Instructions to use ziadrone/oneplusaries55 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ziadrone/oneplusaries55 with Transformers:

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

pipe = pipeline("text-generation", model="ziadrone/oneplusaries55")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ziadrone/oneplusaries55")
model = AutoModelForCausalLM.from_pretrained("ziadrone/oneplusaries55")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ziadrone/oneplusaries55 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ziadrone/oneplusaries55"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ziadrone/oneplusaries55",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ziadrone/oneplusaries55

SGLang

How to use ziadrone/oneplusaries55 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 "ziadrone/oneplusaries55" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ziadrone/oneplusaries55",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "ziadrone/oneplusaries55" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ziadrone/oneplusaries55",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ziadrone/oneplusaries55 with Docker Model Runner:
```
docker model run hf.co/ziadrone/oneplusaries55
```

ToT-Reasoner-Qwen3-1.7B

Model Description

This model is a fine-tuned version of Qwen/Qwen3-1.7B using Supervised Fine-Tuning (SFT) on the HuggingFaceH4/MATH-500 dataset. It is optimized for mathematical reasoning and problem-solving tasks. The fine-tuning process was performed by EKAGRATA TECH PRIVATE LIMITED.

Training Data

Source: HuggingFaceH4/MATH-500 (50 samples).
Format: Prompts with <reasoning>...</reasoning><answer>...</answer> structure.

Fine-Tuning Process

Method: Incremental SFT with learning rate=1e-5, 1 epoch per batch, batch size=10.
Setup: Google Colab Pro with A100 GPU.
Date and Time: Model uploaded at 07:23 AM on Friday, July 04, 2025.

Usage

To use this model for mathematical reasoning:

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("ziadrone/oneplusaries55")
tokenizer = AutoTokenizer.from_pretrained("ziadrone/oneplusaries55")

SYSTEM_PROMPT = """You are a large language model trained to solve mathematical, logical, physics, and general reasoning problems. You must follow the following steps to solve the problem:
1. Carefully analyze the question and identify the key information.
2. Develop a clear and concise plan to approach the problem.
3. Execute your plan step-by-step, providing detailed explanations and intermediate calculations.
4. Verify your solution to ensure it is accurate and makes sense in the context of the problem.
5. Present your final answer in a clear and concise format.
6. Always enclose the reasoning process within <reasoning>...</reasoning> tags.
7. Always enclose the final answer within <answer>...</answer> tags.
8. Do not use any other tags besides <reasoning> and <answer>.
9. Do not include any extra information outside of the reasoning or answer tags."""

prompt = f"SYSTEM: {SYSTEM_PROMPT}\nUSER: Solve the equation 2x + 3 = 7."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Performance

This model has been fine-tuned on mathematical reasoning tasks and should perform well on similar problems involving step-by-step logical reasoning.

Limitations

The model was trained on a limited dataset (50 samples)
Performance may vary on problems significantly different from the training data
Always verify mathematical results for critical applications

License

This model is released under the Apache 2.0 license.

Downloads last month: 2

Safetensors

Model size

2B params

Tensor type

F16

Model tree for ziadrone/oneplusaries55

Base model

Qwen/Qwen3-1.7B-Base

Finetuned

Qwen/Qwen3-1.7B