- Libraries
- PEFT
How to use yalhessi/lemexp-task1-v3-lemma_object_small-deepseek-coder-6.7b-base with PEFT:
from peft import PeftModel
from transformers import AutoModelForCausalLM
base_model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-6.7b-base")
model = PeftModel.from_pretrained(base_model, "yalhessi/lemexp-task1-v3-lemma_object_small-deepseek-coder-6.7b-base") - Transformers
How to use yalhessi/lemexp-task1-v3-lemma_object_small-deepseek-coder-6.7b-base with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="yalhessi/lemexp-task1-v3-lemma_object_small-deepseek-coder-6.7b-base") # Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("yalhessi/lemexp-task1-v3-lemma_object_small-deepseek-coder-6.7b-base", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use yalhessi/lemexp-task1-v3-lemma_object_small-deepseek-coder-6.7b-base with vLLM:
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yalhessi/lemexp-task1-v3-lemma_object_small-deepseek-coder-6.7b-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "yalhessi/lemexp-task1-v3-lemma_object_small-deepseek-coder-6.7b-base",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Use Docker
docker model run hf.co/yalhessi/lemexp-task1-v3-lemma_object_small-deepseek-coder-6.7b-base
- SGLang
How to use yalhessi/lemexp-task1-v3-lemma_object_small-deepseek-coder-6.7b-base with SGLang:
Install from pip and serve model
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "yalhessi/lemexp-task1-v3-lemma_object_small-deepseek-coder-6.7b-base" \
--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": "yalhessi/lemexp-task1-v3-lemma_object_small-deepseek-coder-6.7b-base",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Use Docker images
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 "yalhessi/lemexp-task1-v3-lemma_object_small-deepseek-coder-6.7b-base" \
--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": "yalhessi/lemexp-task1-v3-lemma_object_small-deepseek-coder-6.7b-base",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}' - Docker Model Runner
How to use yalhessi/lemexp-task1-v3-lemma_object_small-deepseek-coder-6.7b-base with Docker Model Runner:
docker model run hf.co/yalhessi/lemexp-task1-v3-lemma_object_small-deepseek-coder-6.7b-base
