metadata
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 (NexusMind.tech)
Funded by: Tristian (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
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"
)