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

Libraries

How to use SicariusSicariiStuff/Zion_Alpha_Instruction_Tuned with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("SicariusSicariiStuff/Zion_Alpha_Instruction_Tuned")
model = AutoModelForMultimodalLM.from_pretrained("SicariusSicariiStuff/Zion_Alpha_Instruction_Tuned")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SicariusSicariiStuff/Zion_Alpha_Instruction_Tuned with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/SicariusSicariiStuff/Zion_Alpha_Instruction_Tuned

SGLang

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

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

Zion_Alpha_Instruction_Tuned / README.md

SicariusSicariiStuff

Update README.md

f3044f7 verified 8 months ago

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metadata

language:
  - en
  - he
license: apache-2.0
widget:
  - text: Zion_Alpha
    output:
      url: >-
        https://huggingface.co/SicariusSicariiStuff/Zion_Alpha/resolve/main/Zion_Alpha.png

Zion_Alpha_Instruction_Tuned

Model Details

Zion_Alpha is the first REAL Hebrew model in the world. This version WAS fine tuned for tasks. 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_Instruction_Tuned,
  author = {SicariusSicariiStuff},
  title = {Zion_Alpha_Instruction_Tuned},
  year = {2024},
  publisher = {Hugging Face},
  url = {https://huggingface.co/SicariusSicariiStuff/Zion_Alpha_Instruction_Tuned}
}

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 🙏🏻