Instructions to use rayistern/Hebrew-Mistral-7B-textembed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rayistern/Hebrew-Mistral-7B-textembed with Transformers:

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

pipe = pipeline("text-generation", model="rayistern/Hebrew-Mistral-7B-textembed")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rayistern/Hebrew-Mistral-7B-textembed")
model = AutoModelForCausalLM.from_pretrained("rayistern/Hebrew-Mistral-7B-textembed")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use rayistern/Hebrew-Mistral-7B-textembed with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/rayistern/Hebrew-Mistral-7B-textembed

SGLang

How to use rayistern/Hebrew-Mistral-7B-textembed 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 "rayistern/Hebrew-Mistral-7B-textembed" \
    --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": "rayistern/Hebrew-Mistral-7B-textembed",
		"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 "rayistern/Hebrew-Mistral-7B-textembed" \
        --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": "rayistern/Hebrew-Mistral-7B-textembed",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use rayistern/Hebrew-Mistral-7B-textembed with Docker Model Runner:
```
docker model run hf.co/rayistern/Hebrew-Mistral-7B-textembed
```

Duplicated from the below; modified for embedding usage

Hebrew-Mistral-7B

Hebrew-Mistral-7B is an open-source Large Language Model (LLM) pretrained in hebrew and english pretrained with 7B billion parameters, based on Mistral-7B-v1.0 from Mistral.

It has an extended hebrew tokenizer with 64,000 tokens and is continuesly pretrained from Mistral-7B on tokens in both English and Hebrew.

The resulting model is a powerful general-purpose language model suitable for a wide range of natural language processing tasks, with a focus on Hebrew language understanding and generation.

Usage

Below are some code snippets on how to get quickly started with running the model.

First make sure to pip install -U transformers, then copy the snippet from the section that is relevant for your usecase.

Running on CPU

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("yam-peleg/Hebrew-Mistral-7B")
model = AutoModelForCausalLM.from_pretrained("yam-peleg/Hebrew-Mistral-7B")

input_text = "שלום! מה שלומך היום?"
input_ids = tokenizer(input_text, return_tensors="pt")

outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))

Running on GPU

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("yam-peleg/Hebrew-Mistral-7B")
model = AutoModelForCausalLM.from_pretrained("yam-peleg/Hebrew-Mistral-7B", device_map="auto")

input_text = "שלום! מה שלומך היום?"
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))

Running with 4-Bit precision

from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig

tokenizer = AutoTokenizer.from_pretrained("yam-peleg/Hebrew-Mistral-7B")
model = AutoModelForCausalLM.from_pretrained("yam-peleg/Hebrew-Mistral-7B", quantization_config = BitsAndBytesConfig(load_in_4bit=True))

input_text = "שלום! מה שלומך היום?"
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0])

Notice

Hebrew-Mistral-7B 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

8B params

Tensor type

BF16