Coder
Collection
Nexora family • 2 items • Updated
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.cppversion 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-sizealone 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.
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