---
license: other
library_name: transformers
base_model:
  - microsoft/NextCoder-7B
  - nvidia/OpenCodeReasoning-Nemotron-7B
  - Qwen/Qwen2.5-7B
  - Qwen/Qwen2.5-Coder-7B
tags:
  - qwen2
  - mergekit
  - merge
  - conversational
  - text-generation-inference
  - code
  - reasoning
  - withinusai
language:
  - en
datasets:
  - bigcode/commitpackft
  - microsoft/NextCoderDataset-Conversational
  - bigcode/starcoderdata
  - nvidia/OpenCodeReasoning
pipeline_tag: text-generation
---

# Next_Nemotron_Reasoning_Coder-7B

**Next_Nemotron_Reasoning_Coder-7B** is a merged 7B-class language model release from **WithIn Us AI**, designed for coding, conversational prompting, and reasoning-oriented text generation.

This repository is distributed as a standard **Transformers** checkpoint in **Safetensors** format and is positioned as a merge-based model that blends coding and reasoning-oriented upstream model traits.

## Model Summary

This model is intended for:

- code generation
- code explanation
- conversational assistant workflows
- reasoning-oriented prompting
- implementation planning
- developer support tasks
- general text generation experiments

The current repository metadata and README indicate that this model is a **merge model** built with **mergekit**.

## Base Model Lineage

The current README metadata lists the following upstream model references:

- `microsoft/NextCoder-7B`
- `nvidia/OpenCodeReasoning-Nemotron-7B`
- `Qwen/Qwen2.5-7B`
- `Qwen/Qwen2.5-Coder-7B`

These names are preserved here as listed in the repository metadata.

## Merge Details

According to the current README:

- this model is a **merge of pre-trained language models**
- it was created using **mergekit**
- the **SLERP** merge method was used
- the “Models Merged” section explicitly lists:
  - `nvidia-OpenCodeReasoning-Nemotron-7B`
  - `microsoft-NextCoder-7B`

The repository also includes a visible `mergekit_config.yml`, which supports the merge-based packaging of the release.

## Training Data / Dataset Lineage

The current repository metadata lists the following datasets:

- `bigcode/commitpackft`
- `microsoft/NextCoderDataset-Conversational`
- `bigcode/starcoderdata`
- `nvidia/OpenCodeReasoning`

These datasets suggest a mix of:

- code-focused training data
- conversational coding supervision
- general programming corpus material
- reasoning-oriented coding data

## Intended Use

Recommended use cases include:

- coding assistant experiments
- code drafting and rewriting
- explaining code and technical concepts
- debugging support
- reasoning-style prompt workflows
- local or hosted developer-assistant inference
- structured implementation planning

## Suggested Use Cases

This model can be useful for:

- generating utility functions and scripts
- explaining programming concepts
- proposing debugging steps
- creating technical plans
- answering developer questions
- assisting with code-oriented chat workflows

## Out-of-Scope Use

This model should not be relied on for:

- legal advice
- medical advice
- financial advice
- safety-critical automation
- autonomous production engineering without review
- security-critical code without expert validation

All generated code should be reviewed, tested, and validated before real-world deployment.

## Repository Contents

The repository currently includes standard Hugging Face model assets such as:

- `README.md`
- `added_tokens.json`
- `config.json`
- `mergekit_config.yml`
- `merges.txt`
- `model-00001-of-00004.safetensors`
- `model-00002-of-00004.safetensors`
- `model-00003-of-00004.safetensors`
- `model.safetensors.index.json`
- `special_tokens_map.json`
- `tokenizer.json`
- `tokenizer_config.json`

## Prompting Guidance

This model will usually work best with prompts that are:

- direct
- scoped to a clear task
- explicit about language or framework
- specific about whether code, explanation, or both are wanted
- structured when reasoning steps are needed

### Example prompt styles

**Code generation**
> Write a Python function that parses a JSON file, validates required keys, and returns cleaned records.

**Debugging**
> Explain why this code raises a KeyError and provide a safer corrected version.

**Implementation planning**
> Create a step-by-step plan for building a FastAPI service with authentication, logging, and tests.

**Reasoning-oriented coding**
> Compare two approaches for implementing caching in a Python API and recommend one.

## Strengths

This model may be especially useful for:

- blended coding + reasoning workflows
- chat-style developer assistance
- merge-model experimentation
- structured software-task prompting
- moderate-scale local or hosted inference
- practical code-oriented text generation

## Limitations

Like other merged 7B-class language models, this model may:

- hallucinate APIs or technical details
- generate incomplete or incorrect code
- produce insecure implementations
- make reasoning mistakes on long or complex tasks
- require prompt iteration for best results
- need human validation before real-world use

## Attribution

**WithIn Us AI** is the publisher of this merged model release.

Credit for upstream assets remains with their original creators. The repository metadata and README specifically reference:

- `microsoft/NextCoder-7B`
- `nvidia/OpenCodeReasoning-Nemotron-7B`
- `Qwen/Qwen2.5-7B`
- `Qwen/Qwen2.5-Coder-7B`

and the datasets:

- `bigcode/commitpackft`
- `microsoft/NextCoderDataset-Conversational`
- `bigcode/starcoderdata`
- `nvidia/OpenCodeReasoning`

## License

This draft uses:

- `license: other`

If you maintain this repo, replace this with the exact license terms you want displayed and make sure they align with any upstream obligations from the referenced source models and datasets.

## Acknowledgments

Thanks to:

- **WithIn Us AI**
- **Microsoft**
- **NVIDIA**
- **Qwen**
- **BigCode**
- the **mergekit** ecosystem
- the Hugging Face platform
- the broader open-source LLM community

## Disclaimer

This model may produce inaccurate, insecure, biased, incomplete, or misleading outputs. All important generations, especially code and technical guidance, should be reviewed and tested before real-world use.