useful for your research, please consider citing:
|
| 181 |
+
```
|
| 182 |
+
@article{ArmoRM,
|
| 183 |
+
title={Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Experts},
|
| 184 |
+
author={Haoxiang Wang and Wei Xiong and Tengyang Xie and Han Zhao and Tong Zhang},
|
| 185 |
+
journal={arXiv preprint arXiv:2406.12845},
|
| 186 |
+
}
|
| 187 |
+
|
| 188 |
+
@inproceedings{wang2024arithmetic,
|
| 189 |
+
title={Arithmetic Control of LLMs for Diverse User Preferences: Directional Preference Alignment with Multi-Objective Rewards},
|
| 190 |
+
author={Haoxiang Wang and Yong Lin and Wei Xiong and Rui Yang and Shizhe Diao and Shuang Qiu and Han Zhao and Tong Zhang},
|
| 191 |
+
year={2024},
|
| 192 |
+
booktitle={ACL},
|
| 193 |
+
}
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| 194 |
+
```
|
| 195 |
+
The second entry, "[Arithmetic Control of LLMs for Diverse User Preferences: Directional Preference Alignment with Multi-Objective Rewards](https://arxiv.org/abs/2402.18571)", is another recent work of ours that trained a multi-objective reward model and adopted it for LLM alignment, which motivated us to develop the current work.
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config.json
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{
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"_name_or_path": "../sample_models/ArmoRM-llama3/",
|
| 3 |
+
"architectures": [
|
| 4 |
+
"LlamaForRewardModelWithGating"
|
| 5 |
+
],
|
| 6 |
+
"attention_bias": false,
|
| 7 |
+
"attention_dropout": 0.0,
|
| 8 |
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"auto_map": {
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| 9 |
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"AutoModelForSequenceClassification": "modeling_custom.LlamaForRewardModelWithGating"
|
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+
},
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| 11 |
+
"bos_token_id": 128000,
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| 12 |
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"eos_token_id": 128001,
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| 13 |
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"gating_hidden_dim": 1024,
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| 14 |
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"gating_n_hidden": 3,
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| 15 |
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"gating_temperature": 10,
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| 16 |
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"hidden_act": "silu",
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| 17 |
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"hidden_size": 4096,
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| 18 |
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"id2label": {
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| 19 |
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"0": "LABEL_0"
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+
},
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| 21 |
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"initializer_range": 0.02,
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| 22 |
+
"intermediate_size": 14336,
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| 23 |
+
"label2id": {
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| 24 |
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"LABEL_0": 0
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},
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| 26 |
+
"max_position_embeddings": 8192,
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| 27 |
+
"model_type": "llama",
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| 28 |
+
"num_attention_heads": 32,
|
| 29 |
+
"num_hidden_layers": 32,
|
| 30 |
+
"num_key_value_heads": 8,
|
| 31 |
+
"num_objectives": 19,
|
| 32 |
+
"pad_token_id": 128256,
|
| 33 |
+
"pretraining_tp": 1,
|
| 34 |
+
"rms_norm_eps": 1e-05,
|
| 35 |
+
"rope_scaling": null,
|
| 36 |
+
"rope_theta": 500000.0,
|
| 37 |
+
"tie_word_embeddings": false,
|
| 38 |
+
"torch_dtype": "bfloat16",
|
| 39 |
+
"transformers_version": "4.40.2",
|
| 40 |
+
"use_cache": false,
|
| 41 |
+
"vocab_size": 128257
|
| 42 |
+
}
|
model-00001-of-00003.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:5b11be57764ed178b13686bc915204be0fe5d33ff3f10a7bcfd0ba87c92adf75
|
| 3 |
+
size 2985565289
|
model-00002-of-00003.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:9399d504a5a4006eaa25e5934c5fd4b1ce0504ad7df96046b8057785b5a6b467
|
| 3 |
+
size 2905525906
|
