Instructions to use razielAI/Duchifat-2-Instruct-Thinking with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use razielAI/Duchifat-2-Instruct-Thinking with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="razielAI/Duchifat-2-Instruct-Thinking", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("razielAI/Duchifat-2-Instruct-Thinking", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use razielAI/Duchifat-2-Instruct-Thinking with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "razielAI/Duchifat-2-Instruct-Thinking"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "razielAI/Duchifat-2-Instruct-Thinking",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/razielAI/Duchifat-2-Instruct-Thinking

SGLang

How to use razielAI/Duchifat-2-Instruct-Thinking with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "razielAI/Duchifat-2-Instruct-Thinking" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "razielAI/Duchifat-2-Instruct-Thinking",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "razielAI/Duchifat-2-Instruct-Thinking" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "razielAI/Duchifat-2-Instruct-Thinking",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use razielAI/Duchifat-2-Instruct-Thinking with Docker Model Runner:
```
docker model run hf.co/razielAI/Duchifat-2-Instruct-Thinking
```

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:

### instruction:
{Your Question}

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

### output:

Usage Example

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}
}

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Safetensors

Model size

0.1B params

Tensor type

F32

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