Instructions to use Vikhrmodels/Vikhr-Nemo-12B-Instruct-R-21-09-24 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Vikhrmodels/Vikhr-Nemo-12B-Instruct-R-21-09-24 with Transformers:

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

pipe = pipeline("text-generation", model="Vikhrmodels/Vikhr-Nemo-12B-Instruct-R-21-09-24")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Vikhrmodels/Vikhr-Nemo-12B-Instruct-R-21-09-24")
model = AutoModelForCausalLM.from_pretrained("Vikhrmodels/Vikhr-Nemo-12B-Instruct-R-21-09-24", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Vikhrmodels/Vikhr-Nemo-12B-Instruct-R-21-09-24 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Vikhrmodels/Vikhr-Nemo-12B-Instruct-R-21-09-24"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Vikhrmodels/Vikhr-Nemo-12B-Instruct-R-21-09-24",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Vikhrmodels/Vikhr-Nemo-12B-Instruct-R-21-09-24

SGLang

How to use Vikhrmodels/Vikhr-Nemo-12B-Instruct-R-21-09-24 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 "Vikhrmodels/Vikhr-Nemo-12B-Instruct-R-21-09-24" \
    --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": "Vikhrmodels/Vikhr-Nemo-12B-Instruct-R-21-09-24",
		"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 "Vikhrmodels/Vikhr-Nemo-12B-Instruct-R-21-09-24" \
        --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": "Vikhrmodels/Vikhr-Nemo-12B-Instruct-R-21-09-24",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Vikhrmodels/Vikhr-Nemo-12B-Instruct-R-21-09-24 with Docker Model Runner:
```
docker model run hf.co/Vikhrmodels/Vikhr-Nemo-12B-Instruct-R-21-09-24
```

AlexWortega commited on Oct 25, 2024

Commit

7499757

verified ·

1 Parent(s): b71ce57

Update README.md

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README.md +4 -1

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@@ -38,11 +38,14 @@ library_name: transformers
 Здесь приведена лишь часть лидерборда, подробнее смотрите в репозитории бенчмарка.
 | Model Name                                       | Winrate  | 95% CI             | Average # Tokens |
 |--------------------------------------------------|--------|--------------------|------------------|
 | gpt-4-1106-preview                               | 90.9   | (-1.3, 1.0)        | 541              |
 | gpt-4o-mini                                      | 83.9   | (-1.8, 1.1)        | 448              |
-| **vikhr-nemo-12b-instruct-r-21-09-24**               | **79.8**   | (-2.2, 1.9)        | **627**              |
 | gemma-2-9b-it-sppo-iter3                         | 73.6   | (-1.6, 2.2)        | 509              |
 | gemma-2-9b-it                                    | 69.2   | (-2.5, 1.9)        | 459              |
 | t-lite-instruct-0.1                              | 64.7   | (-2.1, 1.7)        | 810              |

 Здесь приведена лишь часть лидерборда, подробнее смотрите в репозитории бенчмарка.
+180 сэмплов из арены утекло в трейн, спасибо Илье за информацию!
 | Model Name                                       | Winrate  | 95% CI             | Average # Tokens |
 |--------------------------------------------------|--------|--------------------|------------------|
 | gpt-4-1106-preview                               | 90.9   | (-1.3, 1.0)        | 541              |
 | gpt-4o-mini                                      | 83.9   | (-1.8, 1.1)        | 448              |
+| **vikhr-nemo-12b-instruct-r-21-09-24(180 leaked)**               | **79.8**   | (-2.2, 1.9)        | **627**              |
 | gemma-2-9b-it-sppo-iter3                         | 73.6   | (-1.6, 2.2)        | 509              |
 | gemma-2-9b-it                                    | 69.2   | (-2.5, 1.9)        | 459              |
 | t-lite-instruct-0.1                              | 64.7   | (-2.1, 1.7)        | 810              |