model-00003-of-00003.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:928fa8d99811923e8a91227f95f3cd4c7060495e765d36c9b8bdc207302d4029
|
| 3 |
+
size 566475160
|
model.safetensors.index.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
modeling_custom.py
ADDED
|
@@ -0,0 +1,166 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
from dataclasses import dataclass
|
| 2 |
+
from typing import Optional, List, Tuple
|
| 3 |
+
|
| 4 |
+
import torch
|
| 5 |
+
import torch.nn as nn
|
| 6 |
+
import torch.nn.functional as F
|
| 7 |
+
import torch.utils.checkpoint
|
| 8 |
+
from transformers import LlamaModel, LlamaPreTrainedModel
|
| 9 |
+
from transformers.models.llama.modeling_llama import LLAMA_INPUTS_DOCSTRING
|
| 10 |
+
from transformers.utils import ModelOutput
|
| 11 |
+
from transformers.utils import add_start_docstrings_to_model_forward
|
| 12 |
+
|
| 13 |
+
|
| 14 |
+
class GatingNetwork(nn.Module):
|
| 15 |
+
def __init__(self, in_features: int, out_features: int, bias: bool = True, temperature: float = 10,
|
| 16 |
+
logit_scale: float = 1., hidden_dim: int = 1024, n_hidden: int = 3):
|
| 17 |
+
super().__init__()
|
| 18 |
+
self.temperature = temperature
|
| 19 |
+
self.logit_scale = nn.Parameter(torch.ones(1) * logit_scale)
|
| 20 |
+
layers = []
|
| 21 |
+
for _ in range(n_hidden):
|
| 22 |
+
layers.append(nn.Linear(in_features, hidden_dim))
|
| 23 |
+
in_features = hidden_dim
|
| 24 |
+
layers.append(nn.Linear(in_features, out_features, bias=bias))
|
| 25 |
+
self.layers = nn.ModuleList(layers)
|
| 26 |
+
|
| 27 |
+
def forward(self, x: torch.FloatTensor) -> torch.FloatTensor:
|
| 28 |
+
# Apply the linear layers with ReLU
|
| 29 |
+
for i, layer in enumerate(self.layers):
|
| 30 |
+
x = F.relu(layer(x)) if i < len(self.layers) - 1 else layer(x)
|
| 31 |
+
# Apply the conditional ReLU using the expanded mask
|
| 32 |
+
x = F.softmax(x / self.temperature, dim=1)
|
| 33 |
+
return x * self.logit_scale[0]
|
| 34 |
+
|
| 35 |
+
|
| 36 |
+
# token_pattern = tokenizer.encode("<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n", add_special_tokens=False, )
|
| 37 |
+
token_pattern = [128009, 128006, 78191, 128007, 271]
|
| 38 |
+
|
| 39 |
+
|
| 40 |
+
def find_token_for_gating(lst, ):
|
| 41 |
+
"""Find the last occurrence of a token_pattern in a list."""
|
| 42 |
+
token_pattern_len = len(token_pattern)
|
| 43 |
+
search_end = len(lst)
|
| 44 |
+
for j in range(search_end - token_pattern_len, -1, -1):
|
| 45 |
+
if lst[j:j + token_pattern_len] == token_pattern:
|
| 46 |
+
return j
|
| 47 |
+
raise ValueError("Token pattern not found in the list.")
|
| 48 |
+
|
| 49 |
+
|
| 50 |
+
@dataclass
|
| 51 |
+
class CustomOutput(ModelOutput):
|
| 52 |
+
"""
|
| 53 |
+
Base class for outputs of sentence classification models.
|
| 54 |
+
|
| 55 |
+
Args:
|
| 56 |
+
hidden_state (`Tuple[torch.FloatTensor]` of length `config.num_hidden_layers`):
|
| 57 |
+
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
|
| 58 |
+
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
|
| 59 |
+
|
| 60 |
+
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
|
| 61 |
+
prompt_embedding (`torch.FloatTensor` of shape `(batch_size, hidden_size)`):
|
| 62 |
+
The embeddings of the prompt tokens.
|
| 63 |
+
gating_output (`torch.FloatTensor` of shape `(batch_size, config.num_objectives)`):
|
| 64 |
+
The logits for the gating network.
|
| 65 |
+
score (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
|
| 66 |
+
The final reward score.
|
| 67 |
+
logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
|
| 68 |
+
Same as score
|
| 69 |
+
"""
|
| 70 |
+
|
| 71 |
+
rewards: torch.FloatTensor = None
|
| 72 |
+
hidden_state: Optional[Tuple[torch.FloatTensor, ...]] = None
|
| 73 |
+
prompt_embedding: Optional[torch.FloatTensor] = None
|
| 74 |
+
gating_output: Optional[torch.FloatTensor] = None
|
| 75 |
+
score: Optional[torch.FloatTensor] = None
|
| 76 |
+
logits: Optional[torch.FloatTensor] = None
|
| 77 |
+
|
| 78 |
+
|
| 79 |
+
class LlamaForRewardModelWithGating(LlamaPreTrainedModel):
|
| 80 |
+
def __init__(self, config):
|
| 81 |
+
super().__init__(config)
|
| 82 |
+
self.num_labels = config.num_labels
|
| 83 |
+
self.model = LlamaModel(config)
|
| 84 |
+
config_dict = config.to_dict()
|
| 85 |
+
self.num_objectives = config_dict.get("num_objectives", 19)
|
| 86 |
+
self.regression_layer = nn.Linear(config.hidden_size, self.num_objectives, bias=False)
|
| 87 |
+
self.post_init()
|
| 88 |
+
# Not using torch.eye because it is not supported in BF16
|
| 89 |
+
I = torch.zeros(self.num_objectives, self.num_objectives)
|
| 90 |
+
I[range(self.num_objectives), range(self.num_objectives)] = 1.
