GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
EssentialAI
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Run this model inference with full control and performance in your environment.
Model Details
Model Provider
EssentialAI
Model Tree
EssentialAI/rnj-1
Input Modalities
Output Modalities
Supported Functionality
The easiest way to run Rnj-1 on a laptop is via llama.cpp. A pre-quantized checkpoint is available here as well as instructions to get started.
Rnj-1 is supported starting from transformers 4.51.2
Example code for querying model without tools
python
import torchfrom transformers import AutoTokenizer, AutoModelForCausalLMimport osmodel_id = "EssentialAI/rnj-1-instruct"os.environ["HF_TOKEN"] = <YOUR-HF-TOKEN>print(f"Loading model: {model_id}...")model = AutoModelForCausalLM.from_pretrained(model_id,dtype=torch.bfloat16,device_map="auto",)tokenizer = AutoTokenizer.from_pretrained(model_id)print("Model and tokenizer loaded successfully.")messages = [{"role": "system", "content": "You are a helpful AI assistant."}, # Optional system message{"role": "user", "content": "Who are you?"}]input_ids = tokenizer.apply_chat_template(messages,add_generation_prompt=True,return_tensors="pt").to(model.device)# --- Generate Prediction --- #print("Generating prediction...")output_ids = model.generate(input_ids,max_new_tokens=50,pad_token_id=tokenizer.eos_token_id,do_sample=True,temperature=0.2,top_p=0.95)response = tokenizer.decode(output_ids[0][input_ids.shape[-1]:], skip_special_tokens=True)print(response)
Example code for querying with tools
Rnj-1 supports tool-calling which can be parsed by hermes tool-call parser. The tool calls are formatted inside <tool_call> and </tool_call> tags.
An example usage is as follows:
python
tools = [{"type": "function","function": {"name": "get_weather","description": "Get the current weather in a given location","parameters": {"type": "object","properties": {"location": {"type": "string", "description": "City and state, e.g., 'San Francisco, CA'"},"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}},"required": ["location", "unit"],},},},]messages = [{"role": "system", "content": "You are a helpful AI assistant."}, # Optional system message{"role": "user", "content": "What is the weather in San Francisco, CA in Celsius?"}]input_ids = tokenizer.apply_chat_template(messages,tools=tools,add_generation_prompt=True,return_tensors="pt").to(model.device)# --- Generate Prediction --- #print("Generating prediction...")output_ids = model.generate(input_ids,max_new_tokens=200,pad_token_id=tokenizer.eos_token_id,do_sample=True,temperature=0.2,top_p=0.95)response = tokenizer.decode(output_ids[0][input_ids.shape[-1]:], skip_special_tokens=False)# NOTE: skip_special_tokens is set to False.print(response)
Example code for fill-in-the-middle (FIM)
Rnj-1 supports FIM, we show an example payload to trigger FIM mode for Rnj-1 below:
python
PRE = "<|pre_fim|>"MID = "<|mid_fim|>"SUF = "<|suf_fim|>"prefix = """def binary_search(arr, target):lo = 0hi = len(arr) - 1while lo <= hi:"""suffix = """return -1"""input = PRE + prefix + SUF + suffix + MIDmessages = [{"role": "system", "content": "You are a helpful AI assistant."},{"role": "user", "content": input}]input_ids = tokenizer.apply_chat_template(messages,tools=tools,add_generation_prompt=True,return_tensors="pt").to(model.device)# --- Generate Prediction --- #print("Generating prediction...")output_ids = model.generate(input_ids,max_new_tokens=100,pad_token_id=tokenizer.eos_token_id,do_sample=True,temperature=0.2,top_p=0.95)response = tokenizer.decode(output_ids[0][input_ids.shape[-1]:], skip_special_tokens=False)print(response)
On machines that run vLLM, it’s as easy as:
bash
vllm serve EssentialAI/rnj-1-instruct
To launch a vLLM server with tool-calling support enabled:
python
vllm serve EssentialAI/rnj-1-instruct --enable-auto-tool-choice --tool-call-parser hermes
On machines that run SGLang, it’s as easy as:
bash
python3 -m sglang.launch_server --model EssentialAI/rnj-1-instruct
Rnj-1 works great with Cline, an open source AI coding agent, and is very easy to set up.
The Cline extension is available for VS Code / Cursor, JetBrains IDEs (IntelliJ, PyCharm, WebStorm, etc.) and VSCodium / Windsurf.
Simply add the Cline extension to your favorite IDE (see instructions here) and then enter the details for your Rnj-1 endpoint (instructions here).
To use Rnj-1 with Claude Code, you can use https://github.com/musistudio/claude-code-router. Follow the instructions to set up Claude Code and Claude Code Router at https://github.com/musistudio/claude-code-router/blob/main/README.md.
Clone the EssentialAI fork of mini-swe-agent (github). Inside the repo, run the following inside a virtualenv:
python
git checkout eaipip install -e .export TOGETHER_API_KEY="..." # set this to your Together.AI access key# use EssentialAI/rnj-1-instruct to solve a performance optimization taskmini-extra perf-single [--instance <k>]# use EssentialAI/rnj-1-instruct to resolve a SWE PR descriptionmini-extra swebench-single [--instance <k>]
Rnj-1 is primarily a coding and STEM model. Hence, it is not optimized for factual recovery.
Rnj-1 is trained on online web data, and we have observed that it sometimes confuses its identity with other model providers. We believe this is due to a variety of reasons, including references to language models from other providers, model generated data, etc. We hope to rectify this in our follow-up release.
Additionally, Rnj-1 has not been trained or provided with a knowledge cutoff date and may therefore respond with information coming from its training data. If specifically asked for its knowledge cutoff date, the model may hallucinate a date.
This repository and the model weights are licensed under the Apache License, Version 2.0 (Apache 2.0).
We welcome your questions and feedback. You can contact us at info@essential.ai.
bibtex
@misc{rnj1_instruct,title = {{Rnj-1-Instruct}},author = {Ashish Vaswani and Mike Callahan and Adarsh Chaluvaraju and Aleksa Gordić and Devaansh Gupta and Yash Jain and Divya Mansingka and Philip Monk and Khoi Nguyen and Mohit Parmar and Michael Pust and Tim Romanski and Peter Rushton and Ali Shehper and Divya Shivaprasad and Somanshu Singla and Kurt Smith and Saurabh Srivastava and Anil Thomas and Alok Tripathy and Yash Vanjani and Ameya Velingker and {{Essential AI}}},year = {2025},url = {https://huggingface.co/EssentialAI/rnj-1-instruct},note = {Instruction-tuned model release}}