nilq/small-lua-stack
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How to use nilq/lua-mistral-1L-mini with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="nilq/lua-mistral-1L-mini") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("nilq/lua-mistral-1L-mini")
model = AutoModelForCausalLM.from_pretrained("nilq/lua-mistral-1L-mini")How to use nilq/lua-mistral-1L-mini with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nilq/lua-mistral-1L-mini"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "nilq/lua-mistral-1L-mini",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/nilq/lua-mistral-1L-mini
How to use nilq/lua-mistral-1L-mini with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "nilq/lua-mistral-1L-mini" \
--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": "nilq/lua-mistral-1L-mini",
"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 "nilq/lua-mistral-1L-mini" \
--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": "nilq/lua-mistral-1L-mini",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use nilq/lua-mistral-1L-mini with Docker Model Runner:
docker model run hf.co/nilq/lua-mistral-1L-mini
This model is a mini single-layer Mistral model pre-trained on on the nilq/small-lua-stack dataset.
It achieves the following results on the evaluation set:
This model might contain some very simple model of Lua.
Let's see if we can find some interesting stuff inside this model.
Trained on the Lua subset of The Stack.
The following hyperparameters were used during training: