Instructions to use alibaba-pai/pai-bloom-1b1-text2prompt-sd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alibaba-pai/pai-bloom-1b1-text2prompt-sd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="alibaba-pai/pai-bloom-1b1-text2prompt-sd")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("alibaba-pai/pai-bloom-1b1-text2prompt-sd") model = AutoModelForCausalLM.from_pretrained("alibaba-pai/pai-bloom-1b1-text2prompt-sd") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use alibaba-pai/pai-bloom-1b1-text2prompt-sd with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "alibaba-pai/pai-bloom-1b1-text2prompt-sd" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "alibaba-pai/pai-bloom-1b1-text2prompt-sd", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/alibaba-pai/pai-bloom-1b1-text2prompt-sd
- SGLang
How to use alibaba-pai/pai-bloom-1b1-text2prompt-sd with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "alibaba-pai/pai-bloom-1b1-text2prompt-sd" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "alibaba-pai/pai-bloom-1b1-text2prompt-sd", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "alibaba-pai/pai-bloom-1b1-text2prompt-sd" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "alibaba-pai/pai-bloom-1b1-text2prompt-sd", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use alibaba-pai/pai-bloom-1b1-text2prompt-sd with Docker Model Runner:
docker model run hf.co/alibaba-pai/pai-bloom-1b1-text2prompt-sd
Commit ·
736bdcb
1
Parent(s): ca9b1e9
add model
Browse files- .ipynb_checkpoints/README-checkpoint.md +65 -0
- README.md +62 -0
- config.json +32 -0
- generation_config.json +7 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +7 -0
- tokenizer.json +0 -0
- tokenizer_config.json +11 -0
.ipynb_checkpoints/README-checkpoint.md
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---
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license: apache-2.0
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tags:
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- pytorch
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- transformers
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- text-generation
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---
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# BeautifulPrompt
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## 简介 Brief Introduction
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我们开源了一个自动Prompt生成模型,您可以直接输入一个极其简单的Prompt,就可以得到经过语言模型优化过的Prompt,帮助您更简单地生成高颜值图像。
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We release an automatic Prompt generation model, you can directly enter an extremely simple Prompt and get a Prompt optimized by the language model to help you generate high value images more simply.
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* Github: [EasyNLP](https://github.com/alibaba/EasyNLP)
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## 使用 Usage
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained('alibaba-pai/pai-bloom-1b1-text2prompt-sd')
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model = AutoModelForCausalLM.from_pretrained('alibaba-pai/pai-bloom-1b1-text2prompt-sd').eval().cuda()
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raw_prompt = '1 girl'
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input = f'Instruction: Give a simple description of the image to generate a drawing prompt.\nInput: {raw_prompt}\nOutput:'
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input_ids = tokenizer.encode(input, return_tensors='pt').cuda()
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outputs = model.generate(
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input_ids,
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max_length=384,
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do_sample=True,
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temperature=1.0,
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top_k=50,
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top_p=0.95,
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repetition_penalty=1.2,
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num_return_sequences=5)
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prompts = tokenizer.batch_decode(outputs[:, input_ids.size(1):], skip_special_tokens=True)
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prompts = [p.strip() for p in prompts]
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print(prompts)
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```
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## 作品展示 Gallery
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| Original | BeautifulPrompt |
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| ---------------------------------------- | ---------------------------------- |
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| prompt: taylor swift, country, golden, fearless,wavehair | prompt: portrait of taylor swift as a beautiful woman, long hair, country, golden ratio, intricate, symmetrical, cinematic lighting, highly detailed, digital painting, artstation, concept art, smooth, sharp focus, illustration |
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|  |  |
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| Original | BeautifulPrompt |
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| ---------------------------------------- | ---------------------------------- |
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| prompt: A majestic sailing ship | prompt: a massive sailing ship, epic, cinematic, artstation, greg rutkowski, james gurney, sparth |
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|  |  |
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## 使用须知 Notice for Use
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使用上述模型需遵守[AIGC模型开源特别条款](https://terms.alicdn.com/legal-agreement/terms/common_platform_service/20230505180457947/20230505180457947.html)。
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If you want to use this model, please read this [document](https://terms.alicdn.com/legal-agreement/terms/common_platform_service/20230505180457947/20230505180457947.html) carefully and abide by the terms.
