datatab/alpaca-cleaned-serbian-full
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How to use datatab/Yugo45-GPT with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="datatab/Yugo45-GPT") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("datatab/Yugo45-GPT")
model = AutoModelForCausalLM.from_pretrained("datatab/Yugo45-GPT")How to use datatab/Yugo45-GPT with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "datatab/Yugo45-GPT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "datatab/Yugo45-GPT",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/datatab/Yugo45-GPT
How to use datatab/Yugo45-GPT with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "datatab/Yugo45-GPT" \
--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": "datatab/Yugo45-GPT",
"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 "datatab/Yugo45-GPT" \
--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": "datatab/Yugo45-GPT",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use datatab/Yugo45-GPT with Docker Model Runner:
docker model run hf.co/datatab/Yugo45-GPT
This Yugo45-GPT (7b) model has been fine-tuned on the Alpaca dataset using the gordicaleksa/YugoGPT as the zero ground base model.
Yugo45-GPT is a merge of the following models using LazyMergekit:
Special thanks for idea Stopwolf and this X post @TheStopwolf
slices:
- sources:
- model: datatab/YugoGPT-Alpaca-v1
layer_range: [0, 32]
- model: FlexingD/yarn-mistral-7B-64k-instruct-alpaca-cleaned-origin
layer_range: [0, 32]
merge_method: slerp
base_model: datatab/YugoGPT-Alpaca-v1
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
# TBD
# TBD
Base model
NousResearch/Yarn-Mistral-7b-64k
docker model run hf.co/datatab/Yugo45-GPT