Libraries Transformers How to use harsh99/Codellama-7b-Instruct-hf-product-categorization with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="harsh99/Codellama-7b-Instruct-hf-product-categorization") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("harsh99/Codellama-7b-Instruct-hf-product-categorization")
model = AutoModelForCausalLM.from_pretrained("harsh99/Codellama-7b-Instruct-hf-product-categorization") Inference Inference Providers Local Apps Settings vLLM How to use harsh99/Codellama-7b-Instruct-hf-product-categorization with vLLM:
Install from pip and serve model # Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "harsh99/Codellama-7b-Instruct-hf-product-categorization"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "harsh99/Codellama-7b-Instruct-hf-product-categorization",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}' Use Docker docker model run hf.co/harsh99/Codellama-7b-Instruct-hf-product-categorization SGLang How to use harsh99/Codellama-7b-Instruct-hf-product-categorization 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 "harsh99/Codellama-7b-Instruct-hf-product-categorization" \
--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": "harsh99/Codellama-7b-Instruct-hf-product-categorization",
"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 "harsh99/Codellama-7b-Instruct-hf-product-categorization" \
--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": "harsh99/Codellama-7b-Instruct-hf-product-categorization",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}' Docker Model Runner How to use harsh99/Codellama-7b-Instruct-hf-product-categorization with Docker Model Runner:
docker model run hf.co/harsh99/Codellama-7b-Instruct-hf-product-categorization 
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="harsh99/Codellama-7b-Instruct-hf-product-categorization")