Text Generation
Transformers
PyTorch
JAX
Russian
gpt2
PyTorch
Transformers
text-generation-inference
Instructions to use Gnider/model_old_working with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Gnider/model_old_working with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Gnider/model_old_working")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Gnider/model_old_working") model = AutoModelForCausalLM.from_pretrained("Gnider/model_old_working") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Gnider/model_old_working with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Gnider/model_old_working" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Gnider/model_old_working", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Gnider/model_old_working
- SGLang
How to use Gnider/model_old_working 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 "Gnider/model_old_working" \ --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": "Gnider/model_old_working", "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 "Gnider/model_old_working" \ --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": "Gnider/model_old_working", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Gnider/model_old_working with Docker Model Runner:
docker model run hf.co/Gnider/model_old_working
Upload 9 files
Browse files- README.md +32 -0
- config.json +35 -0
- flax_model.msgpack +3 -0
- gitattributes +9 -0
- merges.txt +0 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +31 -0
- tokenizer_config.json +58 -0
- vocab.json +0 -0
README.md
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---
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language:
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- ru
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tags:
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- PyTorch
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- Transformers
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thumbnail: "https://github.com/sberbank-ai/ru-gpts"
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---
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# rugpt3small\_based\_on\_gpt2
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The model architecture design, pretraining, and evaluation are documented in our preprint: [**A Family of Pretrained Transformer Language Models for Russian**](https://arxiv.org/abs/2309.10931).
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The model was pretrained with sequence length 1024 using transformers by the [SberDevices](https://sberdevices.ru/) team on 80B tokens around 3 epochs. After that, the model was finetuned with the context size of 2048.
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Total training time took around one week on 32 GPUs.
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# Authors
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+ NLP core team RnD [Telegram channel](https://t.me/nlpcoreteam):
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+ Dmitry Zmitrovich
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# Cite us
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```
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@misc{zmitrovich2023family,
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title={A Family of Pretrained Transformer Language Models for Russian},
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author={Dmitry Zmitrovich and Alexander Abramov and Andrey Kalmykov and Maria Tikhonova and Ekaterina Taktasheva and Danil Astafurov and Mark Baushenko and Artem Snegirev and Tatiana Shavrina and Sergey Markov and Vladislav Mikhailov and Alena Fenogenova},
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year={2023},
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eprint={2309.10931},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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config.json
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{
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"activation_function": "gelu_new",
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"architectures": [
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"GPT2LMHeadModel"
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],
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"attn_pdrop": 0.1,
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"bos_token_id": 1,
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"embd_pdrop": 0.1,
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"eos_token_id": 2,
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"gradient_checkpointing": false,
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"id2label": {
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"0": "LABEL_0"
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},
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"initializer_range": 0.02,
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"label2id": {
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"LABEL_0": 0
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},
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"layer_norm_epsilon": 1e-05,
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"model_type": "gpt2",
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"n_ctx": 2048,
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"n_embd": 768,
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"n_head": 12,
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"n_inner": null,
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"n_layer": 12,
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"n_positions": 2048,
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"pad_token_id": 0,
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"resid_pdrop": 0.1,
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"summary_activation": null,
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"summary_first_dropout": 0.1,
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"summary_proj_to_labels": true,
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"summary_type": "cls_index",
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"summary_use_proj": true,
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"use_cache": true,
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"vocab_size": 50264
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}
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flax_model.msgpack
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version https://git-lfs.github.com/spec/v1
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oid sha256:322182be41c143f2f8ecd8b9e76018d09ae5ca4c752b8858a257811bb4c5f5da
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size 500931352
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gitattributes
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*.bin.* filter=lfs diff=lfs merge=lfs -text
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*.lfs.* filter=lfs diff=lfs merge=lfs -text
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*.bin filter=lfs diff=lfs merge=lfs -text
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*.h5 filter=lfs diff=lfs merge=lfs -text
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*.tflite filter=lfs diff=lfs merge=lfs -text
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*.tar.gz filter=lfs diff=lfs merge=lfs -text
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*.ot filter=lfs diff=lfs merge=lfs -text
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*.onnx filter=lfs diff=lfs merge=lfs -text
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*.msgpack filter=lfs diff=lfs merge=lfs -text
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merges.txt
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:0d2a4a387b0430654bb1f6813436bd31f8e10a06e18ea0e5c41b33120b78458e
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size 551290714
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special_tokens_map.json
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{
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"bos_token": {
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"content": "<s>",
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"lstrip": false,
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"normalized": true,
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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": "</s>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false
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},
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"mask_token": "<mask>",
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"pad_token": {
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"content": "<pad>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false
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},
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"unk_token": {
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"content": "<unk>",
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"lstrip": false,
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"normalized": true,
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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_config.json
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{
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"add_bos_token": false,
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"add_prefix_space": false,
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"added_tokens_decoder": {
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"0": {
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"content": "<pad>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"1": {
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"content": "<s>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"2": {
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"content": "</s>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"3": {
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"content": "<unk>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"4": {
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"content": "<mask>",
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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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"special": true
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}
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},
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"bos_token": "<s>",
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"clean_up_tokenization_spaces": true,
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"eos_token": "</s>",
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"errors": "replace",
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"mask_token": "<mask>",
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"model_max_length": 2048,
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"pad_token": "<pad>",
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"padding_side": "left",
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"tokenizer_class": "GPT2Tokenizer",
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"truncation_side": "left",
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"trust_remote_code": false,
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"unk_token": "<unk>"
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}
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vocab.json
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