TESS-QwenRe-1.5B

TESS-QwenRe-1.5B

TESS-QwenRe-1.5B is a chain-of-thought reasoning model, distilled from DeepSeek R1 1.5B and fine-tuned from Qwen-1.5B. It is designed to tackle mathematical problems in English and Chinese, with an emphasis on long-context reasoning and step-by-step explanations — ideal for tutoring, competitive exam preparation, and STEM education tools.

Key Features

1. Chain-of-Thought Math Reasoning
   Trained to generate intermediate reasoning steps, TESS-QwenRe-1.5B offers transparent and interpretable solutions for math problems — essential for educational clarity and verification.

2. Bilingual Support (English + Chinese)
   Supports mathematical problem solving and explanation in both English and Simplified Chinese, enabling global and bilingual learning applications.

3. Long-Context Problem Solving
   Specially optimized for solving multi-step, long-form math problems — perfect for word problems, reasoning chains, and competitive math exams.

4. Distilled from DeepSeek R1 1.5B
   Combines the reasoning capabilities of DeepSeek R1 with the lightweight and efficient architecture of Qwen-1.5B, delivering powerful results in a compact footprint.

5. Step-by-Step Explanations
   Mimics expert human problem solving with clear, structured steps that help learners follow along and develop understanding.

Quickstart with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/TESS-QwenRe-1.5B"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Solve: A train travels 180 km in 3 hours. What is its average speed?"
messages = [
    {"role": "system", "content": "You are a helpful tutor skilled in solving math problems with step-by-step explanations."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

Intended Use

• Math Tutoring Assistants: Ideal for school and exam-level math instruction with detailed explanations.
• Bilingual EdTech Apps: Useful in Chinese-English math learning platforms.
• STEM Reasoning Tasks: Reasoning support for science, engineering, and logical problem domains.
• Efficient LLM Deployments: Well-suited for on-device or browser-based reasoning agents.

Limitations

1. Specialized Domain:
   Tuned for math and logic; may be less effective in open-ended or creative tasks.

2. Compact Model Constraints:
   As a 1.5B parameter model, it may underperform on extremely complex or abstract problems versus larger models.

3. Inherited Bias:
   Distilled and fine-tuned from larger models; outputs should be monitored in sensitive contexts.

4. Prompt Dependency:
   Accurate and structured prompts lead to the best outcomes in problem-solving scenarios.