|
| 91 |
+
self.reward_transform_matrix = nn.Parameter(I)
|
| 92 |
+
self.reward_transform_matrix.requires_grad = False
|
| 93 |
+
|
| 94 |
+
# Initialize weights and apply final processing
|
| 95 |
+
self.gating = GatingNetwork(config.hidden_size, config.num_objectives,
|
| 96 |
+
temperature=config_dict.get("gating_temperature", 10),
|
| 97 |
+
hidden_dim=config_dict.get("gating_hidden_dim", 1024),
|
| 98 |
+
n_hidden=config_dict.get("gating_n_hidden", 3))
|
| 99 |
+
|
| 100 |
+
@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
|
| 101 |
+
def forward(
|
| 102 |
+
self,
|
| 103 |
+
input_ids: torch.LongTensor = None,
|
| 104 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 105 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 106 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
| 107 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
| 108 |
+
labels: Optional[torch.FloatTensor] = None,
|
| 109 |
+
use_cache: Optional[bool] = None,
|
| 110 |
+
output_attentions: Optional[bool] = None,
|
| 111 |
+
output_hidden_states: Optional[bool] = None,
|
| 112 |
+
return_dict: Optional[bool] = None,
|
| 113 |
+
) -> CustomOutput:
|
| 114 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
| 115 |
+
|
| 116 |
+
transformer_outputs = self.model(
|
| 117 |
+
input_ids,
|
| 118 |
+
attention_mask=attention_mask,
|
| 119 |
+
position_ids=position_ids,
|
| 120 |
+
past_key_values=past_key_values,
|
| 121 |
+
inputs_embeds=inputs_embeds,
|
| 122 |
+
use_cache=use_cache,
|
| 123 |
+
output_attentions=output_attentions,
|
| 124 |
+
output_hidden_states=output_hidden_states,
|
| 125 |
+
return_dict=return_dict,
|
| 126 |
+
)
|
| 127 |
+
tokens_hidden_states = transformer_outputs[0]
|
| 128 |
+
|
| 129 |
+
if input_ids is not None:
|
| 130 |
+
batch_size = input_ids.shape[0]
|
| 131 |
+
else:
|
| 132 |
+
batch_size = inputs_embeds.shape[0]
|
| 133 |
+
|
| 134 |
+
if self.config.pad_token_id is None and batch_size != 1:
|
| 135 |
+
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
|
| 136 |
+
if self.config.pad_token_id is None:
|
| 137 |
+
sequence_lengths = -1
|
| 138 |
+
else:
|
| 139 |
+
if input_ids is not None:
|
| 140 |
+
# if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
|
| 141 |
+
sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
|
| 142 |
+
sequence_lengths = sequence_lengths % input_ids.shape[-1]
|
| 143 |
+
sequence_lengths = sequence_lengths.to(tokens_hidden_states.device)
|
| 144 |
+
else:
|
| 145 |
+
sequence_lengths = -1
|
| 146 |
+
|
| 147 |
+
dummy_iterator = torch.arange(batch_size, device=tokens_hidden_states.device)
|
| 148 |
+
hidden_states = tokens_hidden_states[dummy_iterator, sequence_lengths]
|
| 149 |
+
assert hidden_states.shape == (batch_size, self.config.hidden_size)
|
| 150 |
+
rewards = self.regression_layer(hidden_states)
|
| 151 |
+
|
| 152 |
+
gating_token_positions = [find_token_for_gating(ids.tolist()) for ids in input_ids]
|
| 153 |
+
prompt_embedding = tokens_hidden_states[dummy_iterator, gating_token_positions, :]
|
| 154 |
+
gating_output = self.gating(prompt_embedding)
|
| 155 |
+
|
| 156 |
+
rewards_adjusted = rewards @ self.reward_transform_matrix
|
| 157 |
+
score = torch.sum(gating_output * rewards_adjusted, dim=1)
|
| 158 |
+
|
| 159 |
+
return CustomOutput(
|
| 160 |
+
rewards=rewards,
|
| 161 |
+
hidden_state=hidden_states,
|
| 162 |
+
prompt_embedding=prompt_embedding,
|
| 163 |
+
gating_output=gating_output,
|
| 164 |
+
score=score,
|
| 165 |
+
logits=score,
|
| 166 |
+
)
|
special_tokens_map.json
ADDED
|
@@ -0,0 +1,23 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"bos_token": {
|
| 3 |
+
"content": "<|begin_of_text|>",
|
| 4 |
+
"lstrip": false,
|
| 5 |
+
"normalized": false,
|
| 6 |
+
"rstrip": false,
|
| 7 |
+
"single_word": false
|
| 8 |
+
},
|
| 9 |
+
"eos_token": {
|
| 10 |
+
"content": "<|end_of_text|>",
|
| 11 |
+
"lstrip": false,
|
| 12 |
+
"normalized": false,
|
| 13 |
+
"rstrip": false,
|
| 14 |
+
"single_word": false
|
| 15 |
+
},
|
| 16 |
+
"pad_token": {
|
| 17 |
+
"content": "[PAD]",
|
| 18 |
+
"lstrip": false,
|
| 19 |
+
"normalized": false,
|
| 20 |
+
"rstrip": false,
|
| 21 |
+
"single_word": false
|
| 22 |
+
}
|
| 23 |
+
}
|
tokenizer.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,2071 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
| 1 |
+
{
|
| 2 |
+
"added_tokens_decoder": {
|
| 3 |
+
"128000": {
|
| 4 |
+
"content": "<|begin_of_text|>",
|
| 5 |
+
"lstrip": false,
|
| 6 |
+
"normalized": false,
|
| 7 |
+
"rstrip": false,
|
| 8 |
+
"single_word": false,
|
| 9 |
+
"special": true
|
| 10 |
+
},
|
| 11 |
+
"128001": {
|
| 12 |
+
"content": "<|end_of_text|>",
|
| 13 |
+
"lstrip": false,
|
| 14 |
+
"normalized": false,
|
| 15 |
+
"rstrip": false,
|
| 16 |
+
"single_word": false,
|
| 17 |
+
"special": true
|
| 18 |
+
},
|
| 19 |
+
"128002": {
|
| 20 |
+
