Instructions to use fblgit/una-cybertron-7b-v2-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fblgit/una-cybertron-7b-v2-bf16 with Transformers:

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

pipe = pipeline("text-generation", model="fblgit/una-cybertron-7b-v2-bf16")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("fblgit/una-cybertron-7b-v2-bf16")
model = AutoModelForCausalLM.from_pretrained("fblgit/una-cybertron-7b-v2-bf16")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use fblgit/una-cybertron-7b-v2-bf16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fblgit/una-cybertron-7b-v2-bf16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fblgit/una-cybertron-7b-v2-bf16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/fblgit/una-cybertron-7b-v2-bf16

SGLang

How to use fblgit/una-cybertron-7b-v2-bf16 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 "fblgit/una-cybertron-7b-v2-bf16" \
    --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": "fblgit/una-cybertron-7b-v2-bf16",
		"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 "fblgit/una-cybertron-7b-v2-bf16" \
        --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": "fblgit/una-cybertron-7b-v2-bf16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use fblgit/una-cybertron-7b-v2-bf16 with Docker Model Runner:
```
docker model run hf.co/fblgit/una-cybertron-7b-v2-bf16
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Card for una-cybertron-7b-v2-bf16 (UNA: Uniform Neural Alignment)

We strike back, introducing Cybertron 7B v2 a 7B MistralAI based model, best on it's series. Trained on SFT, DPO and UNA (Unified Neural Alignment) on multiple datasets. He scores EXACTLY #1 with 69.67+ score on HF LeaderBoard board, #8 ALL SIZES top score.

v1 Scoring #1 at 2 December 2023 with 69.43 ..few models were releasse .. but only 1 can survive: CYBERTRON!
v2 Scoring #1 at 5 December 2023 with 69.67

Model	Average	ARC (25-s)	HellaSwag (10-s)	MMLU (5-s)	TruthfulQA (MC) (0-s)	Winogrande (5-s)	GSM8K (5-s)
mistralai/Mistral-7B-v0.1	60.97	59.98	83.31	64.16	42.15	78.37	37.83
Intel/neural-chat-7b-v3-2	68.29	67.49	83.92	63.55	59.68	79.95	55.12
perlthoughts/Chupacabra-7B-v2	63.54	66.47	85.17	64.49	57.6	79.16	28.35
fblgit/una-cybertron-7b-v1-fp16	69.49	68.43	85.85	63.34	63.28	80.90	55.12
fblgit/una-cybertron-7b-v2-bf16	69.67	68.26	85.?4	63.23	64.63	81.37	55.04

The model excels in mathematics, logic, reasoning, overall very smart. He can make a deep reasoning over the context and prompt, it gives the impression of not missing details around.

Model Details

Adiestrated with UNA: Uniform Neural Alignment technique (paper going out soon).

What is NOT UNA? Its not a merged layers model. Is not SLERP or SLURP or similar.
What is UNA? A formula & A technique to TAME models
When will be released the code and paper? When have time, contribute and it'll be faster.

Model Description

Developed by: juanako.ai
Author: Xavier M.
Investors CONTACT HERE
Model type: MistralAI 7B
Funded by Cybertron's H100's with few hours training.

Prompt

The model is very good, works well on almost any prompt but ChatML format and Alpaca System gets the best

<|im_start|>system
- You are a helpful assistant chatbot trained by MosaicML.
- You answer questions.
- You are excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.
- You are more than just an information source, you are also able to write poetry, short stories, and make jokes.<|im_end|>
<|im_start|>user
Explain QKV<|im_end|>
<|im_start|>assistant

### Assistant: I am StableVicuna, a large language model created by CarperAI. I am here to chat!

### Human: Explain QKV
### Assistant:

[Round <|round|>]
问：Explain QKV
答：

[Round <|round|>]
Question：Explain QKV
Answer：

Question：Explain QKV
Answer：

Using Exllamav2_HF set alpha=2.5 for 16K Context

Users also report that exllamav2_HF loader, 8bpw-h8 exl2 quant, simple-1 preset provides good results

Framework versions

Transformers 4.35.0-UNA
Pytorch 2.1.0
Datasets 2.14.6
Tokenizers 0.14.1

Citations

If you find Cybertron, Juanako or any of our models useful, specially if you use it for your big brand.. or you clone/merge my modelsm, cite please:

@misc{unacybertron7b,
  title={Cybertron: Uniform Neural Alignment}, 
  author={Xavier Murias},
  year={2023},
  publisher = {HuggingFace},
  journal = {HuggingFace repository},
  howpublished = {\url{https://huggingface.co/fblgit/una-cybertron-7b-v2-bf16}},
}

Special thanks to @TheBloke & @bartowski for converting the models and their support to the community. Thank you!

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	69.67
AI2 Reasoning Challenge (25-Shot)	68.26
HellaSwag (10-Shot)	85.85
MMLU (5-Shot)	63.23
TruthfulQA (0-shot)	64.63
Winogrande (5-shot)	80.98
GSM8k (5-shot)	55.04