---
library_name: transformers
tags:
- CoT
- Code
license: apache-2.0
language:
- en
- zh
- ko
- ru
- de
base_model: Qwen/Qwen2.5-7B-Instruct
model_name: streamerbtw1002/Nexuim-R1-7B-Instruct
revision: main
---

## Model Details  

**Model Name:** streamerbtw1002/Nexuim-R1-7B-Instruct

**Developed by:** [James Phifer](https://nexusmind.tech/) (NexusMind.tech)  
**Funded by:** [Tristian](https://shuttleai.com/) (Shuttle.ai)  
**License:** Apache-2.0  
**Finetuned from:** Qwen/Qwen2.5-VL-7B-Instruct  
**Architecture:** Transformer-based LLM  

### Overview  
This model is designed to handle complex mathematical questions efficiently using Chain of Thought (CoT) reasoning.  

- **Capabilities:**  
  - General-purpose LLM  
  - Strong performance on multi-step reasoning tasks  
  - Able to respond to requests ethically while preventing human harm  

- **Limitations:**  
  - Not evaluated extensively  
  - May generate incorrect or biased outputs in certain contexts  

## Training Details  

**Dataset:** Trained on a **120k-line** CoT dataset for mathematical reasoning.  
**Training Hardware:** 1x A100 80GB GPU (Provided by Tristian at Shuttle.ai)  

## Evaluation  

**Status:** Not formally tested yet.  
**Preliminary Results:**  
- Provides detailed, well-structured answers  
- Performs well on long-form mathematical problems  

## Usage  
```python
from transformers import AutoConfig, AutoModel, AutoTokenizer

model_id = "streamerbtw1002/Nexuim-R1-7B-Instruct"

config = AutoConfig.from_pretrained(
  model_id,
  revision="main"
)
model = AutoModel.from_pretrained(
  model_id,
  revision="main"
)
tokenizer = AutoTokenizer.from_pretrained(
  model_id,
  revision="main"
)

```