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

Libraries

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 AutoModel
model = AutoModel.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
```

XBFDI commited on 17 days ago

Commit

3b6d28f

verified ·

1 Parent(s): 7bacf93

Update modeling_easyformer.py

Browse files

Files changed (1) hide show

modeling_easyformer.py +11 -2

modeling_easyformer.py CHANGED Viewed

@@ -1,7 +1,11 @@
 import math, torch, torch.nn as nn, torch.nn.functional as F
 from transformers import PreTrainedModel, GenerationMixin
-from .configuration_easyformer import EasyFormerConfig
 class RMSNorm(nn.Module):
     def __init__(self, d, eps=1e-5):
@@ -50,6 +54,11 @@ class EasyFormerPreTrainedModel(PreTrainedModel):
     supports_gradient_checkpointing = False
 class EasyFormerLMHeadModel(EasyFormerPreTrainedModel, GenerationMixin):
     def __init__(self, config):
         super().__init__(config)
         self.cfg = config

+# modeling_easyformer.py
 import math, torch, torch.nn as nn, torch.nn.functional as F
 from transformers import PreTrainedModel, GenerationMixin
+try:
+    from .configuration_easyformer import EasyFormerConfig
+except ImportError:
+    from configuration_easyformer import EasyFormerConfig
 class RMSNorm(nn.Module):
     def __init__(self, d, eps=1e-5):
     supports_gradient_checkpointing = False
 class EasyFormerLMHeadModel(EasyFormerPreTrainedModel, GenerationMixin):
+    config_class = EasyFormerConfig
+    base_model_prefix = "easyformer"
+    _tied_weights_keys = ["lm_head.weight"]
+    all_tied_weights_keys = ["lm_head.weight"]
     def __init__(self, config):
         super().__init__(config)
         self.cfg = config