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Browse files- AIRA_FineTuning.ipynb +0 -0
- Aira_emissions.csv +2 -0
- LICENSE +144 -0
- README.md +134 -0
- config.json +33 -0
- generation_config.json +7 -0
- model.safetensors +3 -0
- optimizer.pt +3 -0
- pytorch_model.bin +3 -0
- rng_state.pt +3 -0
- scheduler.pt +3 -0
- special_tokens_map.json +7 -0
- tokenizer.json +3 -0
- tokenizer_config.json +79 -0
- training_stats.parquet +3 -0
AIRA_FineTuning.ipynb
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Aira_emissions.csv
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timestamp,project_name,run_id,duration,emissions,emissions_rate,cpu_power,gpu_power,ram_power,cpu_energy,gpu_energy,ram_energy,energy_consumed,country_name,country_iso_code,region,cloud_provider,cloud_region,os,python_version,codecarbon_version,cpu_count,cpu_model,gpu_count,gpu_model,longitude,latitude,ram_total_size,tracking_mode,on_cloud,pue
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2023-10-10T11:16:42,Aira_emissions,c05b58c7-8167-438d-90ac-635e34170215,38939.95551657677,1.9979995464802223,5.1309754209392266e-05,42.5,300.8959520056844,31.305264472961426,0.45970698638723906,3.296581835596692,0.33850117442999367,4.094789996413927,Singapore,SGP,,,,Linux-5.15.120+-x86_64-with-glibc2.35,3.10.12,2.3.1,12,Intel(R) Xeon(R) CPU @ 2.20GHz,1,1 x NVIDIA A100-SXM4-40GB,103.8547,1.2929,83.48070526123047,machine,N,1.0
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LICENSE
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BigScience RAIL License v1.0
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dated May 19, 2022
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This is a license (the “License”) between you (“You”) and the participants of BigScience (“Licensor”). Whereas the Apache 2.0 license was applicable to resources used to develop the Model, the licensing conditions have been modified for the access and distribution of the Model. This has been done to further BigScience’s aims of promoting not just open-access to its artifacts, but also a responsible use of these artifacts. Therefore, this Responsible AI License (RAIL)[1] aims at having an open and permissive character while striving for responsible use of the Model.
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Section I: PREAMBLE
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BigScience is a collaborative open innovation project aimed at the responsible development and use of large multilingual datasets and Large Language Models (“LLM”), as well as, the documentation of best practices and tools stemming from this collaborative effort. Further, BigScience participants wish to promote collaboration and sharing of research artifacts - including the Model - for the benefit of society, pursuant to this License.
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BigScience believes in the intersection between open and responsible AI development, thus, this License aims to strike a balance between both in order to enable responsible open-science for large language models and future NLP techniques.
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12. If any provision of this License is held to be invalid, illegal or unenforceable, the remaining provisions shall be unaffected thereby and remain valid as if such provision had not been set forth herein.
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END OF TERMS AND CONDITIONS
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Attachment A
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Use Restrictions
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You agree not to use the Model or Derivatives of the Model:
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1. In any way that violates any applicable national, federal, state, local or international law or regulation;
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2. For the purpose of exploiting, harming or attempting to exploit or harm minors in any way;
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3. To generate or disseminate verifiably false information with the purpose of harming others;
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4. To generate or disseminate personal identifiable information that can be used to harm an individual;
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5. To generate or disseminate information or content, in any context (e.g. posts, articles, tweets, chatbots or other kinds of automated bots) without expressly and intelligibly disclaiming that the text is machine generated;
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6. To defame, disparage or otherwise harass others;
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7. To impersonate or attempt to impersonate others;
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8. For fully automated decision making that adversely impacts an individual’s legal rights or otherwise creates or modifies a binding, enforceable obligation;
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9. For any use intended to or which has the effect of discriminating against or harming individuals or groups based on online or offline social behavior or known or predicted personal or personality characteristics
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10. To exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm;
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11. For any use intended to or which has the effect of discriminating against individuals or groups based on legally protected characteristics or categories;
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12. To provide medical advice and medical results interpretation;
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13. To generate or disseminate information for the purpose to be used for administration of justice, law enforcement, immigration or asylum processes, such as predicting an individual will commit fraud/crime commitment (e.g. by text profiling, drawing causal relationships between assertions made in documents, indiscriminate and arbitrarily-targeted use).
