Instructions to use EssentialAI/rnj-1.5-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EssentialAI/rnj-1.5-instruct with Transformers:

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

pipe = pipeline("text-generation", model="EssentialAI/rnj-1.5-instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("EssentialAI/rnj-1.5-instruct")
model = AutoModelForCausalLM.from_pretrained("EssentialAI/rnj-1.5-instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use EssentialAI/rnj-1.5-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "EssentialAI/rnj-1.5-instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EssentialAI/rnj-1.5-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/EssentialAI/rnj-1.5-instruct

SGLang

How to use EssentialAI/rnj-1.5-instruct 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 "EssentialAI/rnj-1.5-instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EssentialAI/rnj-1.5-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "EssentialAI/rnj-1.5-instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EssentialAI/rnj-1.5-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use EssentialAI/rnj-1.5-instruct with Docker Model Runner:
```
docker model run hf.co/EssentialAI/rnj-1.5-instruct
```

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@@ -149,7 +149,7 @@ We welcome your questions and feedback. You can contact us at info@essential.ai.
 ```bibtex
 @misc{rnj1_5_instruct,
   title  = {{Rnj-1-5-Instruct}},
-  author = {Ashish Vaswani and Mike Callahan and Adarsh Chaluvaraju and Aleksa Gordić and Devaansh Gupta and Yash Jain and Philip Monk and Michael Pust and Tim Romanski and Peter Rushton and Ali Shehper and Divya Shivaprasad and Saurabh Srivastava and Anil Thomas and Alok Tripathy and Ameya Velingker and {{Essential AI}}},
   year   = {2026},
   url    = {https://huggingface.co/EssentialAI/rnj-1-5-instruct},
   note   = {Long-context Instruction-tuned model release}

 ```bibtex
 @misc{rnj1_5_instruct,
   title  = {{Rnj-1-5-Instruct}},
+  author = {{{Essential AI}} Mike Callahan and Adarsh Chaluvaraju and Aleksa Gordić and Devaansh Gupta and Yash Jain and Philip Monk and Michael Pust and Tim Romanski and Peter Rushton and Ali Shehper and Divya Shivaprasad and Saurabh Srivastava and Anil Thomas and Alok Tripathy and Ameya Velingker and Ashish Vaswani},
   year   = {2026},
   url    = {https://huggingface.co/EssentialAI/rnj-1-5-instruct},
   note   = {Long-context Instruction-tuned model release}