"content": "<|reserved_special_token_0|>",
|
| 21 |
+
"lstrip": false,
|
| 22 |
+
"normalized": false,
|
| 23 |
+
"rstrip": false,
|
| 24 |
+
"single_word": false,
|
| 25 |
+
"special": true
|
| 26 |
+
},
|
| 27 |
+
"128003": {
|
| 28 |
+
"content": "<|reserved_special_token_1|>",
|
| 29 |
+
"lstrip": false,
|
| 30 |
+
"normalized": false,
|
| 31 |
+
"rstrip": false,
|
| 32 |
+
"single_word": false,
|
| 33 |
+
"special": true
|
| 34 |
+
},
|
| 35 |
+
"128004": {
|
| 36 |
+
"content": "<|reserved_special_token_2|>",
|
| 37 |
+
"lstrip": false,
|
| 38 |
+
"normalized": false,
|
| 39 |
+
"rstrip": false,
|
| 40 |
+
"single_word": false,
|
| 41 |
+
"special": true
|
| 42 |
+
},
|
| 43 |
+
"128005": {
|
| 44 |
+
"content": "<|reserved_special_token_3|>",
|
| 45 |
+
"lstrip": false,
|
| 46 |
+
"normalized": false,
|
| 47 |
+
"rstrip": false,
|
| 48 |
+
"single_word": false,
|
| 49 |
+
"special": true
|
| 50 |
+
},
|
| 51 |
+
"128006": {
|
| 52 |
+
"content": "<|start_header_id|>",
|
| 53 |
+
"lstrip": false,
|
| 54 |
+
"normalized": false,
|
| 55 |
+
"rstrip": false,
|
| 56 |
+
"single_word": false,
|
| 57 |
+
"special": true
|
| 58 |
+
},
|
| 59 |
+
"128007": {
|
| 60 |
+
"content": "<|end_header_id|>",
|
| 61 |
+
"lstrip": false,
|
| 62 |
+
"normalized": false,
|
| 63 |
+
"rstrip": false,
|
| 64 |
+
"single_word": false,
|
| 65 |
+
"special": true
|
| 66 |
+
},
|
| 67 |
+
"128008": {
|
| 68 |
+
"content": "<|reserved_special_token_4|>",
|
| 69 |
+
"lstrip": false,
|
| 70 |
+
"normalized": false,
|
| 71 |
+
"rstrip": false,
|
| 72 |
+
"single_word": false,
|
| 73 |
+
"special": true
|
| 74 |
+
},
|
| 75 |
+
"128009": {
|
| 76 |
+
"content": "<|eot_id|>",
|
| 77 |
+
"lstrip": false,
|
| 78 |
+
"normalized": false,
|
| 79 |
+
"rstrip": false,
|
| 80 |
+
"single_word": false,
|
| 81 |
+
"special": true
|
| 82 |
+
},
|
| 83 |
+
"128010": {
|
| 84 |
+
"content": "<|reserved_special_token_5|>",
|
| 85 |
+
"lstrip": false,
|
| 86 |
+
"normalized": false,
|
| 87 |
+
"rstrip": false,
|
| 88 |
+
"single_word": false,
|
| 89 |
+
"special": true
|
| 90 |
+
},
|
| 91 |
+
"128011": {
|
| 92 |
+
"content": "<|reserved_special_token_6|>",
|
| 93 |
+
"lstrip": false,
|
| 94 |
+
"normalized": false,
|
| 95 |
+
"rstrip": false,
|
| 96 |
+
"single_word": false,
|
| 97 |
+
"special": true
|
| 98 |
+
},
|
| 99 |
+
"128012": {
|
| 100 |
+
"content": "<|reserved_special_token_7|>",
|
| 101 |
+
"lstrip": false,
|
| 102 |
+
"normalized": false,
|
| 103 |
+
"rstrip": false,
|
| 104 |
+
"single_word": false,
|
| 105 |
+
"special": true
|
| 106 |
+
},
|
| 107 |
+
"128013": {
|
| 108 |
+
"content": "<|reserved_special_token_8|>",
|
| 109 |
+
"lstrip": false,
|
| 110 |
+
"normalized": false,
|
| 111 |
+
"rstrip": false,
|
| 112 |
+
"single_word": false,
|
| 113 |
+
"special": true
|
| 114 |
+
},
|
| 115 |
+
"128014": {
|
| 116 |
+
"content": "<|reserved_special_token_9|>",
|
| 117 |
+
"lstrip": false,
|
| 118 |
+
"normalized": false,
|
| 119 |
+
"rstrip": false,
|
| 120 |
+
"single_word": false,
|
| 121 |
+
"special": true
|
| 122 |
+
},
|
| 123 |
+
"128015": {
|
| 124 |
+
"content": "<|reserved_special_token_10|>",
|
| 125 |
+
"lstrip": false,
|
| 126 |
+
"normalized": false,
|
| 127 |
+
"rstrip": false,
|
| 128 |
+
"single_word": false,
|
| 129 |
+
"special": true
|
| 130 |
+
},
|
| 131 |
+
"128016": {
|
| 132 |
+
"content": "<|reserved_special_token_11|>",
|
| 133 |
+
"lstrip": false,
|
| 134 |
+
"normalized": false,
|
| 135 |
+
"rstrip": false,
|
| 136 |
+
"single_word": false,
|
| 137 |
+
"special": true
|
| 138 |
+
},
|
| 139 |
+
"128017": {
|
| 140 |
+
"content": "<|reserved_special_token_12|>",
|
| 141 |
+
"lstrip": false,
|
| 142 |
+
"normalized": false,
|
| 143 |
+
"rstrip": false,
|
| 144 |
+
"single_word": false,
|
| 145 |
+
"special": true
|
| 146 |
+
},
|
| 147 |
+
"128018": {
|
| 148 |
+
"content": "<|reserved_special_token_13|>",
|
| 149 |
+
"lstrip": false,
|
| 150 |
+
"normalized": false,
|
| 151 |
+
"rstrip": false,
|
| 152 |
+
"single_word": false,
|
| 153 |
+
"special": true
|
| 154 |
+
},
|
| 155 |
+
"128019": {
|
| 156 |
+
"content": "<|reserved_special_token_14|>",
|
| 157 |
+
"lstrip": false,
|
| 158 |
+
"normalized": false,
|
| 159 |
+
"rstrip": false,
|
| 160 |
+
"single_word": false,
|
| 161 |
+
"special": true
|
| 162 |
+
},
|
| 163 |
+
"128020": {
|
| 164 |
+
"content": "<|reserved_special_token_15|>",
|
| 165 |
+
"lstrip": false,
|
| 166 |
+
"normalized": false,
|
| 167 |
+
"rstrip": false,
|
| 168 |
+
"single_word": false,
|
| 169 |
+
"special": true
|
| 170 |
+
},
|
| 171 |
+
"128021": {
|
| 172 |
+
"content": "<|reserved_special_token_16|>",
|
| 173 |
+
"lstrip": false,
|
| 174 |
+
"normalized": false,
|
| 175 |
+
"rstrip": false,
|
| 176 |
+
"single_word": false,
|
| 177 |
+
"special": true
|
| 178 |
+
},
|
| 179 |
+
"128022": {
|
| 180 |
+
"content": "<|reserved_special_token_17|>",
