🪐 Qwopus3.5-4B-Coder is a compact coding and agent model built on the Qwen3.5 4B family. It is optimized for local execution, code debugging, structured tool-use behavior, and reasoning-heavy developer workflows. The training recipe follows the Qwopus Coder line: Trace Inversion for learnable reasoning traces, high-quality agent trajectories for tool behavior, and curriculum SFT to preserve formatting stability under longer contexts.
🪶 Agent Trace AlignmentLearns from tool-call trajectories that include real feedback loops, not only isolated prompt-response pairs.
🔁 MTP-Ready EvaluationBenchmarked with Multi-Token Prediction enabled at n=2 against the Qwen3.5-4B-MTP reference.
⚡ Local-First DesignTo empower resource-constrained users, the 4B size serves as the sweet spot for running agentic tasks on 16GB laptops, making it an excellent choice for handling simple repetitive tasks and service monitoring operations.
💡 1. Base Model, MTP Setup, and Training Stack
🧠 1.1 Base Model Specifications (Qwopus3.5-4B-Coder)
Qwopus3.5-4B-Coder inherits the compact Qwen3.5 4B dense architecture and adapts it toward agentic coding, debugging, tool routing, and long-form reasoning. The 4B size is intended to keep the model deployable on local machines while retaining enough reasoning capacity for developer workflows.
Attribute
Specifications & Details
🧠 Architecture
Dense Transformer / 4B-class Qwen3.5 family
🎯 Primary Focus
Agentic coding, code debugging, tool-use stability, instruction following
This project continues the Qwopus collaboration path with engineer Kyle Hessling, whose hardware support and evaluation feedback helped make the local model testing workflow practical and reproducible.
👉Follow hardware and model training updates on X / Twitter: @KyleHessling1
🦥 1.3 Fine-Tuning Framework (Unsloth)
Training and adaptation use the Qwopus SFT workflow with Unsloth acceleration where applicable, focusing on efficient supervised fine-tuning, stable LoRA-style adaptation, and clean model-card reproducibility.
Community Release Notice: Qwopus3.5-4B-Coder is an experimental community release intended for research, local coding experiments, and agent workflow exploration. It has not undergone full safety evaluation or broad general-domain benchmarking.
📖 2. Background and Motivation
⚠️ 2.1 Why a 4B Coder Model?
A 4B-class model is small enough to run locally with practical latency, but large enough to benefit from structured reasoning and agent trace training. The goal of this release is not to maximize raw benchmark size, but to create a compact coding assistant that remains stable under debugging, instruction-following, and tool-use pressure.
🧬 2.2 Trace Inversion and Agent Behavior
Commercial and frontier models often expose only compressed reasoning summaries. Qwopus-style training uses Trace Inversion to reconstruct these compressed "reasoning bubbles" into fuller learnable reasoning traces. For coding, this is paired with agent trajectories that include tool definitions, tool calls, and real feedback, teaching the model to reason through interactive work rather than only produce static answers.
📊 3. benchlocal Evaluation and Baseline Comparison
📊 benchlocal Agent & Coding Benchmark
Local MTP comparison across official Qwen, Claude-Opus reasoning distill, and 9B reference rows for debugging, agent workflow, tool routing, and instruction following.
🏆 Suite Average82.0%vs. 74.0% baseline (+8.0 pp)
🐛 BugFind-1571 / 100+19 Delta over baseline
🧠 HermesAgent-2064 / 100+3 Delta over baseline
🛠️ ToolCall-15100 / 100Perfect tool routing
Test configuration: Models were evaluated through benchlocal using LM Studio on the same local Apple Silicon / MLX / GGUF-style setup as the 9B Coder evaluation. Tested 4B models: Qwopus3.5-4B-Coder-MTP, Qwen3.5-4B-MTP, and Similar Public 4B Claude-Distilled Variant. MTP was set to n=2 for the MTP rows, sampling used temperature=1.0 and top_p=0.95. Each scenario allowed up to three answer attempts per model; a scenario was counted as correct if any attempt passed. Deep-blue rows mark Qwopus3.5-9B-Coder-GGUF reference scores. Other 9B comparison models use neutral rows, and the official Qwen/Qwen3.5-9B baseline is kept on a white background.
🐛 3.1 BugFind-15: Code Debugging and Bug Localization
Model
Score
Delta
Dimension Scores
Readout
Qwopus3.5-4B-Coder-MTP
71
+19
A: 53 / B: 67 / C: 73 / D: 77 / E: 83
Clear debugging lead.
Qwen3.5-4B-MTP
52
baseline
A: 43 / B: 53 / C: 67 / D: 32 / E: 60
Lower consistency on bug-fix scenarios.
Similar Public 4B Claude-Distilled Variant
45
-26
A: 36 / B: 40 / C: 53 / D: 32 / E: 67
Lower debugging consistency in this run.
Qwopus3.5-9B-Coder-GGUF
79
9B reference
A: 67 / B: 87 / C: 100 / D: 77 / E: 43
Leading 9B-class row in this pack.
Qwen3.5-9B-DeepSeek-V4-Flash
75
9B comparison
A: 67 / B: 100 / C: 67 / D: 57 / E: 80
Public comparison row from 9B card.
