Instructions to use Vikhrmodels/Vikhr-7b-0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Vikhrmodels/Vikhr-7b-0.1 with Transformers:

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

pipe = pipeline("text-generation", model="Vikhrmodels/Vikhr-7b-0.1")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Vikhrmodels/Vikhr-7b-0.1")
model = AutoModelForCausalLM.from_pretrained("Vikhrmodels/Vikhr-7b-0.1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Vikhrmodels/Vikhr-7b-0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Vikhrmodels/Vikhr-7b-0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Vikhrmodels/Vikhr-7b-0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Vikhrmodels/Vikhr-7b-0.1

SGLang

How to use Vikhrmodels/Vikhr-7b-0.1 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-7b-0.1" \
    --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": "Vikhrmodels/Vikhr-7b-0.1",
		"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 "Vikhrmodels/Vikhr-7b-0.1" \
        --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": "Vikhrmodels/Vikhr-7b-0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Vikhrmodels/Vikhr-7b-0.1 with Docker Model Runner:
```
docker model run hf.co/Vikhrmodels/Vikhr-7b-0.1
```

AlexWortega commited on Jan 25, 2024

Commit

9f50e62

verified ·

1 Parent(s): a33f5e3

More Iters!!

Browse files

Files changed (6) hide show

README.md +3 -3
config.json +2 -2
generation_config.json +1 -1
model-00001-of-00003.safetensors +1 -1
model-00002-of-00003.safetensors +1 -1
model-00003-of-00003.safetensors +1 -1

README.md CHANGED Viewed

@@ -1,11 +1,11 @@
 ---
-license: apache-2.0
-datasets:
-- IlyaGusev/habr
 language:
 - ru
 - en
 library_name: transformers
 ---
 # Vikhr: модель на базе Mistral для русского языка

 ---
 language:
 - ru
 - en
+license: apache-2.0
 library_name: transformers
+datasets:
+- IlyaGusev/habr
 ---
 # Vikhr: модель на базе Mistral для русского языка

config.json CHANGED Viewed

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "v2_160",
   "architectures": [
     "LlamaForCausalLM"
   ],
@@ -23,7 +23,7 @@
   "sliding_window": 4096,
   "tie_word_embeddings": false,
   "torch_dtype": "bfloat16",
-  "transformers_version": "4.34.0",
   "use_cache": true,
   "vocab_size": 40000
 }

 {
+  "_name_or_path": "Vikhrmodels/Vikhr-7b-0.1",
   "architectures": [
     "LlamaForCausalLM"
   ],
   "sliding_window": 4096,
   "tie_word_embeddings": false,
   "torch_dtype": "bfloat16",
+  "transformers_version": "4.37.1",
   "use_cache": true,
   "vocab_size": 40000
 }

generation_config.json CHANGED Viewed

@@ -2,5 +2,5 @@
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   "bos_token_id": 1,
   "eos_token_id": 2,
-  "transformers_version": "4.34.0"
 }

   "_from_model_config": true,
   "bos_token_id": 1,
   "eos_token_id": 2,
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 }

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