|
| 181 |
+
"lstrip": false,
|
| 182 |
+
"normalized": false,
|
| 183 |
+
"rstrip": false,
|
| 184 |
+
"single_word": false,
|
| 185 |
+
"special": true
|
| 186 |
+
},
|
| 187 |
+
"128023": {
|
| 188 |
+
"content": "<|reserved_special_token_18|>",
|
| 189 |
+
"lstrip": false,
|
| 190 |
+
"normalized": false,
|
| 191 |
+
"rstrip": false,
|
| 192 |
+
"single_word": false,
|
| 193 |
+
"special": true
|
| 194 |
+
},
|
| 195 |
+
"128024": {
|
| 196 |
+
"content": "<|reserved_special_token_19|>",
|
| 197 |
+
"lstrip": false,
|
| 198 |
+
"normalized": false,
|
| 199 |
+
"rstrip": false,
|
| 200 |
+
"single_word": false,
|
| 201 |
+
"special": true
|
| 202 |
+
},
|
| 203 |
+
"128025": {
|
| 204 |
+
"content": "<|reserved_special_token_20|>",
|
| 205 |
+
"lstrip": false,
|
| 206 |
+
"normalized": false,
|
| 207 |
+
"rstrip": false,
|
| 208 |
+
"single_word": false,
|
| 209 |
+
"special": true
|
| 210 |
+
},
|
| 211 |
+
"128026": {
|
| 212 |
+
"content": "<|reserved_special_token_21|>",
|
| 213 |
+
"lstrip": false,
|
| 214 |
+
"normalized": false,
|
| 215 |
+
"rstrip": false,
|
| 216 |
+
"single_word": false,
|
| 217 |
+
"special": true
|
| 218 |
+
},
|
| 219 |
+
"128027": {
|
| 220 |
+
"content": "<|reserved_special_token_22|>",
|
| 221 |
+
"lstrip": false,
|
| 222 |
+
"normalized": false,
|
| 223 |
+
"rstrip": false,
|
| 224 |
+
"single_word": false,
|
| 225 |
+
"special": true
|
| 226 |
+
},
|
| 227 |
+
"128028": {
|
| 228 |
+
"content": "<|reserved_special_token_23|>",
|
| 229 |
+
"lstrip": false,
|
| 230 |
+
"normalized": false,
|
| 231 |
+
"rstrip": false,
|
| 232 |
+
"single_word": false,
|
| 233 |
+
"special": true
|
| 234 |
+
},
|
| 235 |
+
"128029": {
|
| 236 |
+
"content": "<|reserved_special_token_24|>",
|
| 237 |
+
"lstrip": false,
|
| 238 |
+
"normalized": false,
|
| 239 |
+
"rstrip": false,
|
| 240 |
+
"single_word": false,
|
| 241 |
+
"special": true
|
| 242 |
+
},
|
| 243 |
+
"128030": {
|
| 244 |
+
"content": "<|reserved_special_token_25|>",
|
| 245 |
+
"lstrip": false,
|
| 246 |
+
"normalized": false,
|
| 247 |
+
"rstrip": false,
|
| 248 |
+
"single_word": false,
|
| 249 |
+
"special": true
|
| 250 |
+
},
|
| 251 |
+
"128031": {
|
| 252 |
+
"content": "<|reserved_special_token_26|>",
|
| 253 |
+
"lstrip": false,
|
| 254 |
+
"normalized": false,
|
| 255 |
+
"rstrip": false,
|
| 256 |
+
"single_word": false,
|
| 257 |
+
"special": true
|
| 258 |
+
},
|
| 259 |
+
"128032": {
|
| 260 |
+
"content": "<|reserved_special_token_27|>",
|
| 261 |
+
"lstrip": false,
|
| 262 |
+
"normalized": false,
|
| 263 |
+
"rstrip": false,
|
| 264 |
+
"single_word": false,
|
| 265 |
+
"special": true
|
| 266 |
+
},
|
| 267 |
+
"128033": {
|
| 268 |
+
"content": "<|reserved_special_token_28|>",
|
| 269 |
+
"lstrip": false,
|
| 270 |
+
"normalized": false,
|
| 271 |
+
"rstrip": false,
|
| 272 |
+
"single_word": false,
|
| 273 |
+
"special": true
|
| 274 |
+
},
|
| 275 |
+
"128034": {
|
| 276 |
+
"content": "<|reserved_special_token_29|>",
|
| 277 |
+
"lstrip": false,
|
| 278 |
+
"normalized": false,
|
| 279 |
+
"rstrip": false,
|
| 280 |
+
"single_word": false,
|
| 281 |
+
"special": true
|
| 282 |
+
},
|
| 283 |
+
"128035": {
|
| 284 |
+
"content": "<|reserved_special_token_30|>",
|
| 285 |
+
"lstrip": false,
|
| 286 |
+
"normalized": false,
|
| 287 |
+
"rstrip": false,
|
| 288 |
+
"single_word": false,
|
| 289 |
+
"special": true
|
| 290 |
+
},
|
| 291 |
+
"128036": {
|
| 292 |
+
"content": "<|reserved_special_token_31|>",
|
| 293 |
+
"lstrip": false,
|
| 294 |
+
"normalized": false,
|
| 295 |
+
"rstrip": false,
|
| 296 |
+
"single_word": false,
|
| 297 |
+
"special": true
|
| 298 |
+
},
|
| 299 |
+
"128037": {
|
| 300 |
+
"content": "<|reserved_special_token_32|>",
|
| 301 |
+
"lstrip": false,
|
| 302 |
+
"normalized": false,
|
| 303 |
+
"rstrip": false,
|
| 304 |
+
"single_word": false,
|
| 305 |
+
"special": true
|
| 306 |
+
},
|
| 307 |
+
"128038": {
|
| 308 |
+
"content": "<|reserved_special_token_33|>",
|
| 309 |
+
"lstrip": false,
|
| 310 |
+
"normalized": false,
|
| 311 |
+
"rstrip": false,
|
| 312 |
+
"single_word": false,
|
| 313 |
+
"special": true
|
| 314 |
+
},
|
| 315 |
+
"128039": {
|
| 316 |
+
"content": "<|reserved_special_token_34|>",
|
| 317 |
+
"lstrip": false,
|
| 318 |
+
"normalized": false,
|
| 319 |
+
"rstrip": false,
|
| 320 |
+
"single_word": false,
|
| 321 |
+
"special": true
|
| 322 |
+
},
|
| 323 |
+
"128040": {
|
| 324 |
+
"content": "<|reserved_special_token_35|>",
|
| 325 |
+
"lstrip": false,
|
| 326 |
+
"normalized": false,
|
| 327 |
+
"rstrip": false,
|
| 328 |
+
"single_word": false,
|
| 329 |
+
"special": true
|
| 330 |
+
},
|
| 331 |
+
"128041": {
|
| 332 |
+
"content": "<|reserved_special_token_36|>",
|
| 333 |
+
"lstrip": false,
|
| 334 |
+
"normalized": false,
|
| 335 |
+
"rstrip": false,
|
| 336 |
+
"single_word": false,
