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 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
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-### Model Sources [optional]
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-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
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-### Downstream Use [optional]
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-### Out-of-Scope Use
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-## Bias, Risks, and Limitations
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-### Recommendations
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
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-## Training Details
-### Training Data
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-### Training Procedure
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-#### Training Hyperparameters
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-#### Speeds, Sizes, Times [optional]
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-## Evaluation
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-### Testing Data, Factors & Metrics
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-#### Factors
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-#### Metrics
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-### Results
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-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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-## Technical Specifications [optional]
-### Model Architecture and Objective
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-## Citation [optional]
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 ---
 library_name: transformers
+tags:
+- chemistry
+- biology
+- finance
+- legal
+- music
+- art
+- code
+- climate
+- medical
+- agent
+- text-generation-inference
+- Duchifat-2
+- conversational
+- chat
+- SFT
+license: apache-2.0
+language:
+- he
+- en
+base_model:
+- Raziel1234/Duchifat-2
+pipeline_tag: text-generation
 ---
+# 🕊️ Duchifat-2.2-Instruct
+**Duchifat-2.2-Instruct** is a fine-tuned version of the original **Duchifat-2 base model**. While this specific version is an optimized Instruct/Chat model, the underlying base architecture and weights were developed and trained from scratch by **Raziel**.
+## 🚀 Lineage & Development
+- **Base Model (Duchifat-2):** Built and pre-trained from scratch on **3.27 Billion tokens** (50/50 Hebrew-English C4 dataset). It features 136M parameters and was designed to establish a native Hebrew reasoning foundation.
+- **Version 2.2 (Instruct):** A refined fine-tuned version (SFT) designed to transform the base capabilities into a quirky, safe, and highly responsive conversational agent.
+### Key Features:
+* **Native Hebrew Foundation:** Unlike models that adapt English weights, Duchifat was born in Hebrew using the **DictaLM tokenizer**, ensuring high efficiency and natural linguistic flow.
+* **Compact Power:** At only 136M parameters, it delivers impressive performance while remaining small enough for edge deployment and low-latency applications.
+* **Quirky & Human-like:** The SFT process focused on giving the model a distinct personality—witty and engaging rather than robotic.
+* **Safety Integrated:** Built-in guardrails ensure the model remains professional and refuses to engage with profanity or offensive prompts.
+## 📊 Benchmark Results (Zero-Shot)
+Tested using manual prompt formatting to accurately reflect real-world chat performance.
+| Task | Version | Filter | n-shot | Metric | Value | Stderr |
+| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
+| **piqa** | 1 | none | 0 | acc | **0.70** | ± 0.1528 |
+| **piqa** | 1 | none | 0 | acc_norm | **0.70** | ± 0.1528 |
+| **hellaswag** | 1 | none | 0 | acc | **0.40** | ± 0.1633 |
+| **hellaswag** | 1 | none | 0 | acc_norm | **0.40** | ± 0.1633 |
+| **winogrande** | 1 | none | 0 | acc | **0.40** | ± 0.1633 |
+| **arc_easy** | 1 | none | 0 | acc | **0.10** | ± 0.1000 |
+| **arc_easy** | 1 | none | 0 | acc_norm | **0.10** | ± 0.1000 |
+## 🛠️ Technical Specifications
+* **Parameters:** 136M
+* **Base Pre-training Data:** 3.27B tokens (C4 Hebrew/English)
+* **Tokenizer:** DictaLM (Hebrew optimized)
+* **Context Window:** 1024 tokens
+## 💡 How to Use
+Use the following instruction format to trigger the Instruct-tuned behavior:
+Prompt Template:
+<|instruction|>
+{user_query}
+<|assistant|>
+Example Usage:
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model_id = "razielAI/Duchifat-2.2-Instruct"
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True).to("cuda")
+prompt = "<|instruction|>\nשלום!\n<|assistant|>\n"
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+output = model.generate(**inputs, max_new_tokens=256, temperature=0.7, do_sample=True)
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+## ⚠️ Limitations
+Duchifat-2.2 is a lightweight model. It excels at conversational tasks, social media content, and short-form text generation. It is not designed for complex mathematical proofs or extensive coding sessions.
+## 🕊️ About the Duchifat Project
+The Duchifat (Hoopoe) project is dedicated to creating efficient, open-source AI with a native understanding of the Hebrew language and culture.