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________________
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[1] https://arxiv.org/pdf/2011.03116.pdf
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README.md
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---
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license: bigscience-bloom-rail-1.0
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datasets:
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- nicholasKluge/instruct-aira-dataset
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language:
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- pt
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metrics:
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- accuracy
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library_name: transformers
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tags:
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- alignment
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- instruction tuned
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- text generation
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- conversation
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- assistant
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pipeline_tag: text-generation
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widget:
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- text: "<|startofinstruction|>Como você se chama?<|endofinstruction|>"
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example_title: Saudações
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- text: "<|startofinstruction|>Me explique o que é Aprendizagem de Máquina?<|endofinstruction|>"
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example_title: Aprendizagem de Máquina
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- text: "<|startofinstruction|>Você sabe alguma coisa sobre a Ética das Virtudes?<|endofinstruction|>"
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example_title: Ética
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- text: "<|startofinstruction|>Como eu posso fazer a minha namorada feliz?<|endofinstruction|>"
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example_title: Conselho
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inference:
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parameters:
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repetition_penalty: 1.2
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temperature: 0.2
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top_k: 30
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top_p: 0.3
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max_length: 100
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length_penalty: 0.3
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early_stopping: true
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co2_eq_emissions:
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emissions: 1.99
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source: CodeCarbon
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training_type: fine-tuning
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geographical_location: Singapore
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hardware_used: NVIDIA A100-SXM4-40GB
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---
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# Aira-2-portuguese-1B7
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`Aira-2-portuguese-1B7` is the second version of the Aira instruction-tuned series. Aira is an instruction-tuned GPT-style model based on [BLOOM](https://huggingface.co/bigscience/bloom-1b7). The model was trained with a dataset composed of prompts and completions generated synthetically by prompting already-tuned models (ChatGPT, Llama, Open-Assistant, etc).
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Check our gradio-demo in [Spaces](https://huggingface.co/spaces/nicholasKluge/Aira-Demo-Portuguese).
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## Details
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- **Size:** 1,722,005,504 parameters
|
51 |
+
- **Dataset:** [Instruct-Aira Dataset](https://huggingface.co/datasets/nicholasKluge/instruct-aira-dataset)
|
52 |
+
- **Language:** Portuguese
|
53 |
+
- **Number of Epochs:** 3
|
54 |
+
- **Batch size:** 4
|
55 |
+
- **Optimizer:** `torch.optim.AdamW` (warmup_steps = 1e2, learning_rate = 5e-4, epsilon = 1e-8)
|
56 |
+
- **GPU:** 1 NVIDIA A100-SXM4-40GB
|
57 |
+
- **Emissions:** 1.99 KgCO2 (Singapore)
|
58 |
+
- **Total Energy Consumption:** 4.09 kWh
|
59 |
+
|
60 |
+
This repository has the [notebook](AIRA_FineTuning.ipynb) used to train this model.
|
61 |
+
|
62 |
+
## Usage
|
63 |
+
|
64 |
+
Three special tokens are used to mark the user side of the interaction and the model's response:
|
65 |
+
|
66 |
+
`<|startofinstruction|>`O que é um modelo de linguagem?`<|endofinstruction|>`Um modelo de linguagem é uma distribuição de probabilidade sobre um vocabulário.`<|endofcompletion|>`
|
67 |
+
|
68 |
+
```python
|
69 |
+
from transformers import AutoModelForCausalLM, AutoTokenizer
|
70 |
+
import torch
|
71 |
+
|
72 |
+
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
73 |
+
|
74 |
+
tokenizer = AutoTokenizer.from_pretrained('nicholasKluge/Aira-2-portuguese-1B7')
|
75 |
+
aira = AutoModelForCausalLM.from_pretrained('nicholasKluge/Aira-2-portuguese-1B7')
|
76 |
+
|
77 |
+
aira.eval()
|
78 |
+
aira.to(device)
|
79 |
+
|
80 |
+
question = input("Enter your question: ")
|
81 |
+
|
82 |
+
inputs = tokenizer(tokenizer.bos_token + question + tokenizer.eos_token, return_tensors="pt").to(device)
|
83 |
+
|
84 |
+
responses = aira.generate(**inputs,
|
85 |
+
bos_token_id=tokenizer.bos_token_id,
|
86 |
+
pad_token_id=tokenizer.pad_token_id,
|
87 |
+
eos_token_id=tokenizer.eos_token_id,
|
88 |
+
do_sample=True,
|
89 |
+
top_k=50,
|
90 |
+
max_length=200,
|
91 |
+
top_p=0.95,
|
92 |
+
temperature=0.7,
|
93 |
+
num_return_sequences=2)
|
94 |
+
|
95 |
+
print(f"Question: 👤 {question}\n")
|
96 |
+
|
97 |
+
for i, response in enumerate(responses):
|
98 |
+
print(f'Response {i+1}: 🤖 {tokenizer.decode(response, skip_special_tokens=True).replace(question, "")}')
|
99 |
+
```
|
100 |
+
|
101 |
+
The model will output something like:
|
102 |
+
|
103 |
+
```markdown
|
104 |
+
>>> Question: 👤 Qual a capital da Alemanha?
