Instructions to use agentica-org/DeepScaleR-1.5B-Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use agentica-org/DeepScaleR-1.5B-Preview with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="agentica-org/DeepScaleR-1.5B-Preview")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("agentica-org/DeepScaleR-1.5B-Preview") model = AutoModelForCausalLM.from_pretrained("agentica-org/DeepScaleR-1.5B-Preview") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use agentica-org/DeepScaleR-1.5B-Preview with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "agentica-org/DeepScaleR-1.5B-Preview" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "agentica-org/DeepScaleR-1.5B-Preview", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/agentica-org/DeepScaleR-1.5B-Preview
- SGLang
How to use agentica-org/DeepScaleR-1.5B-Preview 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 "agentica-org/DeepScaleR-1.5B-Preview" \ --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": "agentica-org/DeepScaleR-1.5B-Preview", "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 "agentica-org/DeepScaleR-1.5B-Preview" \ --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": "agentica-org/DeepScaleR-1.5B-Preview", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use agentica-org/DeepScaleR-1.5B-Preview with Docker Model Runner:
docker model run hf.co/agentica-org/DeepScaleR-1.5B-Preview
Efficient Fine-Tuning of DeepScaleR-1.5B Without Increasing Parameters
What are the best methods for fine-tuning DeepScaleR-1.5B without increasing the number of parameters during inference? Would LoRA or other PEFT methods be effective, and what settings are recommended?
We don't have any specific recommendation here. For DeepScaleR's training we do full finetuning, but you can also try out LoRA to see if it works well.
We don't have any specific recommendation here. For DeepScaleR's training we do full finetuning, but you can also try out LoRA to see if it works well.
thanks will try it soon!
Some people on Twitter have tried LoRA, apparently it does even better!
Some people on Twitter have tried LoRA, apparently it does even better!
Could you provide a link?