pragmaticcs/MiMo-HERO-Frontier-Agentic-Coder-9B-GGUF

[!TIP]

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[!IMPORTANT] This repository provides quantized GGUF builds of pragmaticcs/MiMo-HERO-Frontier-Agentic-Coder-9B

Most 9B reasoning models have an annoying flaw: they burn thousands of tokens second-guessing themselves before writing a single line of functional code.

Ask them for a complex web interface or an autonomous terminal command, and they get trapped in recursive chain-of-thought loops, debating non-existent edge cases, re-checking basic syntax, only to eventually run out of context.

MiMo-HERO-Frontier-Agentic-Coder-9B eliminates this overthinking trap.

The model is a targeted surgical graft that fuses two complementary specializations on the Qwen 3.5 9B architecture:

• XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B (The Brain): High-density algorithmic reasoning, deep syntax comprehension, and complex architectural synthesis.
• XLearning-SCU/HERO-9B (The Antidote): An RL-trained execution policy designed specifically to minimize token waste, bypass hesitation loops, and prioritize immediate action.

By grafting HERO's high-efficiency decision vectors directly into MiMo's reasoning core, the model cures MiMo's tendency to go into thinking loops unlocking its real potential.

[!NOTE] The result is a fast, disciplined and extremely capable local workhorse that excels at frontend/UI design and website creation while .

Live One-Shot Demo 🎯

As a stress test, I asked the model (running locally on my machine at Q5_K_M quantization in Unsloth Desktop) to one-shot a full landing page with the following minimal prompt:

Prompt:
"Create a one-file landing page website with HTML, CSS and JavaScript that sells a premium Japanese water bottle."

No web search, no tool-calls and no additional system prompts were used during this test, to fairly benchmark the raw model output. This way, the model had to come up with the design, style, contents and functionality of the page all on its own.

You can check out the landing page generated by the model by visiting the link below (the page is hosted via a Hugging Face static space):

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Contents

• Technical Specifications
• Mathematical Formulation: Subspace-Guarded Grafting
• Subsystem Protection Policies
• Chat Template & Agentic Protocol
• Recommended Generation Parameters
• Inference and Deployment
• Unlocking Model Access
• Citation & References

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Technical Specifications

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Mathematical Formulation: Subspace-Guarded Grafting

Direct linear blending, spherical interpolation (SLERP), and uniform DARE perturbations degrade hybrid DeltaNet networks. Linear attention recurrence relies on strictly contractive transition states; arbitrary parameter shifts introduce runaway eigenvalues that destroy long-range memory and break special control tokens.

To prevent degradation, the parameter integration is governed by non-interfering geometric constraints.

1. Delta Task Disentanglement

Let W₀ denote the underlying base model parameters (Qwen3.5-9B), W_MiMo the primary anchor, and W_HERO the donor. The task vectors are defined as:

$$ \Delta_{\text{MiMo}} = W_{\text{MiMo}} - W_{0} $$

$$ \Delta_{\text{HERO}} = W_{\text{HERO}} - W_{0} $$

2. Spectrally Tapered Bi-Subspace Guard

To guarantee that HERO does not overwrite the fundamental representations discovered during MiMo's distillation, HERO's update is projected orthogonally away from the dominant singular directions of MiMo's task vector.

For any weight matrix with singular value decomposition:

$$ \Delta_{\text{MiMo}} \approx U_k \Sigma_k V_k^T $$

The overlapping projection tensor is computed across rank k = 32 and tapered according to spectral energy:

$$ \Omega = \left(\frac{\operatorname{diag}(\Sigma_k)}{\tau}\right)^2 $$

$$ \Delta_{\text{HERO}}^{\perp} = \Delta_{\text{HERO}} - U_k \left( \Omega \odot (U_k^T \Delta_{\text{HERO}} V_k) \right) V_k^T $$

Components of the agentic donor that directly compete with MiMo's primary singular subspace are eliminated, allowing new procedural capabilities to occupy unutilized parameter dimensions.

3. Sign Consensus Damping

When fine-tuning objectives pull individual weights in opposite directions, blind averaging causes catastrophic cancellation and syntax corruption. For coordinates where updates conflict:

$$ \operatorname{sign}\left(\Delta_{\text{MiMo}}^{(i, j)}\right) \neq \operatorname{sign}\left(\Delta_{\text{HERO}}^{\perp (i, j)}\right) $$

An antagonistic penalty coefficient $\Phi$ is evaluated against relative weight salience:

$$ \Phi_{(i, j)} = 1.0 - 0.50 \cdot \left( \frac{|\Delta_{\text{MiMo}}^{(i, j)}|}{|\Delta_{\text{MiMo}}^{(i, j)}| + |\Delta_{\text{HERO}}^{\perp (i, j)}| + \epsilon} \right) $$

$$ \widetilde{\Delta}{\text{HERO}}^{(i, j)} = \Phi{(i, j)} \cdot \Delta_{\text{HERO}}^{\perp (i, j)} $$

Antagonistic donor noise is suppressed, ensuring that settled code generation rules are never inverted.

