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
```

rnj-1.5-instruct / config.json

Philip Monk

remove unused configs

b3675a3 about 1 month ago

raw

history blame contribute delete

2.08 kB

	{
	"architectures": [
	"Rnj1ForCausalLM"
	],
	"attention_bias": false,
	"attention_dropout": 0.0,
	"attn_logit_softcapping": null,
	"bos_token_id": 2,
	"cache_implementation": "hybrid",
	"eos_token_id": 1,
	"final_logit_softcapping": 30.0,
	"head_dim": 128,
	"hidden_act": "gelu_pytorch_tanh",
	"hidden_activation": "gelu_pytorch_tanh",
	"hidden_size": 4096,
	"initializer_range": 0.02,
	"intermediate_size": 16384,
	"layer_types": [
	"chunked_attention",
	"chunked_attention",
	"chunked_attention",
	"full_attention",
	"chunked_attention",
	"chunked_attention",
	"chunked_attention",
	"full_attention",
	"chunked_attention",
	"chunked_attention",
	"chunked_attention",
	"full_attention",
	"chunked_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"chunked_attention",
	"full_attention",
	"chunked_attention",
	"chunked_attention",
	"chunked_attention",
	"full_attention",
	"chunked_attention",
	"chunked_attention",
	"chunked_attention",
	"full_attention",
	"chunked_attention",
	"chunked_attention",
	"chunked_attention",
	"chunked_attention"
	],
	"max_position_embeddings": 163840,
	"model_type": "gemma3_text",
	"num_attention_heads": 32,
	"num_hidden_layers": 32,
	"num_key_value_heads": 8,
	"pad_token_id": 0,
	"query_pre_attn_scalar": 128,
	"rms_norm_eps": 1e-06,
	"rope_theta": 10000,
	"sliding_window": 8192,
	"torch_dtype": "float32",
	"use_cache": true,
	"vocab_size": 128256,
	"rope_parameters": {
	"full_attention": {
	"rope_type": "yarn",
	"rope_theta": 10000,
	"factor": 20.0,
	"original_max_position_embeddings": 8192,
	"attention_factor": 1.0,
	"beta_fast": 64.0,
	"beta_slow": 1.0
	},
	"chunked_attention": {
	"rope_type": "yarn",
	"rope_theta": 10000,
	"factor": 20.0,
	"original_max_position_embeddings": 8192,
	"attention_factor": 1.0,
	"beta_fast": 64.0,
	"beta_slow": 1.0
	}
	}
	}