README.md

---
license: llama3.2
language:
- en
base_model:
- meta-llama/Llama-3.2-1B
pipeline_tag: text-generation
---
# Model Card for InfiR-1B-Instruct

<!-- Provide a quick summary of what the model is/does. -->

InfR aims to advance AI systems by improving reasoning, reducing adoption barriers, and addressing privacy concerns through smaller model sizes.

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->



- **Developed by:** InfiX
- **Language(s) (NLP):** English
- **Continual pretrained from model:** [[meta-llama/Llama-3.2-1B]](https://huggingface.co/meta-llama/Llama-3.2-1B)

### Model Sources

<!-- Provide the basic links for the model. -->

- **Repository:** [[github]](https://github.com/InfiXAI/InfiR)
- **Paper [optional]:** [[Arxiv]](https://arxiv.org/abs/2502.11573)

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

- **Performance gaps** remain vs. 70 B+ models on very hard reasoning (e.g., OlympiadBench).  
- **Safety & bias**: inherits Llama-3.2 tokenizer & pre-training distribution; may reflect web biases.  
- **Knowledge cut-off**: mid-2023.  
- **Evaluation** has focused on English benchmarks; multilingual robustness not verified.


## How to Get Started with the Model

### Installation

First, install the required dependencies:

```bash
pip install torch transformers
```

For optimal performance, we recommend using PyTorch 2.0+ and CUDA 11.8+.

### Basic Usage

Here's a simple example to get started with InfiR-1B-Instruct:

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

# Define messages in chat format
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "A new program had 60 downloads in the first month. The number of downloads in the second month was three times as many as the downloads in the first month, but then reduced by 30% in the third month. How many downloads did the program have total over the three months? Think step by step."},
]

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("InfiX-ai/InfiR-1B-Instruct")
model = AutoModelForCausalLM.from_pretrained("InfiX-ai/InfiR-1B-Instruct")

# Apply chat template and generate
raw_prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(raw_prompt, return_tensors="pt")
outputs = model.generate(inputs["input_ids"], max_new_tokens=2048)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

### Advanced Usage Examples

#### 1. Mathematical Reasoning

```python
# Mathematical problem solving with chat format
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "If a rectangle has a length of 8 units and a width of 6 units, what is its area and perimeter? Solve this step by step."},
]

raw_prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(raw_prompt, return_tensors="pt")
outputs = model.generate(
    inputs["input_ids"], 
    max_new_tokens=512,
    temperature=0.1,
    do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

#### 2. Code Generation

```python
# Code generation example with chat format
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Write a Python function to calculate the factorial of a number."},
]

raw_prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(raw_prompt, return_tensors="pt")
outputs = model.generate(
    inputs["input_ids"],
    max_new_tokens=256,
    temperature=0.2,
    do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

#### 3. Chain-of-Thought Reasoning

```python
# Chain-of-thought reasoning with chat format
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "A train travels 120 km in 2 hours. What is its speed in km/h? Let's approach this step by step."},
]

raw_prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(raw_prompt, return_tensors="pt")
outputs = model.generate(
    inputs["input_ids"],
    max_new_tokens=300,
    temperature=0.3,
    do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Training Details

### Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

| Stage | Tokens | Composition |
|-------|--------|-------------|
| Pre-training | 900 B | 52 % code, 48 % high-quality web (math, science, encyclopedic) |
| Annealing | 40 B | extra math & code + synthetic samples |
| SFT | ~4 M | Infinity-Instruct, Orca-AgentInstruct-1M, NuminaMath, ScaleQuest (filtered) |

Data cleaning: heuristic filters, MinHash de-duplication, 10-gram benchmark decontamination, reward-model rejection sampling.

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

| Hyper-parameter | Value |
|-----------------|-------|
| Precision | bf16 mixed |
| Optimizer | AdamW |
| LR (pre-train) | 1.4 e-3, cosine → 0 |
| LR (SFT) | 2 e-5, cosine w/ 10 % warm-up |
| Batch size | 2048 (pre-train), 128 (SFT) |
| Sequence len | 4096 |
| Epochs | 1 (pre-train), 1 (anneal), 4 (SFT) |
| GPUs | 64 × H800, 5760 GPU-hours total |

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Benchmarks & Results

| Benchmark | InfiR-1B-Instruct | Llama-3.2-1B-Instruct | Qwen-2.5-1.5B-Instruct |
|-----------|-------------------|------------------------|-------------------------|
| MMLU | 50.22 | 46.27 | 61.78 |
| GSM8K | 70.9 | 47.9 | 74.3 |
| MATH | 46.4 | 30.0 | 53.4 |
| HumanEval | 58.54 | 39.63 | 51.83 |
| MBPP | 56.03 | 49.03 | 56.81 |


## Technical Specifications

### Model Architecture and Objective

- Base: Llama-3.2-1B (32 layers, 32 heads, RoPE, GQA, 2 k ctx → 4 k extended)  

## Citation

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

```bibtex
@misc{xie2025infir,
  title={InfiR: Crafting Effective Small Language Models and Multimodal Small Language Models in Reasoning},
  author={Xie, Congkai and Cai, Shuo and Wang, Wenjun and others},
  year={2025},
  eprint={2502.11573},
  archivePrefix={arXiv},
  primaryClass={cs.CL}
}
```

**APA:**

Xie, C., Cai, S., Wang, W., et al. (2025). *InfiR: Crafting Effective Small Language Models and Multimodal Small Language Models in Reasoning*. arXiv:2502.11573.

---

## Glossary

- **SLM**: Small Language Model (<2 B parameters)  
- **CoT**: Chain-of-Thought prompting or training  
- **REC**: Renewable Energy Certificate  
- **PUE**: Power Usage Effectiveness (ratio of total facility power to IT power)

---