Instructions to use GenueAI/Geode-Onyx-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GenueAI/Geode-Onyx-2 with Transformers:

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

pipe = pipeline("text-generation", model="GenueAI/Geode-Onyx-2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("GenueAI/Geode-Onyx-2")
model = AutoModelForCausalLM.from_pretrained("GenueAI/Geode-Onyx-2")
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 GenueAI/Geode-Onyx-2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GenueAI/Geode-Onyx-2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GenueAI/Geode-Onyx-2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/GenueAI/Geode-Onyx-2

SGLang

How to use GenueAI/Geode-Onyx-2 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 "GenueAI/Geode-Onyx-2" \
    --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": "GenueAI/Geode-Onyx-2",
		"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 "GenueAI/Geode-Onyx-2" \
        --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": "GenueAI/Geode-Onyx-2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use GenueAI/Geode-Onyx-2 with Docker Model Runner:
```
docker model run hf.co/GenueAI/Geode-Onyx-2
```

💎 Geode Onyx 2 (3B)

Onyx 2 is a 3-billion parameter conversational AI model, fine-tuned as part of the second generation of the Geode model family.

Model Details

Base Model: Qwen 2.5 3B Instruct
Parameters: 3 Billion
Fine-Tuning: LoRA (r=32, alpha=64)
Training Loss: 0.40
Precision: FP16
License: Apache 2.0

The Geode Family (Second Generation)

The Geode family is Genue AI's lineup of locally-runnable conversational models. In the second generation, Beryl has been retired and replaced by Pyrite, a specialized coding model:

Model	Parameters	Role
Pyrite	7B	Coding specialist
Onyx	3B	Balanced logic & personality
Thaumite	8B	Flagship, highest capability

Note: Beryl (0.5B) was the original lightweight experimental model in the first generation and has been replaced by Pyrite, which focuses specifically on code generation tasks.

Usage

Onyx 2 uses the Qwen Instruct prompt format:

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "GenueAI/Geode-Onyx-2",
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("GenueAI/Geode-Onyx-2")

prompt = "<|im_start|>user\nWhat is your name?<|im_end|>\n<|im_start|>assistant\n"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Data

Fine-tuned on a curated dataset of 1,013 examples covering:

Identity & self-awareness - AI assistant identity and capabilities
Mathematical reasoning - Arithmetic, algebra, word problems
General knowledge - Broad factual knowledge
HTML/CSS/JavaScript code generation - Web development tasks
Physics problems - Falling objects, thermodynamics
Genue AI ecosystem knowledge - Company information, model family details
Conversational generalization - Natural dialogue patterns
Anti-hallucination training - Proper handling of unknown information (time, location, preferences)

Model Architecture

Base: Qwen 2.5 3B Instruct
Adapter: LoRA with r=32, alpha=64
Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Trainable parameters: 59.9M (1.9% of total)

Training Details

Training regime: FP16 mixed precision
Epochs: 2
Batch size: 8
Learning rate: 2e-4
Training time: ~8 minutes on RTX 3090

Developed By

Genue AI — Founded by Brybod123 (Bradar)

Model Card Contact

For questions or issues, contact Genue AI through the HuggingFace repository.

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Safetensors

Model size

3B params

Tensor type

F16

Model tree for GenueAI/Geode-Onyx-2

Base model

Qwen/Qwen2.5-3B

Finetuned

Qwen/Qwen2.5-3B-Instruct

Finetuned

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