Text Generation
Transformers
Safetensors
llama
Generated from Trainer
trl
grpo
conversational
text-generation-inference
Instructions to use ucalyptus/prem-1B-SQL-grpo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ucalyptus/prem-1B-SQL-grpo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ucalyptus/prem-1B-SQL-grpo") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ucalyptus/prem-1B-SQL-grpo") model = AutoModelForCausalLM.from_pretrained("ucalyptus/prem-1B-SQL-grpo", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ucalyptus/prem-1B-SQL-grpo with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ucalyptus/prem-1B-SQL-grpo" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ucalyptus/prem-1B-SQL-grpo", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ucalyptus/prem-1B-SQL-grpo
- SGLang
How to use ucalyptus/prem-1B-SQL-grpo 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 "ucalyptus/prem-1B-SQL-grpo" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ucalyptus/prem-1B-SQL-grpo", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "ucalyptus/prem-1B-SQL-grpo" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ucalyptus/prem-1B-SQL-grpo", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use ucalyptus/prem-1B-SQL-grpo with Docker Model Runner:
docker model run hf.co/ucalyptus/prem-1B-SQL-grpo
Download upload_model.py from ucalyptus/prem-1B-SQL-grpo: direct link, hf CLI and curl.
- Browser
- Download file 839 Bytes
-
https://huggingface.co/ucalyptus/prem-1B-SQL-grpo/resolve/main/upload_model.py
- Command line
-
hf download hf://ucalyptus/prem-1B-SQL-grpo/upload_model.py
-
curl -L -o upload_model.py https://huggingface.co/ucalyptus/prem-1B-SQL-grpo/resolve/main/upload_model.py
839 Bytes
| #!/usr/bin/env python3 | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| # HuggingFace credentials | |
| HF_TOKEN = "hf_" | |
| REPO_ID = "ucalyptus/prem-1B-SQL-grpo" | |
| # Local model path (using latest checkpoint) | |
| MODEL_PATH = "outputs/Prem-1B-SQL-GRPO-v2/checkpoint-295" | |
| def upload_model(): | |
| print(f"Loading model from {MODEL_PATH}") | |
| # Load model and tokenizer | |
| model = AutoModelForCausalLM.from_pretrained(MODEL_PATH) | |
| tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH) | |
| print(f"Uploading model to {REPO_ID}") | |
| # Push to hub | |
| model.push_to_hub( | |
| REPO_ID, | |
| token=HF_TOKEN, | |
| private=False | |
| ) | |
| tokenizer.push_to_hub( | |
| REPO_ID, | |
| token=HF_TOKEN, | |
| private=False | |
| ) | |
| print("Upload complete!") | |
| if __name__ == "__main__": | |
| upload_model() |