Instructions to use agentica-org/DeepScaleR-1.5B-Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
Local Apps Settings

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
```

Non- Math Use Cases?

#18

by DeFactOfficial - opened Feb 21, 2025

Discussion

DeFactOfficial

Feb 21, 2025

How does this model perform at tasks such as tool use, RAG, general purpose QA? Has it been benchmarked for tasks other than math? Is SFT likely to be successful for these sorts of tasks? Sorry if this is a naive question, I'm new to training models - usually I just inference them, but this model seems like something really special so I'm ready to learn whatever I need

michaelzhiluo

Agentica org Feb 25, 2025

We are going to release models for other use cases. Stay tuned!

ZiggyS

Feb 25, 2025

We are going to release models for other use cases. Stay tuned!

Coding perhaps? ( python mainly ). Testing last night, it struggled a bit getting some simple code right. But i know im an edge-case.

michaelzhiluo

Agentica org Mar 2, 2025

Yessir, coding is much much harder though, as the dataset quality is a lot worse than math ;)

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