---
license: apache-2.0
language:
- he
- en
base_model:
- Raziel1234/Duchifat-2
pipeline_tag: text-generation
tags:
- text-generation-inference
- chemistry
- biology
- music
- legal
- medical
- agent
- finance
- climate
- PyTorch-Lightning
- PyTorch
library_name: transformers
---

# Duchifat-2-Thinking 🛰️

**Duchifat-2-Thinking** is a lightweight, efficient Language Model (136M parameters) specifically fine-tuned for **Reasoning tasks** and **Instruction Following**. It utilizes a unique Triple-Prompt architecture (Instruction-Thought-Output) to ensure high-quality, focused, and logical responses.

## Model Details
- **Developed by:** Raziel / TopAI
- **Model type:** Causal Language Model (Transformer)
- **Language(s):** English (Primary), Hebrew (Identity)
- **License:** Apache 2.0
- **Base Model:** Duchifat-2 (136M)
- **Training Technique:** SFT (Supervised Fine-Tuning) with Chain-of-Thought Alignment.

## Key Features
- **Triple-Prompt Architecture:** Designed to process an internal "Thought" block before generating the final output.
- **Efficient Reasoning:** Optimized for CPU and low-resource environments without sacrificing logical consistency.
- **Clean Output:** Significantly reduced hallucination and "word salad" compared to standard small models.

## Prompt Format
To get the best results, use the following structured prompt:

```text
### instruction:
{Your Question}

### thought:
{The logic or reasoning the model should follow}

### output:
```

### Usage Example
``` python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "razielAI/Duchifat-2-Instruct-Thinking" # Update with your exact HF path
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float32)

instruction = "Who are you?"
thought = "The user is asking for my identity. I should state I am Duchifat-2 developed by TopAI."

prompt = f"### instruction:\n{instruction}\n\n### thought:\n{thought}\n\n### output:\n"
inputs = tokenizer(prompt, return_tensors="pt")

outputs = model.generate(**inputs, max_new_tokens=100, temperature=0.1)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

### Performance & Limitations

Duchifat-2-Instruct-Thinking is a **Small Language Model (SLM). While it excels at structured tasks and guided reasoning:

- It may require a guided thought block for highly complex logic.

- Best used with low temperature (0.1 - 0.3) for factual consistency.


### Citation

If you use this model in your research or project, please cite:

```
Plaintext
@misc{duchifat2thinking2026,
  author = {Raziel, TopAI},
  title = {Duchifat-2-Thinking: A Lightweight Reasoning Model},
  year = {2026},
  publisher = {Hugging Face},
  journal = {Hugging Face Model Hub}
}
```