Model reference · open weights
Macaron-Tall is an open-weight language model from mindlab-research. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.
Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.
What it is
| Released by | mindlab-research |
|---|---|
| Type | Language models |
| Task | Text gen |
| Parameters (lead) | 36.0B |
| Context | 256k tokens |
| Runs with | transformers |
| Released | 2026-07-22 |
| Popularity | 555 downloads / month |
| Licence | Open weights |
About
📖 Blog: Introducing Macaron-V1 🚀 Hosted API: Macaron API Platform 🧩 Artifacts: Macaron Artifacts 🛠️ Self-hosted serving: Mixture of LoRA (MoL) serving harness 📄 Technical report: Macaron-V1: Towards Open Continual Learning with Self-Improvement and Mixture-of-LoRA (arXiv:2608.09819) ✉️ Correspondence: contact@mindlab.ltd
Macaron-V1-Tall is a Mixture of LoRA (MoL) model in the Macaron-V1 family, built on Qwen3.6-35B-A3B for personal intelligence, tool use, coding workflows, and code-native Generative UI.
The model combines a Qwen3.6 MoE base with the Macaron-V1 specialist system: four LoRA specialists for chat, personal-agent tasks, coding, and GenUI, with an L0 router selecting the most suitable specialist for each new user request.
Macaron-V1-Tall is released alongside Macaron-V1-Venti as a sibling model in the Macaron-V1 family. Both models share the same product-facing specialist layout and routed serving interface while targeting different deployment and experimentation profiles.
loras/L0 through loras/L3.| Field | Value |
|---|---|
| Model name | Macaron-V1-Tall |
| Organization | MindLab Research |
| Release family | Macaron-V1 |
| Base model | Qwen3.6-35B-A3B |
| Architecture | Qwen3.6 MoE base + Mixture of LoRA (MoL) specialists |
| Parameter footprint | 50B release label: a nominal 35B Qwen3.6-35B-A3B base plus four LoRA specialists (~50.1B by nominal-base-plus-adapter count) |
| LoRA rank / alpha | r = 64, α = 128, dropout 0 |
| Stored values per adapter | 3,775,651,840 (BF16 for L0/L1/L3; F32 for L2) |
| Specialists | L0 Chat, L1 Agent, L2 Coding, L3 GenUI |
| Post-training system | MinT + MindForge |
| Primary domains | Personal intelligence, tool use, coding, Generative UI |
| Context length | 262K |
| Text config | 40 layers, 2048 hidden size, 16 attention heads, 2 KV heads, 256 experts, 8 experts per token |
| Precision / serving format | BF16 base checkpoint with routed LoRA serving |
| License | MIT |
On parameter counts. Tall's adapter configurations use a different rank and expert target set from Macaron-V1-Venti, so its aggregate is reported as a rounded release footprint rather than inferred from base size alone. The two adapter budgets are not directly comparable because the bases expose different target-module and expert structures. The sidebar figure on this page is an automatic safetensors tensor count and may differ from this architectural figure.
| Adapter | Role | Description |
|---|---|---|
loras/L0 | Chat | Conversational and instruction-following backbone; entry point for routing. |
loras/L1 | Agent | Personal-life agent tasks, heavy tool use, long-horizon planning, and dynamic workflows. |
loras/L2 | Coding | Code understanding, SWE tasks, terminal use, and repository workflows. |
loras/L3 | GenUI | UI4A rendering and UI-driven action. |
At runtime, L0 routes each new user request to the most suitable specialist. Ongoing reasoning and tool interactions remain within the selected LoRA, while completed work can be shared across specialists through concise summaries.
| Hop | Avg latency | Share of loop |
|---|---|---|
| Route (L0 constrained decode, 24 tok) | 0.20 s | 11% |
| Answer (specialist generation) | 1.24 s | 70% |
| Summary (192-tok cap) | 0.32 s | 19% |
| Total | 1.76 s | 100% |
Measured over 48 multi-turn mixed-domain requests at temperature 0. The routing overhead share (~30%) is stable across base sizes, while absolute loop latency is roughly 2.7× lower than Macaron-V1-Venti's 4.68 s.
On the same 6,448-sample trace used for Venti — verified identical by matching every sample ID and input hash — Tall reaches 99.04% routing accuracy with 100% canonical-label compliance, so routing quality is stable across base sizes. L3 GenUI reaches 100% and L1 Agent is lowest at 95.1%. The trace is drawn from LoRA training data, so this is an implementation diagnostic rather than a generalization estimate.
Macaron-V1-Tall compared with its Qwen3.6-35B-A3B base on the seven benchmarks evaluated for both systems. Both use the same protocol within each row; higher is better, normalized to 0–100.
| Benchmark | Macaron-V1-Tall | Qwen3.6-35B-A3B |
|---|---|---|
| Macaron ChatBench | 54.9 | 48.0 |
| Macaron LivingBench | 48.4 | 47.1 |
| PinchBench | 86.2 | 82.5 |
| ClawGym | 64.0 | 58.6 |
| SWE-bench Verified | 75.4 | 73.4 |
| TerminalBench 2.1 | 56.2 | 52.5 |
| UI4A-Bench (Final Score) | 59.3 | 33.9 |
Tall has a larger point estimate than its base on all seven rows, from +1.3 on Macaron LivingBench to +25.4 on UI4A-Bench. This is an end-to-end system comparison, not a parameter-matched component ablation: the two systems differ in parameter footprint, adapters, routing, and potentially harness behavior. The table does not identify which component produces each difference, and it does not establish scaling behavior from Macaron-V1-Tall to Macaron-V1-Venti.
The headline Macaron-V1 comparison against frontier baselines is reported for Macaron-V1-Venti; see the Venti model card and Table 8 of the technical report. Per-benchmark judges, case counts, and metrics are documented in Appendix B of the report.
The Macaron-V1-Tall specialists are trained on text-only data, while the Qwen3.6-35B-A3B base is multimodal. The following full-dataset point estimates (no-thinking mode) co
From the published model card. Full card on the HuggingFace links in the sidebar.
Benchmarks
As published on the model card — the maker's own numbers, not measured by AxForge.
| Task | Dataset | Metric | Score |
|---|---|---|---|
| Chat | ChatBench | ChatBench | 54.900 |
| Chat | LivingBench | LivingBench | 48.400 |
| Agent | PinchBench | PinchBench | 86.200 |
| Agent | ClawGym | ClawGym | 64 |
| Coding | SWE-bench Verified | SWE-bench Verified | 75.400 |
| Coding | TerminalBench 2.1 | TerminalBench 2.1 | 56.200 |
| Generative UI | UI4A-Bench | UI4A-Bench | 59.300 |
Using it via the API
Once AxForge deploys macaron-tall for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (macaron-tall below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/chat/completions \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"macaron-tall","messages":[{"role":"user","content":"Hello"}]}'
Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.