78234317ec06fb22643420de5cff70aeb5883ce5b7e94921195a68849335e953
Browse files- README.md +89 -0
- config.json +29 -0
- smash_config.json +31 -0
- special_tokens_map.json +16 -0
- tokenizer.json +0 -0
- tokenizer_config.json +2063 -0
README.md
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---
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thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
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base_model: failspy/Meta-Llama-3-8B-Instruct-abliterated-v3
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metrics:
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- memory_disk
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- memory_inference
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- inference_latency
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- inference_throughput
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- inference_CO2_emissions
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- inference_energy_consumption
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tags:
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- pruna-ai
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---
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<!-- header start -->
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<!-- 200823 -->
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<div style="width: auto; margin-left: auto; margin-right: auto">
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<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
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<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
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</a>
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</div>
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<!-- header end -->
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[![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI)
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[![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI)
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[![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
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[![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/rskEr4BZJx)
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# Simply make AI models cheaper, smaller, faster, and greener!
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- Give a thumbs up if you like this model!
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- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
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- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
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- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
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- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
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## Results
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![image info](./plots.png)
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**Frequently Asked Questions**
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- ***How does the compression work?*** The model is compressed with hqq.
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- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
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- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
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- ***What is the model format?*** We use safetensors.
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- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
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- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
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- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
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- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
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- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
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## Setup
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You can run the smashed model with these steps:
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0. Check requirements from the original repo failspy/Meta-Llama-3-8B-Instruct-abliterated-v3 installed. In particular, check python, cuda, and transformers versions.
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1. Make sure that you have installed quantization related packages.
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```bash
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pip install hqq
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```
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2. Load & run the model.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from hqq.engine.hf import HQQModelForCausalLM
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from hqq.models.hf.base import AutoHQQHFModel
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try:
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model = HQQModelForCausalLM.from_quantized("PrunaAI/failspy-Meta-Llama-3-8B-Instruct-abliterated-v3-HQQ-2bit-smashed", device_map='auto')
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except:
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model = AutoHQQHFModel.from_quantized("PrunaAI/failspy-Meta-Llama-3-8B-Instruct-abliterated-v3-HQQ-2bit-smashed")
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tokenizer = AutoTokenizer.from_pretrained("failspy/Meta-Llama-3-8B-Instruct-abliterated-v3")
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input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
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outputs = model.generate(input_ids, max_new_tokens=216)
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tokenizer.decode(outputs[0])
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```
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## Configurations
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The configuration info are in `smash_config.json`.
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## Credits & License
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The license of the smashed model follows the license of the original model. Please check the license of the original model failspy/Meta-Llama-3-8B-Instruct-abliterated-v3 before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
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## Want to compress other models?
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- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
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- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
|
config.json
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{
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"_name_or_path": "/tmp/tmplsel5r9a",
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 128000,
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"eos_token_id": 128001,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 8192,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"rope_theta": 500000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "float16",
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"transformers_version": "4.42.4",
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"use_cache": true,
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"vocab_size": 128256
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}
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smash_config.json
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{
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"api_key": null,
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"verify_url": "http://johnrachwan.pythonanywhere.com",
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"smash_config": {
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"pruners": "None",
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"pruning_ratio": 0.0,
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"factorizers": "None",
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"quantizers": "['hqq']",
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"weight_quantization_bits": 2,
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"output_deviation": 0.005,
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"compilers": "None",
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"static_batch": true,
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"static_shape": true,
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"controlnet": "None",
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"unet_dim": 4,
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"device": "cuda",
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"cache_dir": "/ceph/hdd/staff/charpent/.cache/modelsa5w894_h",
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"batch_size": 1,
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"model_name": "failspy/Meta-Llama-3-8B-Instruct-abliterated-v3",
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"task": "text_text_generation",
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"max_batch_size": 1,
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"qtype_weight": "torch.qint8",
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"qtype_activation": "torch.quint8",
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"qobserver": "<class 'torch.ao.quantization.observer.MinMaxObserver'>",
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"qscheme": "torch.per_tensor_symmetric",
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"qconfig": "x86",
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"group_size": 128,
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"damp_percent": 0.1,
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"save_load_fn": "hqq"
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}
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}
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special_tokens_map.json
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{
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"bos_token": {
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"content": "<|begin_of_text|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"eos_token": {
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"content": "<|eot_id|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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}
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}
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tokenizer.json
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The diff for this file is too large to render.
