Create README.md

README.md

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+---
+license: apache-2.0
+pipeline_tag: text-generation
+language:
+  - en
+  - he
+tags:
+- pretrained
+inference:
+  parameters:
+    temperature: 0.7
+---
+
+[<img src="dicta-logo.jpg" width="300px"/>](https://dicta.org.il)
+
+
+# Model Card for DictaLM-2.0-AWQ
+
+The DictaLM-2.0 Large Language Model (LLM) is a pretrained generative text model with 7 billion parameters specializing in Hebrew. 
+
+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 base model [DictaLM-2.0](https://huggingface.co/dicta-il/dictalm2.0).
+
+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).
+
+## Example Code
+
+Running this code requires ~5.1GB of GPU VRAM.
+
+```python
+from transformers import pipeline
+
+# This loads the model onto the GPU in bfloat16 precision
+model = pipeline('text-generation', 'dicta-il/dictalm2.0-GPTQ', device_map='cuda')
+
+# Sample few shot examples
+prompt = """
+עבר: הלכתי
+עתיד: אלך
+
+עבר: שמרתי
+עתיד: אשמור
+
+עבר: שמעתי
+עתיד: אשמע
+
+עבר: הבנתי
+עתיד:
+"""
+
+print(model(prompt.strip(), do_sample=False, max_new_tokens=4, stop_sequence='\n'))
+# [{'generated_text': 'עבר: הלכתי\nעתיד: אלך\n\nעבר: שמרתי\nעתיד: אשמור\n\nעבר: שמעתי\nעתיד: אשמע\n\nעבר: הבנתי\nעתיד: אבין\n\n'}]
+```
+
+## Model Architecture
+
+DictaLM-2.0 is based on the [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) model with the following changes:
+- An extended tokenizer with tokens for Hebrew, increasing the compression ratio
+- Continued pretraining on over 190B tokens of naturally occuring text, 50% Hebrew and 50% English.
+
+## Notice
+
+DictaLM 2.0 is a pretrained base model and therefore does not have any moderation mechanisms.
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+[Will be added soon]
+```