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
File size: 1,138 Bytes
ffe4b06 886bcaa ffe4b06 886bcaa ffe4b06 886bcaa ffe4b06 886bcaa ffe4b06 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | # 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 ---
@property
def num_hidden_layers(self):
return self.n_layer
@property
def hidden_size(self):
return self.d_model
@property
def num_attention_heads(self):
return self.n_head
@property
def intermediate_size(self):
return 2 * self.d_model
@property
def max_position_embeddings(self):
return self.ctx
@property
def num_key_value_heads(self):
return self.n_head
@property
def head_dim(self):
return self.d_model // max(1, self.n_head) |