|
| 337 |
+
"special": true
|
| 338 |
+
},
|
| 339 |
+
"128042": {
|
| 340 |
+
"content": "<|reserved_special_token_37|>",
|
| 341 |
+
"lstrip": false,
|
| 342 |
+
"normalized": false,
|
| 343 |
+
"rstrip": false,
|
| 344 |
+
"single_word": false,
|
| 345 |
+
"special": true
|
| 346 |
+
},
|
| 347 |
+
"128043": {
|
| 348 |
+
"content": "<|reserved_special_token_38|>",
|
| 349 |
+
"lstrip": false,
|
| 350 |
+
"normalized": false,
|
| 351 |
+
"rstrip": false,
|
| 352 |
+
"single_word": false,
|
| 353 |
+
"special": true
|
| 354 |
+
},
|
| 355 |
+
"128044": {
|
| 356 |
+
"content": "<|reserved_special_token_39|>",
|
| 357 |
+
"lstrip": false,
|
| 358 |
+
"normalized": false,
|
| 359 |
+
"rstrip": false,
|
| 360 |
+
"single_word": false,
|
| 361 |
+
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| 1593 |
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| 1594 |
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| 1596 |
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| 1597 |
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|
| 1604 |
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| 1605 |
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| 1609 |
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| 1610 |
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| 1612 |
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| 1613 |
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|
| 1620 |
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| 1621 |
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| 1629 |
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| 1818 |
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| 1820 |
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| 1821 |
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| 1828 |
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| 1829 |
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|
| 1830 |
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| 1833 |
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| 1834 |
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| 1836 |
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| 1837 |
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| 1840 |
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| 1841 |
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| 1842 |
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|
| 1844 |
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| 1845 |
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| 1848 |
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| 1849 |
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|
| 1850 |
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|
| 1851 |
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|
| 1852 |
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|
| 1853 |
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| 1854 |
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| 1857 |
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|
| 1858 |
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|
| 1859 |
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|
| 1860 |
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|
| 1861 |
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|
| 1862 |
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|
| 1863 |
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| 1864 |
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|
| 1865 |
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|
| 1866 |
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|
| 1867 |
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|
| 1868 |
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|
| 1869 |
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|
| 1870 |
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|
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| 1873 |
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|
| 1874 |
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| 1875 |
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|
| 1876 |
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|
| 1877 |
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| 1878 |
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| 1881 |
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|
| 1882 |
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|
| 1883 |
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|
| 1884 |
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|
| 1885 |
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|
| 1886 |
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|
| 1887 |
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| 1888 |
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|
| 1889 |