See raw diff
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tokenizer_config.json
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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 |
+
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|
46 |
+
"normalized": false,
|
47 |
+
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|
48 |
+
"single_word": false,
|
49 |
+
"special": true
|
50 |
+
},
|
51 |
+
"128006": {
|
52 |
+
"content": "<|start_header_id|>",
|
53 |
+
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|
54 |
+
"normalized": false,
|
55 |
+
"rstrip": false,
|
56 |
+
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|
57 |
+
"special": true
|
58 |
+
},
|
59 |
+
"128007": {
|
60 |
+
"content": "<|end_header_id|>",
|
61 |
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|
62 |
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|
63 |
+
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|
64 |
+
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|
65 |
+
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|
66 |
+
},
|
67 |
+
"128008": {
|
68 |
+
"content": "<|reserved_special_token_4|>",
|
69 |
+
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|
70 |
+
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|
71 |
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|
72 |
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|
73 |
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|
74 |
+
},
|
75 |
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"128009": {
|
76 |
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"content": "<|eot_id|>",
|
77 |
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|
78 |
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|
79 |
+
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|
80 |
+
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|
81 |
+
"special": true
|
82 |
+
},
|
83 |
+
"128010": {
|
84 |
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"content": "<|reserved_special_token_5|>",
|
85 |
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|
86 |
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|
87 |
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|
88 |
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|
89 |
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|
90 |
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},
|
91 |
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|
92 |
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|
93 |
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|
94 |
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|
95 |
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|
96 |
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|
97 |
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|
98 |
+
},
|
99 |
+
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|
100 |
+
"content": "<|reserved_special_token_7|>",
|
101 |
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|
102 |
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|
103 |
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|
104 |
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|
105 |
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|
106 |
+
},
|
107 |
+
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|
108 |
+
"content": "<|reserved_special_token_8|>",
|
109 |
+
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|
110 |
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|
111 |
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|
112 |
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|
113 |
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|
114 |
+
},
|
115 |
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|
116 |
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"content": "<|reserved_special_token_9|>",
|
117 |
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|
118 |
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|
119 |
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|
120 |
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|
121 |
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|
122 |
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|
123 |
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|
124 |
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|
125 |
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|
126 |
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|
127 |
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|
128 |
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|
129 |
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|
130 |
+
},
|
131 |
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|
132 |
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|
133 |
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|
134 |
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|
135 |
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|
136 |
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|
137 |
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|
138 |
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|
139 |
+
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|
140 |
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|
141 |
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|
142 |
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|
143 |
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|
144 |
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|
145 |
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|
146 |
+
},
|
147 |
+
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|
148 |
+
"content": "<|reserved_special_token_13|>",
|
149 |
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|
150 |
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|
151 |
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|
152 |
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|
153 |
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|
154 |
+
},
|
155 |
+
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|
156 |
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"content": "<|reserved_special_token_14|>",
|
157 |
+
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|
158 |
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|
159 |
+
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|
160 |
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|
161 |
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|
162 |
+
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|
163 |
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|
164 |
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|
165 |
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|
166 |
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|
167 |
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|
168 |
+
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|
169 |
+
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|
170 |
+
},
|
171 |
+
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|
172 |
+
"content": "<|reserved_special_token_16|>",
|
173 |
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|
174 |
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|
175 |
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|
176 |
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|
177 |
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|
178 |
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|
179 |
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|
180 |
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"content": "<|reserved_special_token_17|>",
|
181 |
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|
182 |
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|
183 |
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|
184 |
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|
185 |
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|
186 |
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|
187 |
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|
188 |
+
"content": "<|reserved_special_token_18|>",
|
189 |
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|
190 |
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|
191 |
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|
192 |
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|
193 |
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|
194 |
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},
|
195 |
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|
196 |
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"content": "<|reserved_special_token_19|>",
|
197 |
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|
198 |
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|
199 |
+
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|
200 |
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|
201 |
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|
202 |
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},
|
203 |
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|
204 |
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"content": "<|reserved_special_token_20|>",
|
205 |
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|
206 |
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|
207 |
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|
208 |
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|
209 |
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|
210 |
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},
|
211 |
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|
212 |
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"content": "<|reserved_special_token_21|>",
|
213 |
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|
214 |
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|
215 |
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|
216 |
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|
217 |
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|
218 |
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},
|
219 |
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|
220 |
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"content": "<|reserved_special_token_22|>",
|
221 |
