🛠️ Qwen3.8-27B Code Forge - 3x Merge

A three-way merge bringing together coding, reasoning, and distilled conversational capabilities.

Looking for quantizations/GGUFs? Click here

✨ Overview

This model is a weighted merge of three independently fine-tuned versions of Qwen3.8-27B. The idea behind the merge is fairly straightforward: combine different training approaches and strengths without leaning too heavily into just one specialization.

The three contributors bring complementary qualities:

• Advanced coding, structured reasoning, and efficient thinking from nerkyor's Coder390 EfficientThink.
• Conversational and knowledge-oriented distillation from TeichAI's Fable Distill.
• Coding and agentic capabilities from Jackrong's Qwopus Coder Flash.

The goal is a versatile general-purpose model that can write and understand code, work through challenging problems, follow instructions, and handle longer reasoning tasks while avoiding unnecessary reasoning overhead.

⚖️ Merge Details

The model was created by merging the following checkpoints with these weights:

All three models share Qwen3.8-27B as their underlying foundation.

The weights above are the merge coefficients, not percentages of training data or a claim about each model's relative contribution to a particular capability.

🧩 The Three Contributors

1. 🧠 Coder390 EfficientThink - nerkyor

Model card

This is the most extensively post-trained contributor of the three. It builds on nerkyor's own EfficientThink SFT + SimPO checkpoint, itself derived from the official Qwen3.8-27B model.

Its training combines multiple rounds of supervised fine-tuning (SFT) and Reinforcement Learning from Offline/Online-style comparisons (RLOO, as described in the upstream recipe).

The training provenance is particularly interesting:

• Opus 5.5 and GPT-6 Astra: contributed gold answers and teacher trajectories.
• Grok 4.7: hosted the training run and helped check and filter data.
• DeepSeek V4 Pro and K3: contributed teacher trajectories.
• K3: additionally contributed problem writing and RLOO value review.

The EfficientThink objective is to reduce unproductive reasoning loops and runaway thought sequences without removing the ability to reason deeply when a problem actually requires it.

The Coder390 name reflects the author's stated GPQA, MMLU, and LiveCodeBench performance targets. The upstream model also includes MTP and DFlash2 speculative-decoding components.

This contributor brings additional reasoning refinement, coding specialization, and an explicit focus on efficient problem-solving.

2. 📖 Qwen3.8-27B-Fable-Distill - TeichAI

Model card

Fable Distill is a relatively light fine-tune of Qwen3.8-27B, trained on a combination of publicly identified Fable datasets and a larger private corpus of Fable 5 data.

The two publicly listed datasets are:

• armand0e/claude-fable-5-claude-code - Claude/Fable-oriented coding traces.
• armand0e/Fable-5-Chat - Fable 5 conversational examples.

The model's stated goal is to transfer useful Fable 5 conversational and problem-solving behavior into the Qwen3.8 architecture. Its published ARC Challenge and BoolQ results also suggest an emphasis on retaining and improving general knowledge and instruction-following capabilities.

This contributor adds a different flavor to the merge: conversational ability, knowledge-oriented responses, and distilled teacher behavior.

3. ⚡ Qwopus3.8-27B-Coder-Flash - Jackrong

Model card

A coding-focused fine-tune built on Qwen3.8-27B, with an emphasis on practical software engineering and agentic workflows.

Its training philosophy revolves around making each token count: understanding a task, planning an implementation, using tools, inspecting feedback, fixing errors, and delivering a working result.

The published training recipe describes approximately 200K code and reasoning examples, covering agent interactions, algorithmic problems, code generation, and logical reasoning. Its data sources include teacher-generated examples from Qwen3.8-Flash-Max, Qwen3.8-Max, DeepSeek-V4-Flash, and the NVIDIA Nemotron ecosystem.

This contributor is intended to bring strong implementation skills, code quality, tool-use behavior, and more purposeful reasoning.

🎯 Intended Use

The merged model is intended for:

• Software development, code generation, debugging, and refactoring.
• Complex reasoning, mathematics, and multi-step problem-solving.
• Agentic workflows involving tool use and iterative execution.
• General instruction following and conversational tasks.
• Technical explanations, analysis, and knowledge-intensive queries.
• Tasks where useful reasoning matters more than simply producing long reasoning traces.

It should be considered an experimental general-purpose merge rather than a model optimized for just one benchmark or task.

⚠️ Limitations

This is a weight-space merge, not a new round of supervised fine-tuning or reinforcement learning. Combining checkpoints does not guarantee that every capability of every contributor will be preserved.

Potential limitations include:

• Inconsistent behavior between different types of coding and reasoning tasks.
• Changes in response style or reasoning length.
• Reduced performance in some specialized tasks compared with an individual contributor.
• Tool-calling behavior that may depend on the inference engine and chat template.
• No independent benchmark results are claimed for this merged checkpoint.

As always, generated code and reasoning should be checked before being used in production or other consequential settings.

🙏 Credits and Acknowledgements

A big thank-you to the people behind the three upstream models and to the wider Qwen ecosystem.

Special thanks to:

• Qwen for the Qwen3.8-27B foundation model.
• Jackrong for the Qwopus coding and agentic fine-tune.
• TeichAI for Fable Distill.
• nerkyor for the EfficientThink and Coder390 training work.
• The contributors to the upstream datasets and teacher-model ecosystems.

This merge builds on their work rather than replacing it. Please visit and credit the original repositories when using or redistributing the model.

📜 License

The three listed upstream model repositories specify Apache-2.0. This model card uses the same license designation; users should review the upstream model and dataset terms and any applicable third-party terms before redistribution or commercial use.