Instructions to use AlexWortega/instruct_rugptMedium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AlexWortega/instruct_rugptMedium with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="AlexWortega/instruct_rugptMedium")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AlexWortega/instruct_rugptMedium")
model = AutoModelForCausalLM.from_pretrained("AlexWortega/instruct_rugptMedium")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use AlexWortega/instruct_rugptMedium with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AlexWortega/instruct_rugptMedium"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AlexWortega/instruct_rugptMedium",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AlexWortega/instruct_rugptMedium

SGLang

How to use AlexWortega/instruct_rugptMedium 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 "AlexWortega/instruct_rugptMedium" \
    --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": "AlexWortega/instruct_rugptMedium",
		"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 "AlexWortega/instruct_rugptMedium" \
        --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": "AlexWortega/instruct_rugptMedium",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AlexWortega/instruct_rugptMedium with Docker Model Runner:
```
docker model run hf.co/AlexWortega/instruct_rugptMedium
```

Instructions ruGPT Medium v0.11_75к_a

Model Summary

Это ruGPTMedium дообученная в инструктивно-флановом сетапе, она более ли менее ZSшотиться и FSшотиться и работает лучше чем XGLM1.7b, mgpt на русском языке

Quick Start

from transformers import pipeline
#в душе не ебу будет ли норм работать, ставлю жопу автора хф что токенайзер мисматчнет с моделью, вообще грузите по нормальному
pipe = pipeline(model='AlexWortega/instruct_rugptMedium')
pipe('''Как собрать питон код?''')

from transformers import GPT2TokenizerFast,GPT2LMHeadModel
tokenizer = GPT2TokenizerFast.from_pretrained("AlexWortega/instruct_rugptMedium")
special_tokens_dict = {'additional_special_tokens': ['<code>', '</code>', '<instructionS>', '<instructionE>', '<next>']}

tokenizer.add_special_tokens(special_tokens_dict)
device = 'cuda:1'
model = GPT2LMHeadModel.from_pretrained("AlexWortega/instruct_rugptMedium")
model.to(device)

model.resize_token_embeddings(len(tokenizer))

обратите внимание, что лучшие параметры для генерации

gen_kwargs = {
        "min_length": 20,
        "max_new_tokens": 100,
        "top_k": 50,
        "top_p": 0.9,
        "do_sample": True,  
        "early_stopping": True,
        "no_repeat_ngram_size": 2,
        "eos_token_id": tokenizer.eos_token_id,
        "pad_token_id": tokenizer.eos_token_id,
        "use_cache": True,
        "repetition_penalty": 1.5,  
        "length_penalty": 0.8,  
        "num_beams": 4,
        "num_return_sequences": k
    }

License

The weights of Instructions ruGPT Small v0.1a are licensed under version 2.0 of the Apache License.

Hyperparameters

I used Novograd with a learning rate of 2e-5 and global batch size of 6 (3 for each data parallel worker). I use both data parallelism and pipeline parallelism to conduct training. During training, we truncate the input sequence to 1024 tokens, and for input sequence that contains less than 1024 tokens, we concatenate multiple sequences into one long sequence to improve the data efficiency.

References

#Metrics

SOON

BibTeX entry and citation info

@article{
  title={GPT2xl is underrated task solver},
  author={Nickolich Aleksandr, 5Q, datascience, Ilya Gusev, Alex Kukushkin, Karina Romanova, Arseniy Shahmatov, Maksim Gersimenko},
  year={2023}
}

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