Instructions to use ChaoticNeutrals/IQ_Test_l3_8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ChaoticNeutrals/IQ_Test_l3_8B with Transformers:

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

pipe = pipeline("text-generation", model="ChaoticNeutrals/IQ_Test_l3_8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ChaoticNeutrals/IQ_Test_l3_8B")
model = AutoModelForCausalLM.from_pretrained("ChaoticNeutrals/IQ_Test_l3_8B")
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
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ChaoticNeutrals/IQ_Test_l3_8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ChaoticNeutrals/IQ_Test_l3_8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ChaoticNeutrals/IQ_Test_l3_8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ChaoticNeutrals/IQ_Test_l3_8B

SGLang

How to use ChaoticNeutrals/IQ_Test_l3_8B 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 "ChaoticNeutrals/IQ_Test_l3_8B" \
    --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": "ChaoticNeutrals/IQ_Test_l3_8B",
		"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 "ChaoticNeutrals/IQ_Test_l3_8B" \
        --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": "ChaoticNeutrals/IQ_Test_l3_8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ChaoticNeutrals/IQ_Test_l3_8B with Docker Model Runner:
```
docker model run hf.co/ChaoticNeutrals/IQ_Test_l3_8B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

IQ Test

A new model built on Undi's Unholy and my own intelligence dataset. The goal is to increase Llama 3's benchmarks and intelligence level while still retaining the uncensored nature that users crave.

This is just the first test, with many more to come.

Downloads last month: 9

Safetensors

Model size

8B params

Tensor type

F16

Model tree for ChaoticNeutrals/IQ_Test_l3_8B

Base model

ResplendentAI/Smarts_Llama3

Finetuned

(1)

this model

Finetunes

1 model

Quantizations

5 models

ChaoticNeutrals
/

IQ_Test_l3_8B

IQ Test

Model tree for ChaoticNeutrals/IQ_Test_l3_8B

Spaces using ChaoticNeutrals/IQ_Test_l3_8B 9