| --- |
| 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} |
| } |
| ``` |