Other Public 9B Agent Model
58
9B comparison
A: 29 / B: 87 / C: 73 / D: 20 / E: 67
Public comparison row from 9B card.
🧠 3.2 HermesAgent-20: Memory, Workspace Orchestration, and Agent Workflow
📄 3.4 InstructFollow-15: Formatting and Constraint Following
Model
Score
Delta
Dimension Scores
Readout
Qwopus3.5-4B-Coder-MTP
93
0
A: 100 / B: 100 / C: 100 / D: 65 / E: 100
Tie
Qwen3.5-4B-MTP
93
0
A: 100 / B: 100 / C: 100 / D: 65 / E: 100
Tie
Similar Public 4B Claude-Distilled Variant
60
-33
A: 65 / B: 35 / C: 100 / D: 60 / E: 39
Lower constraint-following reliability in this run.
Qwopus3.5-9B-Coder-GGUF
93
9B reference
A: 100 / B: 100 / C: 100 / D: 67 / E: 100
Reported 9B Coder reference score.
🍎 All screenshots of the test interfaces have been uploaded to the image folder in the repository. Click the link below to view and verify:
🔗 View Test Screenshots
❤️ Kyle Hessling for his generous hardware and equipment support. You can follow him for more updates on X / Twitter: @KyleHessling1.
🗺️ 4. Training & Data Pipeline Overview
The training process fuses Trace Inversion data augmentation with a Three-Stage Curriculum Learning pipeline. The core engineering focuses on expanding context length gradually while training on reconstructed reasoning traces and real agent trajectories to keep the output format stable.
To steadily scale reasoning quality under local and long-context inference, Qwopus3.5-4B-Coder uses a curriculum-style data mixture. The model is first stabilized on short, clean reasoning samples, then exposed to complex coding and agent traces, and finally reinforced with longer contexts plus replay data. This section also describes the fine-tuning context-length distribution; runtime long-context extension guidance is covered in Section 6.
Focuses on short-to-medium length, cleanly formatted reasoning samples. The primary goal is to establish reliable structured reasoning output, including stable <think> boundaries, before exposing the model to longer chains.
🛠️ Stage 2: Complexity Expansion
• Extend length to 4,096 - 8,192 tokens • Introduce higher-difficulty coding and agent samples
Gradually increases the ratio of complex reasoning chains, code debugging tasks, and multi-turn tool traces. The model learns to connect reasoning, action selection, and environment feedback.
🚀 Stage 3: Long-Context SFT
• Progressively scale samples up to 32K tokens • Use short-sample replay
Pushes the model toward long-context and multi-turn reasoning while replaying high-quality short samples to reduce instruction-following drift. The 32K figure describes the fine-tuning sequence/data mixture target, not a hard architectural limit.
📄 6. Context Length and Long-Context Usage
📄 6.1 Runtime Context Guidance
During fine-tuning, this model was trained with a maximum sequence length of 32K tokens. The training data mixture was also constructed around samples up to 32K tokens, so the context-length distribution in this model card reflects the fine-tuning data distribution rather than a hard architectural limit.
The model still inherits the native long-context capability of the Qwen3.5-family base model. Longer context windows such as 128K or 256K may be available in compatible inference runtimes, depending on backend support and configuration.
For practical long-context inference beyond 32K, especially when using llama.cpp / GGUF, it is recommended to enable RoPE/YaRN scaling instead of only increasing n_ctx or --ctx-size. Directly setting a larger context window without RoPE scaling may work in some setups, but it can be less stable and may not deliver the expected long-context behavior.
This follows Qwen community guidance for GGUF long-context usage. In a Qwen GGUF discussion, a Qwen maintainer noted that "128K context length needs YaRN" and later clarified that supported scaling should be explicitly enabled rather than assumed to be on by default. Reference: Qwen/Qwen2.5-72B-Instruct-GGUF discussion #2.
Community feedback also suggests that RoPE/YaRN scaling can improve long-context stability for this model family. One user reported that, on HermesAgent-20, Qwopus3.6-35B-A3B-v1 performed better when extending from 32K to 128K via RoPE scaling than when directly setting a 128K context window without scaling, with scores of 83 vs. 72 in their setup. This result may vary depending on backend, quantization type, KV cache settings, hardware, and benchmark configuration, but it is consistent with the recommendation to use RoPE/YaRN scaling for contexts beyond 32K.
Example llama.cpp configuration for extending from 32K to 128K:
Please note that long-context behavior may vary depending on inference backend, quantization type, KV cache settings, available memory, and task type. For best results, benchmark the target workload when using contexts beyond 32K.
🎯 7. Recommended Use Cases and Limitations
✅ Good Fits
Code debugging, small repository tasks, tool-call routing, local coding agents, structured instruction following, development workflow assistants, and reasoning traces where concise local latency matters.
❌ Known Limits
As a compact model, it can still miss broad world knowledge, complex repository-wide dependencies, or highly specialized domain requirements. Tool-call behavior depends strongly on prompt format and tool schema consistency.
Deployment note: The model may emit reasoning inside <think> and </think> tags. Front-end applications and agent frameworks should parse or hide these sections where appropriate.
👉 Qwen MTP GGUF Processing Workflow
A custom splitting and merging methodology designed specifically for Qwen series Multi-Token Prediction (MTP) heads.
