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---
library_name: transformers
tags:
- CoT
- Code
license: apache-2.0
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
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"
)
``` |