Instructions to use yam-peleg/Hebrew-Gemma-11B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yam-peleg/Hebrew-Gemma-11B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="yam-peleg/Hebrew-Gemma-11B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("yam-peleg/Hebrew-Gemma-11B-Instruct")
model = AutoModelForCausalLM.from_pretrained("yam-peleg/Hebrew-Gemma-11B-Instruct")
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

vLLM

How to use yam-peleg/Hebrew-Gemma-11B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yam-peleg/Hebrew-Gemma-11B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yam-peleg/Hebrew-Gemma-11B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/yam-peleg/Hebrew-Gemma-11B-Instruct

SGLang

How to use yam-peleg/Hebrew-Gemma-11B-Instruct 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 "yam-peleg/Hebrew-Gemma-11B-Instruct" \
    --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": "yam-peleg/Hebrew-Gemma-11B-Instruct",
		"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 "yam-peleg/Hebrew-Gemma-11B-Instruct" \
        --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": "yam-peleg/Hebrew-Gemma-11B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use yam-peleg/Hebrew-Gemma-11B-Instruct with Docker Model Runner:
```
docker model run hf.co/yam-peleg/Hebrew-Gemma-11B-Instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Hebrew-Gemma-11B-Instruct

Base Models:

07.03.2024: Hebrew-Gemma-11B
16.03.2024: Hebrew-Gemma-11B-V2

Instruct Models:

07.03.2024: Hebrew-Gemma-11B-Instruct

The Hebrew-Gemma-11B-Instruct Large Language Model (LLM) is a instruct fine-tuned version of the Hebrew-Gemma-11B generative text model using a variety of conversation datasets.

It is continued pretrain of gemma-7b, extended to a larger scale and trained on 3B additional tokens of both English and Hebrew text data.

Instruction format

This format must be strictly respected, otherwise the model will generate sub-optimal outputs.

<bos><start_of_turn>user
Write a hello world program<end_of_turn>
<start_of_turn>model
Here is a simple hellow world program<end_of_turn><eos>

The conversation starts with <bos>.
Each turn is preceded by a <start_of_turn> delimiter and then the role of the entity (user or model).
Turns finish with the <end_of_turn> token.
Conversation finish with the <eos> token.

You can follow this format to build the prompt manually, if you need to do it without the tokenizer's chat template.

A simple example using the tokenizer's chat template:

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Hebrew-Gemma-11B-Instruct"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="cuda")

chat = [
    { "role": "user", "content": "כתוב קוד פשוט בפייתון שמדפיס למסך את התאריך של היום" },
]
prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)

Terms of Use

As an extention of Gemma-7B, this model is subject to the original license and terms of use by Google.

Benchmark Results

Coming Soon!

Notice

Hebrew-Gemma-11B is a pretrained base model and therefore does not have any moderation mechanisms.

Authors

Trained by Yam Peleg.
In collaboration with Jonathan Rouach and Arjeo, inc.

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Safetensors

Model size

10B params

Tensor type

F16

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Hebrew Models

Collection

Open source models pretrained in hebrew • 7 items • Updated May 5, 2024 • 17