0x7o/fialka-v1-zephyr
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How to use 0x7o/fialka-7B-v2 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="0x7o/fialka-7B-v2") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("0x7o/fialka-7B-v2")
model = AutoModelForCausalLM.from_pretrained("0x7o/fialka-7B-v2")How to use 0x7o/fialka-7B-v2 with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "0x7o/fialka-7B-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "0x7o/fialka-7B-v2",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/0x7o/fialka-7B-v2
How to use 0x7o/fialka-7B-v2 with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "0x7o/fialka-7B-v2" \
--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": "0x7o/fialka-7B-v2",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'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 "0x7o/fialka-7B-v2" \
--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": "0x7o/fialka-7B-v2",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use 0x7o/fialka-7B-v2 with Docker Model Runner:
docker model run hf.co/0x7o/fialka-7B-v2
Fialka language models are trained to follow instructions and maintain communication in Russian. Version 2.0 is trained on a dataset of instructions.
Check out our new V3 model, which generates Russian text more accurately and better.
The model has a query format as in zephyr.
<|user|>
Напиши код на python, который удалит файл `1.txt`.</s>
<|assistant|>
Для того чтобы удалить файл в Python необходимо использовать функцию os.remove(). Она принимает имя файла в качестве аргумента и выполняет операции удаления <...>
Check out the space to use the model in UI without downloading.