license: mit

🧠 Nexora-Qwen-Coder-4B

Compact Agentic Coding Model

Coding · Debugging · Tool Use · Structured Reasoning · Local AI

Nexora-Qwen-Coder-4B is a compact, coding-focused language model fine-tuned from the Qwen 3.5 4B family, with an emphasis on code generation, debugging, structured reasoning, tool use, and local agentic workflows.

The core idea behind Nexora is simple:

A small coding model should do more than generate code. It should understand the task, reason through problems, interact with tools, inspect feedback, and iterate toward a solution.

Designed for developers who want capable AI assistance without requiring a large datacenter-scale deployment.

✦ Model Overview

Property	Details
Model	`Nexora-Qwen-Coder-4B`
Hugging Face	`guell00/Nexora-Qwen-Coder-4B`
Base Model	Qwen 3.5 4B
Architecture	Dense Transformer
Model Class	4B Parameters
Primary Focus	Coding & Agentic Workflows
Fine-Tuning	Nexora Fine-Tuning
Training Method	SFT + Curriculum Learning
Reasoning Data	Trace Inversion
Agent Data	Tool-Use & Agent Trajectories
Training Context	Up to 32K tokens
Evaluation	MTP, `n=2`
Format	GGUF
Inference	llama.cpp compatible

⚡ Why Nexora?

Nexora-Qwen-Coder-4B is not designed around parameter count alone.

The objective is to make a compact local model more useful for real software development workflows.

The fine-tuning strategy focuses on four primary capabilities:

`01` · Coding

Generate, complete, explain, refactor, and implement code across common programming tasks.

`02` · Debugging

Analyze errors, identify potential failure points, reason about bugs, and produce targeted fixes.

`03` · Agentic Workflows

Operate in environments where the model can inspect files, select tools, execute actions, receive feedback, and iterate.

`04` · Structured Reasoning

Handle multi-step technical tasks that benefit from planning, decomposition, and structured problem solving.

🎛️ Recommended Sampling

For the best balance of consistency, coding quality, and controlled generation, the recommended default configuration is:

Parameter	Recommended
Temperature	`0.1`
Top P	`0.95`
Top K	`52`
Min P	`0.05`
Repetition Penalty	`1.1`
Presence Penalty	`Off`

Default Configuration

Temperature:        0.1
Top P:              0.95
Top K:              52
Min P:              0.05
Repetition Penalty: 1.1
Presence Penalty:   Off

This configuration is recommended for:

Code generation
Code completion
Debugging
Refactoring
Technical reasoning
Tool calling
Agentic coding workflows

The low Temperature is intended to improve consistency while preserving a small amount of generation flexibility.

Note: Evaluation results may vary when using sampling parameters different from those used during benchmarking.

📊 Benchmark Performance

Nexora-Qwen-Coder-4B was evaluated locally using the benchlocal evaluation framework.

The evaluation focuses primarily on practical developer workflows, including debugging, tool use, agent behavior, and instruction following.

Results

Benchmark	Nexora-Qwen-Coder-4B	Qwen 3.5 4B	Delta
BugFind-15	71 / 100	52 / 100	+19
HermesAgent-20	64 / 100	61 / 100	+3
ToolCall-15	100 / 100	90 / 100	+10
InstructFollow-15	93 / 100	93 / 100	0

Relative Evaluation Snapshot

BugFind-15
Nexora-Qwen-Coder-4B  ██████████████░░░░░░  71
Qwen 3.5 4B           ██████████░░░░░░░░░░  52

HermesAgent-20
Nexora-Qwen-Coder-4B  █████████████░░░░░░░  64
Qwen 3.5 4B           ████████████░░░░░░░░  61

ToolCall-15
Nexora-Qwen-Coder-4B  ████████████████████ 100
Qwen 3.5 4B           ██████████████████░░  90

InstructFollow-15
Nexora-Qwen-Coder-4B  ██████████████████░░  93
Qwen 3.5 4B           ██████████████████░░  93

Key Observations

The strongest observed results were in:

Debugging
Tool calling
Coding-oriented workflows
Instruction following
Local agent scenarios

These results suggest that the fine-tuning process improved the model's performance on targeted coding and agentic tasks compared with the base evaluation reference.

Benchmark results are snapshots from specific evaluation runs. They should not be interpreted as universal performance guarantees.

🤖 Agentic Coding

Nexora-Qwen-Coder-4B is designed for workflows where the model can interact with an external environment rather than simply returning a single static answer.

A typical agent loop can be represented as:

┌──────────────────┐
│   User Request   │
└────────┬─────────┘
         │
         ▼
┌──────────────────┐
│ Understand Task  │
└────────┬─────────┘
         │
         ▼
┌──────────────────┐
│ Plan Solution    │
└────────┬─────────┘
         │
         ▼
┌──────────────────┐
│ Select Tool      │
└────────┬─────────┘
         │
         ▼
┌──────────────────┐
│ Execute Action   │
└────────┬─────────┘
         │
         ▼
┌──────────────────┐
│ Inspect Feedback │
└────────┬─────────┘
         │
         ▼
      Success?
       ╱    ╲
     Yes     No
      │       │
      ▼       │
┌───────────┐ │
│  Final    │ │
│  Answer   │ │
└───────────┘ │
              │
              └──────► Iterate

This makes the model suitable for local environments that expose tools such as:

File operations
Terminal execution
Code search
Repository inspection
Build systems
Test runners
Linters
Debugging environments

Typical agent workflows may include:

Read → Plan → Act → Observe → Verify → Repair

Tool-call reliability depends on the application's prompt template, tool definitions, schema design, and execution environment.

