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

Libraries

How to use SicariusSicariiStuff/Zion_Alpha with Transformers:

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

pipe = pipeline("text-generation", model="SicariusSicariiStuff/Zion_Alpha")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SicariusSicariiStuff/Zion_Alpha")
model = AutoModelForCausalLM.from_pretrained("SicariusSicariiStuff/Zion_Alpha")

llama-cpp-python

How to use SicariusSicariiStuff/Zion_Alpha with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="SicariusSicariiStuff/Zion_Alpha",
	filename="Zion_Alpha-Q3_K_M.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Inference
Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use SicariusSicariiStuff/Zion_Alpha with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf SicariusSicariiStuff/Zion_Alpha:Q3_K_M
# Run inference directly in the terminal:
llama-cli -hf SicariusSicariiStuff/Zion_Alpha:Q3_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf SicariusSicariiStuff/Zion_Alpha:Q3_K_M
# Run inference directly in the terminal:
llama-cli -hf SicariusSicariiStuff/Zion_Alpha:Q3_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 SicariusSicariiStuff/Zion_Alpha:Q3_K_M
# Run inference directly in the terminal:
./llama-cli -hf SicariusSicariiStuff/Zion_Alpha:Q3_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 SicariusSicariiStuff/Zion_Alpha:Q3_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SicariusSicariiStuff/Zion_Alpha:Q3_K_M

Use Docker

docker model run hf.co/SicariusSicariiStuff/Zion_Alpha:Q3_K_M

LM Studio
Jan

vLLM

How to use SicariusSicariiStuff/Zion_Alpha with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/SicariusSicariiStuff/Zion_Alpha:Q3_K_M

SGLang

How to use SicariusSicariiStuff/Zion_Alpha 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 "SicariusSicariiStuff/Zion_Alpha" \
    --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": "SicariusSicariiStuff/Zion_Alpha",
		"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 "SicariusSicariiStuff/Zion_Alpha" \
        --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": "SicariusSicariiStuff/Zion_Alpha",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use SicariusSicariiStuff/Zion_Alpha with Ollama:
```
ollama run hf.co/SicariusSicariiStuff/Zion_Alpha:Q3_K_M
```

Unsloth Studio new

How to use SicariusSicariiStuff/Zion_Alpha with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for SicariusSicariiStuff/Zion_Alpha to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for SicariusSicariiStuff/Zion_Alpha to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for SicariusSicariiStuff/Zion_Alpha to start chatting

Docker Model Runner
How to use SicariusSicariiStuff/Zion_Alpha with Docker Model Runner:
```
docker model run hf.co/SicariusSicariiStuff/Zion_Alpha:Q3_K_M
```

Lemonade

How to use SicariusSicariiStuff/Zion_Alpha with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SicariusSicariiStuff/Zion_Alpha:Q3_K_M

Run and chat with the model

lemonade run user.Zion_Alpha-Q3_K_M

List all available models

lemonade list

Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Zion_Alpha

Model Details

Zion_Alpha is the first REAL Hebrew model in the world. It wasn't finetuned for any tasks yet, but it actually understands and comprehends Hebrew. It can even do some decent translation. I've tested GPT4 vs Zion_Alpha, and out of the box, Zion_Alpha did a better job translating. I did the finetune using SOTA techniques and using my insights from years of underwater basket weaving. If you wanna offer me a job, just add me on Facebook.

Future Plans

I plan to perform a SLERP merge with one of my other fine-tuned models, which has a bit more knowledge about Israeli topics. Additionally, I might create a larger model using MergeKit, but we'll see how it goes.

Looking for Sponsors

Since all my work is done on-premises, I am constrained by my current hardware. I would greatly appreciate any support in acquiring an A6000, which would enable me to train significantly larger models much faster.

Papers?

Maybe. We'll see. No promises here 🤓

Contact Details

I'm not great at self-marketing (to say the least) and don't have any social media accounts. If you'd like to reach out to me, you can email me at sicariussicariistuff@gmail.com. Please note that this email might receive more messages than I can handle, so I apologize in advance if I can't respond to everyone.

Versions and QUANTS

Base model: FP16
Instruction tuned: FP16 | GGUF

Model architecture

Based on Mistral 7B. I didn't even bother to alter the tokenizer.

The recommended prompt setting is Debug-deterministic:

temperature: 1
top_p: 1
top_k: 1
typical_p: 1
min_p: 1
repetition_penalty: 1

The recommended instruction template is Mistral:

{%- for message in messages %}
    {%- if message['role'] == 'system' -%}
        {{- message['content'] -}}
    {%- else -%}
        {%- if message['role'] == 'user' -%}
            {{-'[INST] ' + message['content'].rstrip() + ' [/INST]'-}}
        {%- else -%}
            {{-'' + message['content'] + '</s>' -}}
        {%- endif -%}
    {%- endif -%}
{%- endfor -%}
{%- if add_generation_prompt -%}
    {{-''-}}
{%- endif -%}

English to hebrew example:

Zion_Alpha English to Hebrew example

English to hebrew example:

Zion_Alpha Hebrew to English example

Unscripted video: live zero shot demonstration at story writing capabilities in Hebrew

Zion_Alpha VS Mistral 'Hebrew' Live & unscripted in real time

Zion_Alpha VS Mistral 'Hebrew' Live & unscripted in real time Long text translation

History

The model was originally trained about 2 month after Mistral (v0.1) was released. As of 04 June 2024, Zion_Alpha got the Highest SNLI score in the world among open source models in Hebrew, surpassing most of the models by a huge margin. (84.05 score)

Citation Information

@llm{Zion_Alpha,
  author = {SicariusSicariiStuff},
  title = {Zion_Alpha},
  year = {2024},
  publisher = {Hugging Face},
  url = {https://huggingface.co/SicariusSicariiStuff/Zion_Alpha}
}

Benchmarks

Metric	Value
Avg.	19.11
IFEval (0-Shot)	33.24
BBH (3-Shot)	29.16
MATH Lvl 5 (4-Shot)	4.76
GPQA (0-shot)	5.37
MuSR (0-shot)	18.45
MMLU-PRO (5-shot)	23.68

Support

My Ko-fi page ALL donations will go for research resources and compute, every bit counts 🙏🏻
My Patreon ALL donations will go for research resources and compute, every bit counts 🙏🏻

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Safetensors

Model size

7B params

Tensor type

BF16

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Collection

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