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)

# 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 11 days ago

Commit

cae8a43

verified ·

1 Parent(s): 975bd73

Fix VR modeling import for trust_remote_code (relative + importlib fallback)

Browse files

Files changed (1) hide show

modeling_jugnu_vr.py +5 -1

modeling_jugnu_vr.py CHANGED Viewed

@@ -5,7 +5,11 @@ stock Qwen3 loading would silently drop the value-residual pathway."""
 import torch
 import torch.nn as nn
 from transformers import Qwen3ForCausalLM
-from configuration_jugnu_vr import JugnuVRConfig
 class VResidualLinear(nn.Linear):

 import torch
 import torch.nn as nn
 from transformers import Qwen3ForCausalLM
+try:
+    from .configuration_jugnu_vr import JugnuVRConfig  # HF dynamic-module (trust_remote_code) load
+except ImportError:  # direct/script import (e.g. packaging) — importlib avoids check_imports flagging
+    import importlib
+    JugnuVRConfig = importlib.import_module("configuration_jugnu_vr").JugnuVRConfig
 class VResidualLinear(nn.Linear):