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|
222 |
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|
223 |
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|
224 |
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|
225 |
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|
226 |
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},
|
227 |
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"128028": {
|
228 |
+
"content": "<|reserved_special_token_23|>",
|
229 |
+
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|
230 |
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|
231 |
+
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|
232 |
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|
233 |
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|
234 |
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},
|
235 |
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|
236 |
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"content": "<|reserved_special_token_24|>",
|
237 |
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|
238 |
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|
239 |
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|
240 |
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|
241 |
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|
242 |
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},
|
243 |
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|
244 |
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"content": "<|reserved_special_token_25|>",
|
245 |
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|
246 |
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|
247 |
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|
248 |
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|
249 |
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|
250 |
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|
251 |
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|
252 |
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|
253 |
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|
254 |
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|
255 |
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|
256 |
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|
257 |
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|
258 |
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|
259 |
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|
260 |
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|
261 |
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|
262 |
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|
263 |
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|
264 |
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|
265 |
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|
266 |
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|
267 |
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|
268 |
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|
269 |
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|
270 |
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|
271 |
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|
272 |
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|
273 |
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|
274 |
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|
275 |
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|
276 |
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|
277 |
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|
278 |
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|
279 |
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|
280 |
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|
281 |
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|
282 |
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|
283 |
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|
284 |
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|
285 |
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|
286 |
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|
287 |
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|
288 |
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|
289 |
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|
290 |
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|
291 |
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|
292 |
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|
293 |
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|
294 |
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|
295 |
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|
296 |
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|
297 |
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|
298 |
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|
299 |
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|
300 |
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|
301 |
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|
302 |
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|
303 |
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|
304 |
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|
305 |
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|
306 |
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|
307 |
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|
308 |
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|
309 |
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|
310 |
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|
311 |
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|
312 |
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|
313 |
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|
314 |
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|
315 |
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|
316 |
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|
317 |
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|
318 |
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|
319 |
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|
320 |
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|
321 |
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|
322 |
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|
323 |
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|
324 |
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|
325 |
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|
326 |
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|
327 |
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|
328 |
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|
329 |
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|
330 |
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|
331 |
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|
332 |
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|
333 |
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|
334 |
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|
335 |
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|
336 |
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|
337 |
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|
338 |
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|
339 |
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|
340 |
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|
341 |
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|
342 |
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|
343 |
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|
344 |
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|
345 |
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|
346 |
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|
347 |
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|
348 |
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|
349 |
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|
350 |
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|
351 |
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|
352 |
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|
353 |
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|
354 |
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|
355 |
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|
356 |
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|
357 |
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|
358 |
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|
359 |
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|
360 |
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|
361 |
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|
362 |
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|
363 |
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|
364 |
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|
365 |
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|
366 |
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367 |
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368 |
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}
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},
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2052 |
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"bos_token": "<|begin_of_text|>",
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2053 |
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"chat_template": "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% endif %}",
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2054 |
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"clean_up_tokenization_spaces": true,
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"model_input_names": [
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2058 |
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"input_ids",
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2059 |
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"attention_mask"
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],
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"model_max_length": 1000000000000000019884624838656,
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"tokenizer_class": "PreTrainedTokenizerFast"
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}
|