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Model Details
Model Provider
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Model Tree
Jackrong/Qwopus3.5-9B-Coder
TeichAI/Qwen3.5-9B-Fable-5-v1
Qwen/Qwen3.5-9B
empero-ai/Qwythos-9B-v2
Input Modalities
Output Modalities
Supported Functionality
| Property | Details |
|---|---|
| Base architecture | Qwen3.5-9B |
| Model type | Weighted checkpoint merge |
| Merge method | Linear weighted average |
| Primary focus | Reasoning, conversational generation, and coding |
| Additional focus | Agent workflows, tool use, and long-context tasks |
| Parameter class | Approximately 9B |
| Declared license | Apache-2.0; verify upstream terms before redistribution |
The merged checkpoint combines three source models using the following configured coefficients.
| Source model | Weight | Primary emphasis |
|---|---|---|
| empero-ai/Qwythos-9B-v2 | 1.0 | Mythos reasoning, output stability, and reduced repetition |
| TeichAI/Qwen3.5-9B-Fable-5-v1 | 0.8 | Claude Fable 5 / Claude Code trace fine-tuning |
| Jackrong/Qwopus3.5-9B-Coder | 0.6 | Opus coding, reasoning, and agentic tool use |
Merge method: Linear weighted average.
Qwythos-9B-v2 is an Empero AI fine-tune built on the Qwen3.5-9B architecture and the earlier Qwythos-9B-Claude-Mythos-5-1M model.
The model emphasizes reasoning-oriented generation and long-context use. Its v2 release specifically targets repetitive output and degeneration during greedy or low-temperature decoding.
The author describes using Final-Token Preference Optimization (FTPO) to identify the token that begins a repetition loop and train the model to prefer coherent alternatives. The published training procedure reports approximately 2,000 automatically mined preference tuples.
The v2 release also restores the base model's multi-token-prediction (MTP) head and updates model behavior to reduce unwanted self-identification in unrelated responses.
The source card reports a YaRN-scaled context length of approximately 1,048,576 tokens, although this setting should not be assumed to transfer unchanged to the merged checkpoint.
Role in this merge: Provides the primary reasoning-oriented foundation, with emphasis on stable generation and reducing repetitive output.
Important distinction: Qwythos v2 is described as a robustness and hygiene update rather than a major increase in raw capabilities. Its reported benchmarks belong to the source model, not this merge.
TeichAI's Fable 5 is a Qwen3.5-9B fine-tune trained on the publicly identified armand0e/claude-fable-5-claude-code dataset.
The author describes extracting, formatting, and masking the training data using Teich, with training accelerated by Unsloth and Hugging Face TRL.
The source card reports the following results:
| Benchmark | Fable-5-v1 | Qwen3.5-9B |
|---|---|---|
| ARC | 0.624 | 0.553 |
| ARC-Easy | 0.806 | 0.712 |
| BoolQ | 0.891 | 0.892 |
These are the source model's reported results and should not be attributed to the merged checkpoint.
The dataset name indicates Claude Fable 5 / Claude Code traces. The source card does not provide sufficient evidence to characterize every training example or establish that the model is exclusively a creative-writing fine-tune.
Role in this merge: Adds a distinct conversational and instruction-tuning trajectory learned from the Fable 5 dataset.
Qwopus3.5-9B-Coder is Jackrong's coding-oriented Qwen3.5-9B fine-tune. Its published tags and model documentation emphasize software development, structured reasoning, function calling, and agentic workflows.
The model tree identifies Qwen3.5-9B as its underlying architecture and lists Qwopus3.5-9B-v3.5 in its fine-tuning lineage.
The publicly listed training datasets are:
lambda/hermes-agent-reasoning-tracesJackrong/Claude-opus-4.7-TraceInversion-5000xJackrong/Claude-opus-4.6-TraceInversion-9000xThe training approach combines agent-oriented reasoning traces with Claude Opus-style trace-inversion data. This provides a training emphasis on structured problem-solving and coding-related agent workflows.
