Text Generation
Transformers
Safetensors
PyTorch
Russian
transformer
fill-mask
chat
russian
easyformer
custom_code
conversational
Eval Results (legacy)
Instructions to use OpenRussianAI/andrey with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenRussianAI/andrey with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="OpenRussianAI/andrey", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelWithLMHead model = AutoModelWithLMHead.from_pretrained("OpenRussianAI/andrey", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use OpenRussianAI/andrey with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "OpenRussianAI/andrey" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpenRussianAI/andrey", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/OpenRussianAI/andrey
- SGLang
How to use OpenRussianAI/andrey 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 "OpenRussianAI/andrey" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpenRussianAI/andrey", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "OpenRussianAI/andrey" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpenRussianAI/andrey", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use OpenRussianAI/andrey with Docker Model Runner:
docker model run hf.co/OpenRussianAI/andrey
Download configuration_easyformer.py from OpenRussianAI/andrey: direct link, hf CLI and curl.
- Browser
- Download file 1.14 kB
-
https://huggingface.co/OpenRussianAI/andrey/resolve/main/configuration_easyformer.py
- Command line
-
hf download hf://OpenRussianAI/andrey/configuration_easyformer.py
-
curl -L -o configuration_easyformer.py https://huggingface.co/OpenRussianAI/andrey/resolve/main/configuration_easyformer.py
1.14 kB
| # configuration_easyformer.py | |
| from transformers import PretrainedConfig | |
| class EasyFormerConfig(PretrainedConfig): | |
| model_type = "transformer" | |
| def __init__( | |
| self, | |
| vocab_size=8000, | |
| d_model=192, | |
| n_layer=5, | |
| n_head=4, | |
| ctx=128, | |
| dropout=0.1, | |
| **kwargs, | |
| ): | |
| super().__init__(**kwargs) | |
| self.vocab_size = vocab_size | |
| self.d_model = d_model | |
| self.n_layer = n_layer | |
| self.n_head = n_head | |
| self.ctx = ctx | |
| self.dropout = dropout | |
| # --- HF-совместимые property --- | |
| def num_hidden_layers(self): | |
| return self.n_layer | |
| def hidden_size(self): | |
| return self.d_model | |
| def num_attention_heads(self): | |
| return self.n_head | |
| def intermediate_size(self): | |
| return 2 * self.d_model | |
| def max_position_embeddings(self): | |
| return self.ctx | |
| def num_key_value_heads(self): | |
| return self.n_head | |
| def head_dim(self): | |
| return self.d_model // max(1, self.n_head) |