sentence-transformers/msmarco
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How to use qikp/bingo-8m with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="qikp/bingo-8m") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("qikp/bingo-8m")
model = AutoModelForCausalLM.from_pretrained("qikp/bingo-8m")How to use qikp/bingo-8m with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "qikp/bingo-8m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "qikp/bingo-8m",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/qikp/bingo-8m
How to use qikp/bingo-8m with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "qikp/bingo-8m" \
--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": "qikp/bingo-8m",
"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 "qikp/bingo-8m" \
--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": "qikp/bingo-8m",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use qikp/bingo-8m with Docker Model Runner:
docker model run hf.co/qikp/bingo-8m
bingo is a small language model designed for on-device search suggestions.
It was trained on the queries config of sentence-transformers/msmarco, using 1 epoch, 32 batch size, 1.5e-4 learning rate, and the pika 2 tokenizer.
Also, evaluation for 5K rows was done during training.