---
license: apache-2.0
pipeline_tag: text-generation
language:
  - en
  - he
tags:
- instruction-tuned
base_model: dicta-il/dictalm2.0
inference:
  parameters:
    temperature: 0.7
---

[<img src="https://i.ibb.co/5Lbwyr1/dicta-logo.jpg" width="300px"/>](https://dicta.org.il)


# Model Card for DictaLM-2.0-Instruct

The DictaLM-2.0-Instruct Large Language Model (LLM) is an instruct fine-tuned version of the [DictaLM-2.0](https://huggingface.co/dicta-il/dictalm2.0) generative model using a variety of conversation datasets.

For full details of this model please read our [release blog post](https://example.com).

This model contains the GPTQ 4-bit quantized version of the instruct-tuned model designed for chat [DictaLM-2.0-Instruct](https://huggingface.co/dicta-il/dictalm2.0-instruct).

You can view and access the full collection of base/instruct unquantized/quantized versions of `DictaLM-2.0` [here](https://huggingface.co/collections/dicta-il/dicta-lm-20-collection-661bbda397df671e4a430c27).

## Instruction format

In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.

E.g.
```
text = """<s>[INST] What is your favourite condiment? [/INST]
Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s>[INST] Do you have mayonnaise recipes? [/INST]"
```

This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method:

## Example Code

Running this code requires under 5GB of GPU VRAM.

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

device = "cuda" # the device to load the model onto

model = AutoModelForCausalLM.from_pretrained("dicta-il/dictalm2.0-instruct-GPTQ", device_map=device)
tokenizer = AutoTokenizer.from_pretrained("dicta-il/dictalm2.0-instruct-GPTQ")

messages = [
    {"role": "user", "content": "מה הרוטב אהוב עליך?"},
    {"role": "assistant", "content": "טוב, אני די מחבב כמה טיפות מיץ לימון סחוט טרי. זה מוסיף בדיוק את הכמות הנכונה של טעם חמצמץ לכל מה שאני מבשל במטבח!"},
    {"role": "user", "content": "האם יש לך מתכונים למיונז?"}
]

encoded = tokenizer.apply_chat_template(messages, return_tensors="pt").to(device)

generated_ids = model.generate(encoded, max_new_tokens=50, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
# <s> [INST] מה הרוטב אהוב עליך? [/INST]
# טוב, אני די מחבב כמה טיפות מיץ לימון סחוט טרי. זה מוסיף בדיוק את הכמות הנכונה של טעם חמצמץ לכל מה שאני מבשל במטבח!</s>  [INST] האם יש לך מתכונים למיונז? [/INST]
# בטח, הנה מתכון קל מאוד למיונז ביתי:
# 
# מרכיבים:
# - 2 ביצים גדולות
# - 1 כף חרדל דיז'ון
# - 2 כפות
# (it stopped early because we set max_new_tokens=50)
```

## Model Architecture

DictaLM-2.0-Instruct follows the [Zephyr-7B-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) recipe for fine-tuning an instruct model, with an extended instruct dataset for Hebrew.

## Limitations

The DictaLM 2.0 Instruct model is a demonstration that the base model can be fine-tuned to achieve compelling performance. 
It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to
make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.

## Citation

If you use this model, please cite:

```bibtex
[Will be added soon]
```