Instructions to use bunnycore/Cognitron-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bunnycore/Cognitron-8B with Transformers:

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

pipe = pipeline("text-generation", model="bunnycore/Cognitron-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bunnycore/Cognitron-8B")
model = AutoModelForCausalLM.from_pretrained("bunnycore/Cognitron-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 bunnycore/Cognitron-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bunnycore/Cognitron-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": "bunnycore/Cognitron-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bunnycore/Cognitron-8B

SGLang

How to use bunnycore/Cognitron-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 "bunnycore/Cognitron-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": "bunnycore/Cognitron-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 "bunnycore/Cognitron-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": "bunnycore/Cognitron-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use bunnycore/Cognitron-8B with Docker Model Runner:
```
docker model run hf.co/bunnycore/Cognitron-8B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Cognitron-8B

Cognitron-8B is an experimental large language model (LLM) created by combining three pre-existing models: Llama-3-8B-Lexi-Uncensored, Einstein-v6.1-Llama3-8B, and dolphin-2.9-llama3-8b. This combination aims to achieve a unique blend of capabilities:

Uncensored Knowledge: By incorporating Llama-3-8B-Lexi-Uncensored, Cognitron-8B has access to a wider range of information without filtering.
Enhanced Intelligence: The inclusion of Einstein-v6.1-Llama3-8B is intended to boost Cognitron-8B's reasoning and problem-solving abilities.
Creative Fluency: The dolphin-2.9-llama3-8b component is designed to contribute creativity and unconventional thinking to Cognitron-8B's responses.

It is important to note that combining these models is an experiment, and the resulting performance is unknown.

GGUF: https://huggingface.co/mradermacher/Cognitron-8B-GGUF

Cognitron-8B is a merge of the following models using mergekit:

Potential Biases and Limitations

Uncensored Content: Due to the inclusion of uncensored models, Cognitron-8B may generate outputs containing biases, hate speech, or offensive language.

Importance of Uncensored Models

The inclusion of an uncensored model in Cognitron-8B reflects a growing interest in exploring the potential benefits of unfiltered information for LLMs. Here's why uncensored models are important:

Comprehensiveness: Unrestricted access to information allows LLMs to capture a more complete picture of the world, even if it includes controversial or sensitive topics.
Real-World Applicability: In situations where internet access is limited, uncensored LLMs could serve as a valuable source of unfiltered knowledge, allowing users to make informed decisions based on the available data.

🧩 Configuration

models:
  - model: Orenguteng/Llama-3-8B-Lexi-Uncensored
  - model: Weyaxi/Einstein-v6.1-Llama3-8B
  - model: cognitivecomputations/dolphin-2.9-llama3-8b
merge_method: model_stock
base_model: Orenguteng/Llama-3-8B-Lexi-Uncensored
dtype: bfloat16

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for bunnycore/Cognitron-8B

Merges

1 model

Quantizations

3 models

bunnycore
/

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Cognitron-8B

Potential Biases and Limitations

Importance of Uncensored Models

🧩 Configuration

Model tree for bunnycore/Cognitron-8B

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