Text Generation
Transformers
Safetensors
gpt_bigcode
trl
reward-trainer
Generated from Trainer
text-generation-inference
Instructions to use nahed22/rm_checkpoint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nahed22/rm_checkpoint with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nahed22/rm_checkpoint")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("nahed22/rm_checkpoint") model = AutoModelForCausalLM.from_pretrained("nahed22/rm_checkpoint", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use nahed22/rm_checkpoint with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nahed22/rm_checkpoint" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nahed22/rm_checkpoint", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/nahed22/rm_checkpoint
- SGLang
How to use nahed22/rm_checkpoint 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 "nahed22/rm_checkpoint" \ --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": "nahed22/rm_checkpoint", "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 "nahed22/rm_checkpoint" \ --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": "nahed22/rm_checkpoint", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use nahed22/rm_checkpoint with Docker Model Runner:
docker model run hf.co/nahed22/rm_checkpoint
- Xet hash:
- 5b8732590b071cfca55c6d6d4e0ba16740d936e455b906d91a7fab5cc3b151ec
- Size of remote file:
- 5.05 kB
- SHA256:
- 784f89a2cc77c7154c0c6ff7d753ab5516e2dad00f5c0b5bf0b34281ff2b65a1
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