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|
| 1890 |
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|
| 1891 |
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|
| 1892 |
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|
| 1893 |
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|
| 1894 |
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|
| 1895 |
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| 1896 |
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|
| 1897 |
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|
| 1898 |
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|
| 1899 |
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|
| 1900 |
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|
| 1901 |
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| 1902 |
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|
| 1903 |
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| 1904 |
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| 1905 |
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|
| 1906 |
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|
| 1907 |
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|
| 1908 |
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|
| 1909 |
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|
| 1910 |
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|
| 1911 |
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| 1912 |
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|
| 1913 |
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|
| 1914 |
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|
| 1915 |
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|
| 1916 |
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|
| 1917 |
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| 1918 |
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|
| 1919 |
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| 1920 |
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| 1921 |
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|
| 1922 |
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|
| 1923 |
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|
| 1924 |
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|
| 1925 |
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|
| 1926 |
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|
| 1927 |
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| 1928 |
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|
| 1929 |
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|
| 1930 |
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|
| 1931 |
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|
| 1932 |
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|
| 1933 |
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|
| 1934 |
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|
| 1935 |
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| 1936 |
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|
| 1937 |
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|
| 1938 |
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|
| 1939 |
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|
| 1940 |
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|
| 1941 |
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| 1942 |
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|
| 1943 |
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| 1944 |
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|
| 1945 |
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|
| 1946 |
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|
| 1947 |
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|
| 1948 |
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|
| 1949 |
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|
| 1950 |
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|
| 1951 |
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|
| 1952 |
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|
| 1953 |
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|
| 1954 |
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|
| 1955 |
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|
| 1956 |
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|
| 1957 |
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|
| 1958 |
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|
| 1959 |
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|
| 1960 |
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|
| 1961 |
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|
| 1962 |
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|
| 1963 |
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|
| 1964 |
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|
| 1965 |