4. DARE Energy-Preserved Trimming

To eliminate low-magnitude parameter drift, the bottom 4% energy of the sanitized update is zeroed out. The surviving parameters are rescaled by the inverse square root of the retained energy ratio to preserve exact variance:

$$ \widehat{\Delta}{\text{HERO}} = \operatorname{Mask}\left(\widetilde{\Delta}{\text{HERO}}, , p=0.96\right) \cdot \sqrt{\frac{1.0}{0.96}} $$

5. Bell-Plateau Depth Routing

Layer-wise integration follows a smooth transition profile across the network's depth index l over total layers L:

$$ g(l) = \begin{cases} 0.50 + 0.50 \cdot S\left(\frac{l}{0.20 L}\right) & 0 \le l < 0.20 L \ 1.00 & 0.20 L \le l \le 0.80 L \ 1.00 - 0.30 \cdot S\left(\frac{l - 0.80 L}{0.20 L}\right) & 0.80 L < l \le L \end{cases} $$

where S(t) represents the smooth cubic step function:

$$ S(t) = 3t^2 - 2t^3 $$

• Early Layers (0% to 20%): Lower graft gain (0.50) safeguards token embeddings and initial sensory projections.
• Mid Layers (20% to 80%): Full-strength injection (1.00) transforms core reasoning and planning pathways.
• Late Layers (80% to 100%): Scaled down (0.70) to protect tool-call formatting, stopping tokens, and output logits.

6. Dual Trust Region Clamping

To prevent distribution collapse, the effective step size is bound simultaneously by Frobenius norm ratio $\rho$ and spectral radius $\sigma_{\max}$:

$$ s = \min\left( s_{\max}, ; \rho \cdot \frac{|\Delta_{\text{MiMo}}|F}{|\widehat{\Delta}{\text{HERO}}|F}, ; \kappa \cdot \frac{\sigma{\max}(\Delta_{\text{MiMo}})}{\sigma_{\max}(\widehat{\Delta}_{\text{HERO}})} \right) $$

Final parameter state:

$$ W_{\text{final}} = W_{\text{MiMo}} + \big(s \cdot g(l)\big) \cdot \widehat{\Delta}_{\text{HERO}} $$

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Subsystem Protection Policies

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Chat Template & Agentic Protocol

This model integrates the Improved Chat Template for Qwen 3.x directly into its tokenizer configuration and Jinja runtime environment.

Protocol Highlights

• Multi-Tier Chain-of-Thought Control: Isolates internal reasoning paths within explicit <thought>...</thought> delimiters prior to tool calling or terminal emission.
• Dual Tool Schemas: Fully supports both JSON-RPC function payloads and structured XML tool execution tags, enabling zero-shot integration with platforms like Browser-Use, OpenHands, and native agent loops.
• Anti-Repetition Circuit Breaker: Suppresses cyclical generation traps when linters, compilers, or shell environments return non-zero exit codes.
• Strict Whitespace & Prompt Hygiene: Eliminates redundant framing tokens, conserving the active context budget for long-sequence programming and multi-turn debugging.

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Recommended Generation Parameters

For repository-level software engineering, bug localization, and automated tool use:

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Inference and Deployment

Serving via vLLM

Thanks to the excision of multi-token prediction layers, the model deploys natively without custom engine flags:

vllm serve pragmaticcs/MiMo-HERO-Frontier-Agentic-Coder-9B \
  --dtype bfloat16 \
  --max-model-len 32768 \
  --gpu-memory-utilization 0.90 \
  --enable-auto-tool-choice \
  --tool-call-parser hermes \
  --enable-reasoning \
  --reasoning-parser qwen3

Serving via SGLang

python -m sglang.launch_server \
  --model-path pragmaticcs/MiMo-HERO-Frontier-Agentic-Coder-9B \
  --port 30000 \
  --host 0.0.0.0 \
  --context-length 32768

Python API Execution

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "pragmaticcs/MiMo-HERO-Frontier-Agentic-Coder-9B"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id, torch_dtype=torch.bfloat16, device_map="auto"
)

conversation = [
    {
        "role": "user",
        "content": "Create a beautiful one-file landing page for a travel agency using HTML, CSS and JavaScript.",
    },
]

inputs = tokenizer.apply_chat_template(
    conversation, add_generation_prompt=True, enable_thinking=True, return_tensors="pt"
).to(model.device)

outputs = model.generate(
    inputs, max_new_tokens=65536, temperature=0.6, top_p=0.95, top_k=20, do_sample=True
)

response = tokenizer.decode(outputs[0][inputs.shape[-1] :], skip_special_tokens=True)
print(response)

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Citation & References

• XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B
• XLearning-SCU/HERO-9B
• Qwen/Qwen3.5-9B
• Improved Chat Template for Qwen 3.x

@article{yu2024dare,
  title={Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch},
  author={Yu, Le and Yu, Bowman and Yu, Haiyang and Huang, Fei and Li, Yongbin},
  journal={arXiv preprint arXiv:2311.03099},
  year={2023}
}

@inproceedings{yadav2023ties,
  title={Resolving Interference When Merging Models},
  author={Yadav, Prateek and Tam, Derek and Choshen, Leshem and Raffel, Colin and Bansal, Mohit},
  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
  volume={36},
  pages={7093--7115},
  year={2023}
}

@article{yang2024deltanet,
  title={Gated Delta Networks: Improving Mamba2 with Delta Rule for Linear Recurrent Attention},
  author={Yang, Songlin and Wang, Bailin and Shen, Yikang and Panda, Rameswar and Kim, Yoon},
  journal={arXiv preprint arXiv:2412.06464},
  year={2024}
}