🧩 Recommended Use Cases

Use Case	Fit
Code Generation	★★★★★
Debugging	★★★★★
Tool Calling	★★★★★
Local Coding Agents	★★★★★
Code Explanation	★★★★★
Refactoring	★★★★☆
Repository Analysis	★★★★☆
Technical Reasoning	★★★★☆
Documentation	★★★★☆
Software Architecture	★★★☆☆

Best suited for

Code generation
Code completion
Debugging
Bug localization
Error analysis
Refactoring
Repository inspection
Tool calling
Local coding agents
Multi-step development tasks
Technical reasoning
Developer assistants

🖥️ Built for Local AI

The 4B parameter class is intentionally compact.

Nexora-Qwen-Coder-4B aims to provide a practical balance between:

                    CAPABILITY
                        ▲
                        │
                        │       ● Nexora-Qwen-Coder-4B
                        │
                        │
                        │
                        └────────────────────────►
                            LOCAL EFFICIENCY

The goal is straightforward:

Deliver useful coding and agentic capabilities while remaining practical to run locally.

Potential deployment scenarios include:

Local development
Personal coding assistants
Offline workflows
Lightweight coding agents
Developer workstations
Experimental AI agents
Privacy-focused development environments

📦 Model Files & Quantization

Nexora-Qwen-Coder-4B is available in GGUF format for efficient local inference.

Quantization	Recommended For
Q4_K_M	Best balance of quality, memory, and speed
Q8_0	Higher quantized quality with increased memory usage

Recommended: `Q4_K_M`

For most users, Q4_K_M provides a strong balance between:

Quality · Memory · Speed

Higher Quality: `Q8_0`

Recommended when memory usage is less restrictive and higher quantized fidelity is preferred.

🚀 Quick Start

llama.cpp

Run the model directly from Hugging Face:

llama-cli -hf guell00/Nexora-Qwen-Coder-4B --jinja

Start an OpenAI-compatible local server:

llama-server -hf guell00/Nexora-Qwen-Coder-4B --jinja

Command availability may depend on your installed llama.cpp version and the model files available in the repository.

🧠 Context Length

The model was fine-tuned using sequences reaching approximately 32K tokens.

The underlying Qwen 3.5 family may support larger context windows depending on the specific architecture and inference backend.

Long-context performance depends on:

Backend support
RoPE configuration
YaRN scaling
Quantization
KV cache
Available memory
Context length

When extending beyond the training distribution, users should validate performance on their own workloads.

Example: Extended Context with llama.cpp

./llama-server \
  -m model.gguf \
  --ctx-size 131072 \
  --rope-scaling yarn \
  --rope-scale 4 \
  --yarn-orig-ctx 32768

Important: Increasing --ctx-size alone does not guarantee reliable long-context behavior.

🎯 Deterministic Coding Configuration

For highly deterministic coding, debugging, and code-repair workflows:

Parameter	Value
Temperature	`0`
Top P	`0.95`
Top K	`40`
Min P	`0.05`
Repetition Penalty	`1.1`
Presence Penalty	`Off`
Max Tokens	`Max`

Temperature:        0
Top P:              0.95
Top K:              40
Min P:              0.05
Repetition Penalty: 1.1
Presence Penalty:   Off
Max Tokens:         Max

For creative programming, brainstorming, or exploratory generation, increasing the temperature may produce more diverse outputs.

For debugging and code repair, lower temperatures generally provide more deterministic results.

🔧 Built With

Technology	Role
Qwen	Base model family
Unsloth	Fine-tuning & conversion workflows
GGUF	Efficient local model format
llama.cpp	Local inference
benchlocal	Coding & agent evaluation

⚠️ Limitations

Nexora-Qwen-Coder-4B is a compact 4B-class model and should be evaluated accordingly.

It may struggle with:

Extremely large repository-wide changes
Complex multi-file dependencies
Highly specialized professional domains
Tasks requiring extensive external knowledge
Long autonomous workflows without verification
Deep architectural decisions involving large systems

The model should be treated as a coding assistant, not a fully autonomous software engineer.

Generated code should always be:

REVIEWED
   ↓
TESTED
   ↓
VALIDATED
   ↓
DEPLOYED

Applications should verify generated code before using it in production environments.

Depending on the inference template and runtime configuration, the model may generate reasoning content inside:

<think>
...
</think>

Applications may parse, hide, or otherwise handle these sections according to their requirements.

🙏 Acknowledgements

Special thanks to:

The Qwen team for the base model family.
The Unsloth team for efficient fine-tuning and conversion tooling.
The open-source AI community for datasets, tools, and research.
Contributors supporting local hardware testing and evaluation.

📜 License

This model is released under the MIT License.

Please review the licensing terms of the underlying base model and any third-party components used in your deployment.

⚖️ Disclaimer

Nexora-Qwen-Coder-4B is provided for:

Research · Development · Experimentation · Local Inference

Actual performance may vary depending on:

Quantization method
Inference backend
Hardware
Context length
Prompt formatting
Sampling parameters
Evaluation methodology

Benchmark results represent specific evaluation runs and should not be interpreted as guaranteed performance across all environments or tasks.

Always review, test, and validate generated code before deploying it to production systems.

🧠 Nexora

Intelligence. Code. Evolve.

Built for developers who want capable AI coding assistance running locally.

guell00/Nexora-Qwen-Coder-4B

Hugging Face

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