Quantization made by Richard Erkhov.

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Hebrew-Gemma-11B-Instruct - GGUF
- Model creator: https://huggingface.co/yam-peleg/
- Original model: https://huggingface.co/yam-peleg/Hebrew-Gemma-11B-Instruct/


| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Hebrew-Gemma-11B-Instruct.Q2_K.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Hebrew-Gemma-11B-Instruct-gguf/blob/main/Hebrew-Gemma-11B-Instruct.Q2_K.gguf) | Q2_K | 3.9GB |
| [Hebrew-Gemma-11B-Instruct.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Hebrew-Gemma-11B-Instruct-gguf/blob/main/Hebrew-Gemma-11B-Instruct.IQ3_XS.gguf) | IQ3_XS | 4.27GB |
| [Hebrew-Gemma-11B-Instruct.IQ3_S.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Hebrew-Gemma-11B-Instruct-gguf/blob/main/Hebrew-Gemma-11B-Instruct.IQ3_S.gguf) | IQ3_S | 4.48GB |
| [Hebrew-Gemma-11B-Instruct.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Hebrew-Gemma-11B-Instruct-gguf/blob/main/Hebrew-Gemma-11B-Instruct.Q3_K_S.gguf) | Q3_K_S | 4.48GB |
| [Hebrew-Gemma-11B-Instruct.IQ3_M.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Hebrew-Gemma-11B-Instruct-gguf/blob/main/Hebrew-Gemma-11B-Instruct.IQ3_M.gguf) | IQ3_M | 4.63GB |
| [Hebrew-Gemma-11B-Instruct.Q3_K.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Hebrew-Gemma-11B-Instruct-gguf/blob/main/Hebrew-Gemma-11B-Instruct.Q3_K.gguf) | Q3_K | 4.94GB |
| [Hebrew-Gemma-11B-Instruct.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Hebrew-Gemma-11B-Instruct-gguf/blob/main/Hebrew-Gemma-11B-Instruct.Q3_K_M.gguf) | Q3_K_M | 4.94GB |
| [Hebrew-Gemma-11B-Instruct.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Hebrew-Gemma-11B-Instruct-gguf/blob/main/Hebrew-Gemma-11B-Instruct.Q3_K_L.gguf) | Q3_K_L | 5.33GB |
| [Hebrew-Gemma-11B-Instruct.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Hebrew-Gemma-11B-Instruct-gguf/blob/main/Hebrew-Gemma-11B-Instruct.IQ4_XS.gguf) | IQ4_XS | 5.44GB |
| [Hebrew-Gemma-11B-Instruct.Q4_0.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Hebrew-Gemma-11B-Instruct-gguf/blob/main/Hebrew-Gemma-11B-Instruct.Q4_0.gguf) | Q4_0 | 5.68GB |
| [Hebrew-Gemma-11B-Instruct.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Hebrew-Gemma-11B-Instruct-gguf/blob/main/Hebrew-Gemma-11B-Instruct.IQ4_NL.gguf) | IQ4_NL | 5.72GB |
| [Hebrew-Gemma-11B-Instruct.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Hebrew-Gemma-11B-Instruct-gguf/blob/main/Hebrew-Gemma-11B-Instruct.Q4_K_S.gguf) | Q4_K_S | 5.72GB |
| [Hebrew-Gemma-11B-Instruct.Q4_K.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Hebrew-Gemma-11B-Instruct-gguf/blob/main/Hebrew-Gemma-11B-Instruct.Q4_K.gguf) | Q4_K | 6.04GB |
| [Hebrew-Gemma-11B-Instruct.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Hebrew-Gemma-11B-Instruct-gguf/blob/main/Hebrew-Gemma-11B-Instruct.Q4_K_M.gguf) | Q4_K_M | 6.04GB |
| [Hebrew-Gemma-11B-Instruct.Q4_1.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Hebrew-Gemma-11B-Instruct-gguf/blob/main/Hebrew-Gemma-11B-Instruct.Q4_1.gguf) | Q4_1 | 6.25GB |
| [Hebrew-Gemma-11B-Instruct.Q5_0.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Hebrew-Gemma-11B-Instruct-gguf/blob/main/Hebrew-Gemma-11B-Instruct.Q5_0.gguf) | Q5_0 | 6.81GB |
| [Hebrew-Gemma-11B-Instruct.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Hebrew-Gemma-11B-Instruct-gguf/blob/main/Hebrew-Gemma-11B-Instruct.Q5_K_S.gguf) | Q5_K_S | 6.81GB |
| [Hebrew-Gemma-11B-Instruct.Q5_K.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Hebrew-Gemma-11B-Instruct-gguf/blob/main/Hebrew-Gemma-11B-Instruct.Q5_K.gguf) | Q5_K | 7.0GB |
| [Hebrew-Gemma-11B-Instruct.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Hebrew-Gemma-11B-Instruct-gguf/blob/main/Hebrew-Gemma-11B-Instruct.Q5_K_M.gguf) | Q5_K_M | 7.0GB |
| [Hebrew-Gemma-11B-Instruct.Q5_1.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Hebrew-Gemma-11B-Instruct-gguf/blob/main/Hebrew-Gemma-11B-Instruct.Q5_1.gguf) | Q5_1 | 7.37GB |
| [Hebrew-Gemma-11B-Instruct.Q6_K.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Hebrew-Gemma-11B-Instruct-gguf/blob/main/Hebrew-Gemma-11B-Instruct.Q6_K.gguf) | Q6_K | 8.01GB |
| [Hebrew-Gemma-11B-Instruct.Q8_0.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Hebrew-Gemma-11B-Instruct-gguf/blob/main/Hebrew-Gemma-11B-Instruct.Q8_0.gguf) | Q8_0 | 10.37GB |




Original model description:
---
license: other
license_name: gemma-terms-of-use
license_link: https://ai.google.dev/gemma/terms
language:
- en
- he
library_name: transformers
---
# Hebrew-Gemma-11B-Instruct

### Base Models:
- **07.03.2024:** [Hebrew-Gemma-11B](https://huggingface.co/yam-peleg/Hebrew-Gemma-11B)
- **16.03.2024:** [Hebrew-Gemma-11B-V2](https://huggingface.co/yam-peleg/Hebrew-Gemma-11B-V2)

### Instruct Models:
- **07.03.2024:** [Hebrew-Gemma-11B-Instruct](https://huggingface.co/yam-peleg/Hebrew-Gemma-11B-Instruct)

The Hebrew-Gemma-11B-Instruct Large Language Model (LLM) is a instruct fine-tuned version of the [Hebrew-Gemma-11B](https://huggingface.co/yam-peleg/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:

```python
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.