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|
| 1966 |
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|
| 1967 |
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|
| 1968 |
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|
| 1969 |
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|
| 1970 |
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|
| 1971 |
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|
| 1972 |
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|
| 1973 |
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|
| 1974 |
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|
| 1975 |
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| 1976 |
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|
| 1977 |
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|
| 1978 |
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|
| 1979 |
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|
| 1980 |
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|
| 1981 |
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|
| 1982 |
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|
| 1983 |
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|
| 1984 |
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|
| 1985 |
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|
| 1986 |
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|
| 1987 |
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|
| 1988 |
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"content": "<|reserved_special_token_243|>",
|
| 1989 |
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|
| 1990 |
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|
| 1991 |
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|
| 1992 |
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|
| 1993 |
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|
| 1994 |
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|
| 1995 |
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|
| 1996 |
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|
| 1997 |
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|
| 1998 |
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|
| 1999 |
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|
| 2000 |
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|
| 2001 |
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|
| 2002 |
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|
| 2003 |
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|
| 2004 |
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"content": "<|reserved_special_token_245|>",
|
| 2005 |
+
"lstrip": false,
|
| 2006 |
+
"normalized": false,
|
| 2007 |
+
"rstrip": false,
|
| 2008 |
+
"single_word": false,
|
| 2009 |
+
"special": true
|
| 2010 |
+
},
|
| 2011 |
+
"128251": {
|
| 2012 |
+
"content": "<|reserved_special_token_246|>",
|
| 2013 |
+
"lstrip": false,
|
| 2014 |
+
"normalized": false,
|
| 2015 |
+
"rstrip": false,
|
| 2016 |
+
"single_word": false,
|
| 2017 |
+
"special": true
|
| 2018 |
+
},
|
| 2019 |
+
"128252": {
|
| 2020 |
+
"content": "<|reserved_special_token_247|>",
|
| 2021 |
+
"lstrip": false,
|
| 2022 |
+
"normalized": false,
|
| 2023 |
+
"rstrip": false,
|
| 2024 |
+
"single_word": false,
|
| 2025 |
+
"special": true
|
| 2026 |
+
},
|
| 2027 |
+
"128253": {
|
| 2028 |
+
"content": "<|reserved_special_token_248|>",
|
| 2029 |
+
"lstrip": false,
|
| 2030 |
+
"normalized": false,
|
| 2031 |
+
"rstrip": false,
|
| 2032 |
+
"single_word": false,
|
| 2033 |
+
"special": true
|
| 2034 |
+
},
|
| 2035 |
+
"128254": {
|
| 2036 |
+
"content": "<|reserved_special_token_249|>",
|
| 2037 |
+
"lstrip": false,
|
| 2038 |
+
"normalized": false,
|
| 2039 |
+
"rstrip": false,
|
| 2040 |
+
"single_word": false,
|
| 2041 |
+
"special": true
|
| 2042 |
+
},
|
| 2043 |
+
"128255": {
|
| 2044 |
+
"content": "<|reserved_special_token_250|>",
|
| 2045 |
+
"lstrip": false,
|
| 2046 |
+
"normalized": false,
|
| 2047 |
+
"rstrip": false,
|
| 2048 |
+
"single_word": false,
|
| 2049 |
+
"special": true
|
| 2050 |
+
},
|
| 2051 |
+
"128256": {
|
| 2052 |
+
"content": "[PAD]",
|
| 2053 |
+
"lstrip": false,
|
| 2054 |
+
"normalized": false,
|
| 2055 |
+
"rstrip": false,
|
| 2056 |
+
"single_word": false,
|
| 2057 |
+
"special": true
|
| 2058 |
+
}
|
| 2059 |
+
},
|
| 2060 |
+
"bos_token": "<|begin_of_text|>",
|
| 2061 |
+
"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 = content %}{% endif %}{{ content }}{% endfor %}",
|
| 2062 |
+
"clean_up_tokenization_spaces": true,
|
| 2063 |
+
"eos_token": "<|end_of_text|>",
|
| 2064 |
+
"model_input_names": [
|
| 2065 |
+
"input_ids",
|
| 2066 |
+
"attention_mask"
|
| 2067 |
+
],
|
| 2068 |
+
"model_max_length": 4096,
|
| 2069 |
+
"pad_token": "[PAD]",
|
| 2070 |
+
"tokenizer_class": "PreTrainedTokenizerFast"
|
| 2071 |
+
}
|