WhoTookMyAmogusNickname
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Browse files- README.md +103 -1
- added_tokens.json +3 -0
- config.json +26 -0
- generation_config.json +8 -0
- pytorch_model-00001-of-00003.bin +3 -0
- pytorch_model-00002-of-00003.bin +3 -0
- pytorch_model-00003-of-00003.bin +3 -0
- pytorch_model.bin.index.json +410 -0
- special_tokens_map.json +24 -0
- tokenizer.model +3 -0
- tokenizer_config.json +34 -0
README.md
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---
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-
license:
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---
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---
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license: llama2
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---
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# NewHope: Harnessing 99% of GPT-4's Programming Capabilities
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We introduce NewHope, a fine-tuned chat model based on llama-2-13b, aiming to provide a strong coding capability. NewHope handle different languages including Python, C++, Java, JavaScript, Go, and more. Preliminary evaluation on HumanEval shows that **NewHope possesses 99% of GPT-4's programming capabilities**.
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**Contact**: SLAM (<ins>S</ins>UFE <ins>L</ins>arge <ins>A</ins>I <ins>M</ins>odel) is a research group at Shanghai University of Finance and Economics.
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cui.wanyun@sufe.edu.cn
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**TODO**: We will release more evaluatation results and training details later.
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# Evaluation Results
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We evaluated NewHope on [HumanEval](https://github.com/openai/human-eval) using the official evaluation script by OpenAI. We compared the Pass@1 metric of NewHope with other models. The results of other models are from PapersWithCode.
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| Model | Pass@1 |
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| ----- | ------ |
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| **GPT-4** | **67.0** |
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| **NewHope** | **66.5** |
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| PanGu-Coder2 15B | 61.6 |
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| WizardCoder 15B | 57.3 |
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| phi-1 1.3B | 50.6 |
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| GPT-3.5 | 48.1 |
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| phi-1-small | 45.0 |
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| PaLM-Coder | 36.0 |
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| CodeGeeX2-6B | 35.9 |
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# Model Weights
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We have open-sourced the model weights [NewHope](https://huggingface.co/SLAM-group/NewHope).
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We are uploading the model weights. The weights will be available in a few hours.
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# Usage
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To load the NewHope model using Transformers, use the following code:
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```
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import torch
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from transformers import LlamaTokenizer, LlamaForCausalLM
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base_model = "SLAM-group/NewHope"
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tokenizer = LlamaTokenizer.from_pretrained(base_model)
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model = LlamaForCausalLM.from_pretrained(base_model, torch_dtype=torch.float16, device_map="auto")
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# model.config.use_cache is default to `False`. For inference: `model.config.use_cache = True`
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```
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**Note:** At least Huggingface Transformers **4.31.0** is required to load this model!
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You can ask NewHope to generate code with instructions. We provide a simple example of how NewHope model generates code with the specific prompt:
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```
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# Suppose required tokenizer and model have already been loaded
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instruction = "Write a Python function to tell me what the date is today."
