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

Libraries

How to use EssentialAI/rnj-1-instruct with Transformers:

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

pipe = pipeline("text-generation", model="EssentialAI/rnj-1-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-instruct")
model = AutoModelForCausalLM.from_pretrained("EssentialAI/rnj-1-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]:]))

Inference
HuggingChat
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Use Docker

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

SGLang

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

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

Tool calling appears to be currently broken

#10

by sanjilover - opened Feb 28

Discussion

sanjilover

Feb 28

Hello!

I’m currently using the rnj-1:8b-instruct-q4_K_M model locally via Ollama, integrated with Cline. While testing its capabilities, I’ve noticed that when I attempt to invoke tools in my IDE, the model responds with unexpected output (e.g., "jibberish").

I’ve also tested the web_search tool directly in the Ollama GUI, but it appears to be non-functional as well.

Does this model support tool integration? Given its strong benchmark performance, I’d expect tool functionality to be available. I’d greatly appreciate any guidance on resolving this, as I’m eager to leverage this model for my workflows.

Thank you for your time and assistance!

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