README.md

---
license: apache-2.0
language:
- en
- he
library_name: transformers
---
# Hebrew-Mistral-7B-200K

> **Please note: There has been some issues reported about this model, updates coming soon.**

Hebrew-Mistral-7B-200K is an open-source Large Language Model (LLM) pretrained in hebrew and english pretrained with 7B billion parameters and with 200K context length, 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

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("yam-peleg/Hebrew-Mistral-7B-200K")
model = AutoModelForCausalLM.from_pretrained("yam-peleg/Hebrew-Mistral-7B-200K")

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

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("yam-peleg/Hebrew-Mistral-7B-200K")
model = AutoModelForCausalLM.from_pretrained("yam-peleg/Hebrew-Mistral-7B-200K", 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

```python
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig

tokenizer = AutoTokenizer.from_pretrained("yam-peleg/Hebrew-Mistral-7B-200K")
model = AutoModelForCausalLM.from_pretrained("yam-peleg/Hebrew-Mistral-7B-200K", 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-200K is a pretrained base model and therefore does not have any moderation mechanisms.

### Authors
- Trained by Yam Peleg.