---
language:
- he
- en
license: llama3.2
base_model: meta-llama/Llama-3.2-1B-Instruct
tags:
- llama-3.2
- hebrew
- instruction-tuned
- sft
- safetensors
- nlp
model_name: Hebrew-GPT
model_type: causal-lm
precision: bfloat16
---

# Hebrew-GPT: Specialized 1B Hebrew Instruction Model 

**Hebrew-GPT** is a state-of-the-art, instruction-tuned Small Language Model (SLM) based on the **Llama-3.2-1B** architecture. It has been engineered to bridge the gap in low-parameter Hebrew linguistic performance, providing a compact yet powerful solution for Hebrew natural language understanding and generation.



---

## 💎 Model Highlights

* **Linguistic Specialization:** Specifically tuned to handle the Morphologically Rich Language (MRL) features of Hebrew, including prefix-suffix handling and correct right-to-left (RTL) context awareness.
* **16-bit Precision:** Unlike many quantized small models, this version features **Full Merged BFloat16 weights**, ensuring no loss of intelligence from the fine-tuning process.
* **Instruction Optimized:** Trained specifically to follow complex prompts, summarize documents, and engage in dialogue, rather than just basic text completion.
* **Efficiency:** At 1 billion parameters, it is optimized for edge deployment, providing high-speed inference on standard consumer hardware.

---

## 🛠 Technical Specifications

### Architecture
- **Base Architecture:** Llama 3.2
- **Parameters:** 1.23 Billion
- **Context Length:** 128k tokens (native support)
- **Weight Format:** Safetensors (Standalone)
- **Precision:** BFloat16 ($BF16$)

### Training Methodology
The model underwent **Supervised Fine-Tuning (SFT)** using a curated multi-source dataset strategy to ensure high-quality Hebrew output without compromising logical reasoning:
* **Hebrew Instruction Set (70%):** Extensive Alpaca-formatted datasets translated and corrected for Hebrew grammar.
* **Hebrew Contextual Knowledge (20%):** Fact-based data from Hebrew wikis and structured Q&A.
* **Logic Preservation (10%):** High-quality English instructional data to maintain cross-lingual reasoning and mathematical stability.

---

## 📈 Performance & Monitoring

During the development phase, the model was monitored via detailed telemetry to ensure stable convergence. Key metrics tracked included:
- **Gradient Norm Stability:** Monitored to prevent exploding gradients in RTL text generation.
- **VRAM Optimization:** Efficiently managed to maximize batch size and learning stability.
- **Loss Decay:** Consistent downward trend in cross-entropy loss across all three data streams.



---

## 🚀 Quick Start Guide

### Installation
```bash
pip install transformers torch accelerate
``` 
### Basic Usage (Python)
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "XythicK/Hebrew-GPT"

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Standard Llama-3.2 Chat Template
messages = [
    {"role": "system", "content": "אתה עוזר חכם ומקצועי בעברית."},
    {"role": "user", "content": "כתוב לי מתכון קצר לחלה לשבת."},
]

input_ids = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

outputs = model.generate(
    input_ids,
    max_new_tokens=256,
    do_sample=True,
    temperature=0.7,
    top_p=0.9,
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
### ⚖️ Ethics and Limitations
While Hebrew-GPT is highly capable for its size, users should note:

Hallucination: Like all LLMs, it can generate incorrect facts. Verify critical information.

Bias: The model reflects the biases present in its training data.

Parameter Constraints: As a 1B model, it may struggle with highly technical academic subjects compared to 70B+ models.