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README.md
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---
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license: apache-2.0
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---
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---
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license: apache-2.0
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tags:
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- pytorch
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- transformers
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- text-generation
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| 7 |
---
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| 8 |
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| 9 |
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# BeautifulPrompt
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| 10 |
+
|
| 11 |
+
## 简介 Brief Introduction
|
| 12 |
+
|
| 13 |
+
我们开源了一个自动Prompt生成模型,您可以直接输入一个极其简单的Prompt,就可以得到经过语言模型优化过的Prompt,帮助您更简单地生成高颜值图像。
|
| 14 |
+
|
| 15 |
+
We release an automatic Prompt generation model, you can directly enter an extremely simple Prompt and get a Prompt optimized by the language model to help you generate high value images more simply.
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+
|
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* Github: [EasyNLP](https://github.com/alibaba/EasyNLP)
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## 使用 Usage
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained('alibaba-pai/pai-bloom-1b1-text2prompt-sd')
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model = AutoModelForCausalLM.from_pretrained('alibaba-pai/pai-bloom-1b1-text2prompt-sd').eval().cuda()
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raw_prompt = '1 girl'
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input = f'Instruction: Give a simple description of the image to generate a drawing prompt.\nInput: {raw_prompt}\nOutput:'
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input_ids = tokenizer.encode(input, return_tensors='pt').cuda()
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outputs = model.generate(
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input_ids,
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max_length=384,
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do_sample=True,
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temperature=1.0,
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top_k=50,
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top_p=0.95,
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repetition_penalty=1.2,
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num_return_sequences=5)
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prompts = tokenizer.batch_decode(outputs[:, input_ids.size(1):], skip_special_tokens=True)
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prompts = [p.strip() for p in prompts]
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print(prompts)
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```
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## 作品展示 Gallery
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| 47 |
+
|
| 48 |
+
| Original | BeautifulPrompt |
|
| 49 |
+
| ---------------------------------------- | ---------------------------------- |
|
| 50 |
+
| prompt: taylor swift, country, golden, fearless,wavehair | prompt: portrait of taylor swift as a beautiful woman, long hair, country, golden ratio, intricate, symmetrical, cinematic lighting, highly detailed, digital painting, artstation, concept art, smooth, sharp focus, illustration |
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+
|  |  |
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+
|
| 53 |
+
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| Original | BeautifulPrompt |
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| 55 |
+
| ---------------------------------------- | ---------------------------------- |
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+
| prompt: A majestic sailing ship | prompt: a massive sailing ship, epic, cinematic, artstation, greg rutkowski, james gurney, sparth |
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| 57 |
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|  |  |
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+
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| 60 |
+
|
| 61 |
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## 使用须知 Notice for Use
|
| 62 |
+
|
| 63 |
+
使用上述模型需遵守[AIGC模型开源特别条款](https://terms.alicdn.com/legal-agreement/terms/common_platform_service/20230505180457947/20230505180457947.html)。
|
| 64 |
+
|
| 65 |
+
If you want to use this model, please read this [document](https://terms.alicdn.com/legal-agreement/terms/common_platform_service/20230505180457947/20230505180457947.html) carefully and abide by the terms.
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config.json
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{
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"_name_or_path": "alibaba-pai/pai-bloom-1b1-text2prompt-sd",
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"apply_residual_connection_post_layernorm": false,
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"architectures": [
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"BloomForCausalLM"
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],
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"attention_dropout": 0.0,
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"attention_softmax_in_fp32": true,
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"bias_dropout_fusion": true,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"hidden_dropout": 0.0,
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"hidden_size": 1536,
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"initializer_range": 0.02,
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"layer_norm_epsilon": 1e-05,
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"masked_softmax_fusion": true,
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"model_type": "bloom",
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"n_head": 16,
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"n_inner": null,
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"n_layer": 24,
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"offset_alibi": 100,
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"pad_token_id": 3,
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"pretraining_tp": 1,
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"skip_bias_add": true,
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"skip_bias_add_qkv": false,
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"slow_but_exact": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.29.2",
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"unk_token_id": 0,
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"use_cache": true,
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"vocab_size": 250880
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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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| 5 |
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"pad_token_id": 3,
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"transformers_version": "4.29.2"
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:6c63faf4cb0a99a8cac144fb3faaeabb0c053b33d09c9e2d5ebee36440b8d506
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size 2140165151
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special_tokens_map.json
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{
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"bos_token": "<s>",
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"eos_token": "</s>",
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"pad_token": "</s>",
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"sep_token": "<sep>",
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"unk_token": "<unk>"
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}
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tokenizer.json
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tokenizer_config.json
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{
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"add_prefix_space": false,
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"bos_token": "<s>",
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"clean_up_tokenization_spaces": false,
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"eos_token": "</s>",
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "<pad>",
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"padding_side": "left",
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"tokenizer_class": "BloomTokenizer",
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"unk_token": "<unk>"
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}
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