Instructions to use RollingMuffin/scripts_ru with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RollingMuffin/scripts_ru with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="RollingMuffin/scripts_ru")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("RollingMuffin/scripts_ru") model = AutoModelForCausalLM.from_pretrained("RollingMuffin/scripts_ru") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use RollingMuffin/scripts_ru with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "RollingMuffin/scripts_ru" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "RollingMuffin/scripts_ru", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/RollingMuffin/scripts_ru
- SGLang
How to use RollingMuffin/scripts_ru 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 "RollingMuffin/scripts_ru" \ --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": "RollingMuffin/scripts_ru", "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 "RollingMuffin/scripts_ru" \ --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": "RollingMuffin/scripts_ru", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use RollingMuffin/scripts_ru with Docker Model Runner:
docker model run hf.co/RollingMuffin/scripts_ru
Commit ·
0d8a7a6
1
Parent(s): 11bf78c
Commit from colab
Browse files- added_tokens.json +1 -0
- config.json +34 -0
- merges.txt +0 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +1 -0
- tokenizer.json +0 -0
- tokenizer_config.json +1 -0
- training_args.bin +3 -0
- vocab.json +0 -0
added_tokens.json
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{"<|endoftext|>": 50257}
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config.json
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{
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"_name_or_path": "sberbank-ai/rugpt3small_based_on_gpt2",
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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": 50256,
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"embd_pdrop": 0.1,
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"eos_token_id": 50256,
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"gradient_checkpointing": false,
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"initializer_range": 0.02,
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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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"reorder_and_upcast_attn": false,
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"resid_pdrop": 0.1,
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"scale_attn_by_inverse_layer_idx": false,
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"scale_attn_weights": true,
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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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"torch_dtype": "float32",
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"transformers_version": "4.16.2",
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"use_cache": true,
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"vocab_size": 50264
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}
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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 sha256:0c1f0d84ef1079701c83ff564124ad358c399a79e64e48787e14b67d35658b4d
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size 551319785
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special_tokens_map.json
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{"bos_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "eos_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "unk_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "pad_token": "<|endoftext|>"}
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tokenizer.json
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tokenizer_config.json
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{"unk_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "bos_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "eos_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "add_prefix_space": false, "special_tokens_map_file": null, "name_or_path": "sberbank-ai/rugpt3small_based_on_gpt2", "errors": "replace", "tokenizer_class": "GPT2Tokenizer"}
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:338449769623e92b438a7ed6bbc03986348358994b5a1d22545e14e260da2926
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size 2991
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vocab.json
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