|
|
--- |
|
|
license: apache-2.0 |
|
|
datasets: |
|
|
- simplescaling/aime24_figures |
|
|
- amphora/QwQ-LongCoT-130K |
|
|
- HuggingFaceH4/MATH-500 |
|
|
- RyotaKadoya1993/math-5000-nemotron-v2 |
|
|
language: |
|
|
- en |
|
|
base_model: |
|
|
- Qwen/Qwen2-1.5B-Instruct |
|
|
pipeline_tag: text-generation |
|
|
library_name: transformers |
|
|
tags: |
|
|
- text-generation-inference |
|
|
- thinker |
|
|
- math |
|
|
--- |
|
|
|
|
|
 |
|
|
|
|
|
# **Open-Xi-Math-Preview** |
|
|
|
|
|
> **Open-Xi-Math-Preview** is a **mathematics-focused reasoning model** fine-tuned on **Qwen2-1.5B-Instruct**, utilizing a **modular dataset** designed for enhancing **mathematical thinking**. It provides robust capabilities in symbolic reasoning, structured deduction, and compact coding — optimized for edge deployment on **resource-constrained devices**. |
|
|
|
|
|
## **Key Improvements** |
|
|
|
|
|
1. **Mathematical Reasoning via Modular Data**: |
|
|
Fine-tuned on diverse and structured math-focused datasets to handle problem-solving, symbolic computation, and multi-step derivations with efficiency on low-power devices. |
|
|
|
|
|
2. **Compact Coding & Math Assistant**: |
|
|
Understands multiple programming languages and math representations (e.g., LaTeX, symbolic algebra). Ideal for math-enhanced embedded coding and problem-solving environments. |
|
|
|
|
|
3. **Error Detection in Structured Data**: |
|
|
Accurately detects and corrects logical errors, malformed math expressions, and data structures (e.g., JSON, XML, LaTeX), all while maintaining low inference latency. |
|
|
|
|
|
4. **Instruction Following for Problem-Solving**: |
|
|
Enhanced with strong instruction-following performance, particularly for step-wise solutions in math word problems, logic puzzles, and equation derivations. |
|
|
|
|
|
5. **Extended Context Support**: |
|
|
Supports **128K token inputs** and **8K token outputs**, enabling it to work with long math chains-of-thought and proofs, while remaining lightweight enough for edge inference. |
|
|
|
|
|
## **Quickstart with Transformers** |
|
|
|
|
|
```python |
|
|
from transformers import AutoModelForCausalLM, AutoTokenizer |
|
|
|
|
|
model_name = "your-username/Open-Xi-Math-Preview" |
|
|
|
|
|
model = AutoModelForCausalLM.from_pretrained( |
|
|
model_name, |
|
|
torch_dtype="auto", |
|
|
device_map="auto" |
|
|
) |
|
|
tokenizer = AutoTokenizer.from_pretrained(model_name) |
|
|
|
|
|
prompt = "Solve the equation: 2x^2 - 4x - 6 = 0. Show all steps." |
|
|
messages = [ |
|
|
{"role": "system", "content": "You are a helpful and concise mathematical reasoning assistant."}, |
|
|
{"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** |
|
|
|
|
|
1. **Math-Centric Edge Applications**: |
|
|
Designed for embedded AI systems in calculators, educational tools, and mobile math tutoring. |
|
|
|
|
|
2. **Advanced Math Reasoning**: |
|
|
Effective for solving algebra, geometry, calculus, and competition math problems using logical derivation. |
|
|
|
|
|
3. **Educational & Instructional Aids**: |
|
|
Useful for step-by-step teaching in math-heavy domains like STEM education, coding classes, and robotics kits. |
|
|
|
|
|
4. **Low-Latency Math Agents**: |
|
|
Deployable in customer support bots, interactive kiosks, and STEM-based IoT systems for fast math-based interactions. |
|
|
|
|
|
5. **Structured Output Generation**: |
|
|
Generates LaTeX, JSON, or tabular formats for math answers and reasoning in structured pipelines. |
|
|
|
|
|
## **Limitations** |
|
|
|
|
|
1. **Edge Hardware Still Required**: |
|
|
Though lightweight, best used with devices equipped with NPUs, GPUs, or optimized ML accelerators. |
|
|
|
|
|
2. **No Internet or Real-Time Info**: |
|
|
Static knowledge cutoff; cannot retrieve or interact with live external data sources. |
|
|
|
|
|
3. **Not Suited for Creative Tasks**: |
|
|
Focused on deterministic reasoning — not built for abstract, poetic, or generative creative writing. |
|
|
|
|
|
4. **Prompt Sensitivity**: |
|
|
Clear, structured prompts yield more accurate reasoning; ambiguous questions may degrade output quality. |
|
|
|
|
|
5. **Potential Dataset Biases**: |
|
|
Model may carry forward biases or inconsistencies present in the training datasets; vet outputs in critical settings. |