|
105 |
+
|
106 |
+
>>>Response 1: 🤖 A capital da Alemanha é Berlim. É a maior cidade da Alemanha e serve como centro administrativo, cultural e político da Alemanha.
|
107 |
+
>>>Response 2: 🤖 A capital da Alemanha é Berlim. É a maior cidade da Alemanha e serve como centro administrativo, cultural e político da Alemanha.
|
108 |
+
```
|
109 |
+
|
110 |
+
## Limitations
|
111 |
+
|
112 |
+
🤥 Generative models can perpetuate the generation of pseudo-informative content, that is, false information that may appear truthful.
|
113 |
+
|
114 |
+
🤬 In certain types of tasks, generative models can produce harmful and discriminatory content inspired by historical stereotypes.
|
115 |
+
|
116 |
+
## Cite as 🤗
|
117 |
+
|
118 |
+
```latex
|
119 |
+
|
120 |
+
@misc{nicholas22aira,
|
121 |
+
doi = {10.5281/zenodo.6989727},
|
122 |
+
url = {https://huggingface.co/nicholasKluge/Aira-2-portuguese-560M},
|
123 |
+
author = {Nicholas Kluge Corrêa},
|
124 |
+
title = {Aira},
|
125 |
+
year = {2023},
|
126 |
+
publisher = {HuggingFace},
|
127 |
+
journal = {HuggingFace repository},
|
128 |
+
}
|
129 |
+
|
130 |
+
```
|
131 |
+
|
132 |
+
## License
|
133 |
+
|
134 |
+
The `Aira-2-portuguese-1B7` is licensed under the RAIL License since it is a model derived from BLOOM. See the [LICENSE](LICENSE) file for more details.
|
config.json
ADDED
@@ -0,0 +1,33 @@
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|
1 |
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{
|
2 |
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|
3 |
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|
4 |
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|
5 |
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"BloomForCausalLM"
|
6 |
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|
7 |
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|
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|
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|
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|
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|
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|
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|
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|
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|
16 |
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|
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|
18 |
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|
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|
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|
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|
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|
23 |
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|
24 |
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|
25 |
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"skip_bias_add": true,
|
26 |
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|
27 |
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|
28 |
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"torch_dtype": "float32",
|
29 |
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"transformers_version": "4.34.0",
|
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"unk_token_id": 0,
|
31 |
+
"use_cache": true,
|
32 |
+
"vocab_size": 250684
|
33 |
+
}
|
generation_config.json
ADDED
@@ -0,0 +1,7 @@
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|
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|
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|
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|
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|
5 |
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|
6 |
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"transformers_version": "4.34.0"
|
7 |
+
}
|
model.safetensors
ADDED
@@ -0,0 +1,3 @@
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|
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version https://git-lfs.github.com/spec/v1
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optimizer.pt
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pytorch_model.bin
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rng_state.pt
ADDED
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version https://git-lfs.github.com/spec/v1
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size 5809
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scheduler.pt
ADDED
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special_tokens_map.json
ADDED
@@ -0,0 +1,7 @@
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|
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|
|
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|
1 |
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{
|
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"bos_token": "<|startofinstruction|>",
|
3 |
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|
4 |
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|
5 |
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"sep_token": "<|endofinstruction|>",
|
6 |
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"unk_token": "<unk>"
|
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|
tokenizer.json
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@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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tokenizer_config.json
ADDED
@@ -0,0 +1,79 @@
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
35 |
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
79 |
+
}
|
training_stats.parquet
ADDED
@@ -0,0 +1,3 @@
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|
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