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prompt = f"<s> ### Instruction:\n{instruction}\n\n### Response:\n"
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inputs = tokenizer(prompt, add_special_tokens=False, return_tensors="pt").to("cuda")
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output = model.generate(**inputs, do_sample=True, top_p=0.9, max_new_tokens=2048)[0]
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decoded_output = tokenizer.decode(output, skip_special_tokens=True).split("### Response:\n")[-1].strip()
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print(decoded_output)
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```
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You can also interact with NewHope in a dialog manner with the following prompt:
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```
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<s> ### Instruction:\nQ1\n\n### Response:\nA1</s><s> ### Instruction:\nQ2\n\n### Response:\nA2</s>
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```
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# Evaluation
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### Local setup
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1. Install HumanEval for evaluation. [Details](https://github.com/openai/human-eval)
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2. Install dependencies
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```bash
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pip install -r requirements.txt
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```
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---
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For HumanEval, we use the following prompt:
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```
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example_input = 'def is_odd(number: int) -> bool:\n """ Check whether the given number is odd\n >>> is_odd(3)\n True\n >>> is_odd(6)\n False\n """\n'
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example_output = 'def is_odd(number: int) -> bool:\n """ Check whether the given number is odd\n >>> is_odd(3)\n True\n >>> is_odd(6)\n False\n """\n return number % 2 == 1'
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task_in_humaneval = "REPLACE `task_in_humaneval` WITH THE SPECIFIC TASK IN HUMANEVAL DATA"
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prompt = f"<s> ### Instruction:\nComplete the given function below:\n\n{example_input}\n\n### Response:\n{example_output}</s><s> ### Instruction:\nComplete the given function below:\n\n{task_in_human_eval}\n\n### Response:\n"
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```
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To reproduce the results on HumanEval, use the following script:
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```
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python complete.py --base_model SLAM-group/NewHope --output_dir output --n_gpu 8
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```
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The above script will generate `samples.jsonl` in `output_dir`, which can be directly evaluated by HumanEval. [Evaluation procedure](https://github.com/openai/human-eval). We conducted the experiment with `fp16` on 8xA800, 80GB GPUs, reaching `66.5%` on Pass@1 (v.s. GPT4 `67.0%`).
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# Citation
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```
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@misc{2023newhope,
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title={NewHope: Harnessing 99% of GPT-4's Programming Capabilities},
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author={Wanyun Cui and Qianle Wang},
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howpublished = https://github.com/SLAM-group/newhope,
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year={2023}
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}
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```
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added_tokens.json
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{
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"<pad>": 32000
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}
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config.json
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{
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"_name_or_path": "/data/wangqianle/llm/NewHope",
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"architectures": [
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"LlamaForCausalLM"
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],
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"bos_token_id": 1,
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"eos_token_id": 2,
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"hidden_act": "silu",
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"hidden_size": 5120,
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"initializer_range": 0.02,
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"intermediate_size": 13824,
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"max_position_embeddings": 2048,
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"model_type": "llama",
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"num_attention_heads": 40,
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"num_hidden_layers": 40,
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"num_key_value_heads": 40,
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"pad_token_id": 32000,
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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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"tie_word_embeddings": false,
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"torch_dtype": "float16",
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"transformers_version": "4.31.0",
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"use_cache": false,
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"vocab_size": 32001
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"pad_token_id": 32000,
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"top_p": 0.9,
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"transformers_version": "4.31.0"
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}
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pytorch_model-00001-of-00003.bin
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size 9948740782
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pytorch_model-00002-of-00003.bin
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version https://git-lfs.github.com/spec/v1
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pytorch_model-00003-of-00003.bin
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version https://git-lfs.github.com/spec/v1
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pytorch_model.bin.index.json
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|
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special_tokens_map.json
ADDED
@@ -0,0 +1,24 @@
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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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|
1 |
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{
|
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|
3 |
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|
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|
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|
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"single_word": false
|
8 |
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},
|
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"eos_token": {
|
10 |
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"content": "</s>",
|
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|
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|
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"rstrip": false,
|
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"single_word": false
|
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},
|
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"pad_token": "<pad>",
|
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"unk_token": {
|
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|
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|
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|
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"rstrip": false,
|
22 |
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"single_word": false
|
23 |
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}
|
24 |
+
}
|
tokenizer.model
ADDED
@@ -0,0 +1,3 @@
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|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
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3 |
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size 499723
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tokenizer_config.json
ADDED
@@ -0,0 +1,34 @@
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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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"__type": "AddedToken",
|
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|
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|
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|
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|
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"tokenizer_class": "LlamaTokenizer",
|
26 |
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"unk_token": {
|
27 |
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"__type": "AddedToken",
|
28 |
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|
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|
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|
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|
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|
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}
|
34 |
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