0x7o/fialka-v3-data
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How to use 0x7o/fialka-7B-v3 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="0x7o/fialka-7B-v3") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("0x7o/fialka-7B-v3")
model = AutoModelForCausalLM.from_pretrained("0x7o/fialka-7B-v3")How to use 0x7o/fialka-7B-v3 with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "0x7o/fialka-7B-v3"
# 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-v3",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/0x7o/fialka-7B-v3
How to use 0x7o/fialka-7B-v3 with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "0x7o/fialka-7B-v3" \
--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-v3",
"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-v3" \
--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-v3",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use 0x7o/fialka-7B-v3 with Docker Model Runner:
docker model run hf.co/0x7o/fialka-7B-v3
Fialka language models are trained to follow instructions and maintain communication in Russian. The third version of the model was based on the Llama 2 model, which was specifically trained on a large corpus of Russian, allowing Fialka to generate its responses more accurately.
The model has a query format as in zephyr.
<|user|>
Что такое мем?</s>
<|assistant|>
Мем - это культурный феномен, который может быть описан как информационный вирус. Он представляет собой абстрактный элемент культуры или идеи в виде изображения (обычно с изображением человека) и его краткого текста на основе которого можно создать мемы различных форматов: от графических до текстовых шуток; мема могут использоваться для передачи определенных идей через интернет-сообщество без необходимости использовать слова напрямую!</s>
Check out the space to use the model in UI without downloading.