Instructions to use altslate/JugnuLM-110M-R2plus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use altslate/JugnuLM-110M-R2plus with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="altslate/JugnuLM-110M-R2plus", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("altslate/JugnuLM-110M-R2plus", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use altslate/JugnuLM-110M-R2plus with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "altslate/JugnuLM-110M-R2plus"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "altslate/JugnuLM-110M-R2plus",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/altslate/JugnuLM-110M-R2plus

SGLang

How to use altslate/JugnuLM-110M-R2plus 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 "altslate/JugnuLM-110M-R2plus" \
    --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": "altslate/JugnuLM-110M-R2plus",
		"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 "altslate/JugnuLM-110M-R2plus" \
        --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": "altslate/JugnuLM-110M-R2plus",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use altslate/JugnuLM-110M-R2plus with Docker Model Runner:
```
docker model run hf.co/altslate/JugnuLM-110M-R2plus
```

rajpdus commited on 23 days ago

Commit

d3dd9ab

verified ·

1 Parent(s): f3d2fa6

Model card: cite arXiv:2609.14715

Browse files

Files changed (1) hide show

README.md +17 -0

README.md CHANGED Viewed

@@ -91,3 +91,20 @@ size bonus as the smallest of the three. Numbers are from a **VR-aware** eval (B
 ## License
 Apache-2.0. Training recipe and code: https://github.com/AltSlate-Labs/jugnu

 ## License
 Apache-2.0. Training recipe and code: https://github.com/AltSlate-Labs/jugnu
+## Paper
+This model is described in [*Depth and Scale in the Sub-150M Regime: JugnuLM-53M vs JugnuLM-110M*](https://arxiv.org/abs/2609.14715)
+(arXiv:2609.14715 · [HF Papers](https://huggingface.co/papers/2609.14715)).
+```bibtex
+@misc{rajput2026jugnu,
+  title         = {Depth and Scale in the Sub-150M Regime: JugnuLM-53M vs JugnuLM-110M},
+  author        = {Rajput, Dushyant and Chauhan, Nirdesh and Kosaraju, Siddharth},
+  year          = {2026},
+  eprint        = {2609.14715},
+  archivePrefix = {arXiv},
+  primaryClass  = {cs.AI},
+  url           = {https://arxiv.org/abs/2609.14715}
+}
+```