Role in this merge: Adds coding-oriented behavior, tool-use patterns, and agentic reasoning.
Qwen3.5-9B is the shared underlying architecture for all three source models.
The official model card describes a 9B-parameter causal language model with a vision encoder. Its hybrid architecture combines gated linear-attention layers with full-attention layers, supporting long-context processing while retaining multimodal capabilities.
The base model documents a native context length of 262,144 tokens, with extended context supported through appropriate RoPE scaling configurations.
These are base-model specifications. The merged checkpoint may differ in context behavior, multimodal compatibility, MTP support, or inference requirements.
The merged checkpoint is produced by combining three independently fine-tuned models. It was not directly trained on a newly assembled dataset containing all of the samples listed below.
| Dataset | Description |
|---|---|
| armand0e/claude-fable-5-claude-code | Claude Fable 5 / Claude Code traces used for the Fable 5 fine-tune |
The source model card describes the extraction, formatting, and masking of the data for training. It does not fully document every preprocessing detail or the provenance of every underlying trace.
| Dataset | Intended role |
|---|---|
| lambda/hermes-agent-reasoning-traces | Agent-oriented reasoning and tool-use traces |
| Jackrong/Claude-opus-4.7-TraceInversion-5000x | Claude Opus 4.7-style trace-inversion data |
| Jackrong/Claude-opus-4.6-TraceInversion-9000x | Claude Opus 4.6-style trace-inversion data |
These datasets are listed on the Qwopus Coder source card. Their presence does not imply that all samples were used in equal proportions or that the same data was used by the other source models.
Qwythos-9B-v2's documented v2 training stage uses approximately 2,000 automatically mined preference tuples for FTPO-based repetition correction. These examples are constructed from the model's own looping behavior and coherent alternative tokens.
The source model's broader training lineage includes empero-ai/Qwythos-9B-Claude-Mythos-5-1M. The Qwythos v2 card does not identify a complete public dataset manifest for the earlier training stages, so those datasets are not listed here as verified public dataset IDs.
Dataset names and training details above are based on the upstream model cards. They should not be interpreted as a complete record of every preprocessing stage, sample count, or training mixture.
Consult the source model and dataset cards for their latest descriptions, licenses, and acknowledgements.
This merge is intended for experimentation with a compact model combining general reasoning, conversational generation, and coding-oriented behavior.
Potential use cases include:
These are intended use cases inferred from the source models, not independently verified performance guarantees.
The source models have different training histories and generation characteristics.
Merging these models may preserve some of these characteristics, alter others, or introduce new failure modes. In particular, the merge may exhibit repetition, inconsistent formatting, unexpected reasoning behavior, or unreliable tool calls.
The presence of reasoning markers or structured tool-call outputs does not guarantee that the model's reasoning is correct or that its tools will execute successfully.
Use independent verification and human review for consequential decisions.
No independent benchmark results are reported here for the merged checkpoint.
The source models publish their own evaluations, but differences in prompts, sampling settings, datasets, and scoring procedures make direct comparisons difficult. Their reported results should not be presented as results for this merge.
Recommended evaluation areas include:
| Area | Evaluation focus |
|---|---|
| General reasoning | Multi-step problems, logical deduction, and instruction following |
| Coding | Code generation, debugging, and software-engineering benchmarks |
| Agentic behavior | Tool selection, argument formatting, and multi-step tool execution |
| Generation stability | Repetition, looping, and low-temperature decoding |
| Conversational quality | Coherence, formatting, and adherence to instructions |
| Long context | Accuracy and consistency at increasing context lengths |
| Multimodal support | Image understanding, if the required components are retained |
For reproducible comparisons, document the exact model revisions, inference settings, prompts, quantization, context length, and evaluation methodology.
This merge builds on the work of:
Please consult the original model cards for additional acknowledgements and citations.
This repository declares the Apache-2.0 license. Verify the applicable license and usage terms for the base model, all three source checkpoints, and the underlying datasets before redistribution or commercial use.