Instructions to use duyntnet/Neural-una-cybertron-7b-imatrix-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use duyntnet/Neural-una-cybertron-7b-imatrix-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="duyntnet/Neural-una-cybertron-7b-imatrix-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("duyntnet/Neural-una-cybertron-7b-imatrix-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use duyntnet/Neural-una-cybertron-7b-imatrix-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf duyntnet/Neural-una-cybertron-7b-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf duyntnet/Neural-una-cybertron-7b-imatrix-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf duyntnet/Neural-una-cybertron-7b-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf duyntnet/Neural-una-cybertron-7b-imatrix-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf duyntnet/Neural-una-cybertron-7b-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf duyntnet/Neural-una-cybertron-7b-imatrix-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf duyntnet/Neural-una-cybertron-7b-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf duyntnet/Neural-una-cybertron-7b-imatrix-GGUF:Q4_K_M

Use Docker

docker model run hf.co/duyntnet/Neural-una-cybertron-7b-imatrix-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use duyntnet/Neural-una-cybertron-7b-imatrix-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "duyntnet/Neural-una-cybertron-7b-imatrix-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "duyntnet/Neural-una-cybertron-7b-imatrix-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/duyntnet/Neural-una-cybertron-7b-imatrix-GGUF:Q4_K_M

SGLang

How to use duyntnet/Neural-una-cybertron-7b-imatrix-GGUF 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 "duyntnet/Neural-una-cybertron-7b-imatrix-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "duyntnet/Neural-una-cybertron-7b-imatrix-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "duyntnet/Neural-una-cybertron-7b-imatrix-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "duyntnet/Neural-una-cybertron-7b-imatrix-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use duyntnet/Neural-una-cybertron-7b-imatrix-GGUF with Ollama:
```
ollama run hf.co/duyntnet/Neural-una-cybertron-7b-imatrix-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use duyntnet/Neural-una-cybertron-7b-imatrix-GGUF with Docker Model Runner:
```
docker model run hf.co/duyntnet/Neural-una-cybertron-7b-imatrix-GGUF:Q4_K_M
```

Lemonade

How to use duyntnet/Neural-una-cybertron-7b-imatrix-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull duyntnet/Neural-una-cybertron-7b-imatrix-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Neural-una-cybertron-7b-imatrix-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Quantizations of https://huggingface.co/Weyaxi/Neural-una-cybertron-7b

Open source inference clients/UIs

Closed source inference clients/UIs

LM Studio
More will be added...

From original readme

Neural-una-cybertron-7b is an fblgit/una-cybertron-7b-v2-bf16 model that has been further fine-tuned with Direct Preference Optimization (DPO) using the Intel/orca_dpo_pairs dataset.

This model was created after examining the procedure of mlabonne/NeuralHermes-2.5-Mistral-7B model. Special thanks to @mlabonne.

Addionatal Information

This model was fine-tuned on Nvidia A100-SXM4-40GB GPU.

The total training time was 1 hour and 10 minutes.

Prompt Template(s)

ChatML

<|im_start|>system
{system}<|im_end|>
<|im_start|>user
{user}<|im_end|>
<|im_start|>assistant
{asistant}<|im_end|>

Training hyperparameters

LoRA:

r=16
lora_alpha=16
lora_dropout=0.05
bias="none"
task_type="CAUSAL_LM"
target_modules=['k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj']

Training arguments:

per_device_train_batch_size=4
gradient_accumulation_steps=4
gradient_checkpointing=True
learning_rate=5e-5
lr_scheduler_type="cosine"
max_steps=200
optim="paged_adamw_32bit"
warmup_steps=100

DPOTrainer:

beta=0.1
max_prompt_length=1024
max_length=1536

Downloads last month: 221

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

1-bit

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5-bit

6-bit

8-bit