Instructions to use EssentialAI/rnj-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EssentialAI/rnj-1 with Transformers:

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

pipe = pipeline("text-generation", model="EssentialAI/rnj-1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("EssentialAI/rnj-1")
model = AutoModelForCausalLM.from_pretrained("EssentialAI/rnj-1")
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 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "EssentialAI/rnj-1"
# 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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/EssentialAI/rnj-1

SGLang

How to use EssentialAI/rnj-1 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" \
    --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",
		"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" \
        --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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use EssentialAI/rnj-1 with Docker Model Runner:
```
docker model run hf.co/EssentialAI/rnj-1
```

Evaluation of Essential AI’s RNJ-1 for Differential Equations

by Javedalam - opened Dec 8, 2025

Discussion

Javedalam

Dec 8, 2025

Essential AI’s RNJ-1 is an 8-billion-parameter open-weight model based on a Gemma-3 style architecture with global attention and an extended context window. It is trained on more than eight trillion tokens and tuned for coding, mathematics, and general STEM reasoning.

my interest is specifically in its mathematical ability, I tested it directly on a sequence of differential equations, increasing the difficulty step by step.

The five equations I used were:

dy/dx – y = e^x
x * dy/dx + 2y = x^3
y'' – 4y' + 5y = e^(2x) * (x + 1)
y'' – 4y' + 4y = e^(2x) * (x + 1)
x^2 * y'' – 3x * y' + 4y = x^3

RNJ-1 solved four of these five equations correctly. It handled both first-order equations cleanly, produced the correct complementary and particular solutions for the non-resonant second-order equation, and solved the Cauchy–Euler equation exactly as expected. The only failure was on equation (4), the resonant second-order case where the forcing term overlaps with the homogeneous solution. In that problem, the model chose the wrong trial form for the particular solution and did not include the additional power of x required to break the resonance.

Aside from that one miss, RNJ-1’s reasoning was clear and consistent. It followed standard methods, including integrating factors, characteristic equations, and trial-particular functions, and it carried out the algebra correctly. The only practical issue I encountered was that very large context lengths in ZeroGPU caused truncation or runtime errors; at normal context lengths, the model behaved stably.

Compared with other models in this size class that I have used for solving similat differential equations— RNJ-1 performs noticeably better on routine STEM tasks. Its derivations are cleaner, and its structure is more reliable. it produced output in latex form but did not render it, while deepseek math renders the math equation.

Other than its difficulty with the resonance case and the occasional instability at very large context sizes, RNJ-1 is one of the strongest open-source 8B models available today for solving differential equations.

I was not able to run its gguf , quantized version on mh phone. it needs a recompile of llama.cpp

The model is running using zerogpu at this Space

https://huggingface.co/spaces/Javedalam/EssentialAI

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