pragmaticcs/PentaCoder-9B-GGUF

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[!NOTE] This repository provides quantized GGUF builds of pragmaticcs/PentaCoder-9B

Most sub-10B coding models fail in realistic agentic environments due to a common trade-off: aggressive fine-tuning on synthetic coding instructions improves immediate benchmark pass rates but introduces brittle syntactic degradation and catastrophic repetition loops when shell commands or compiler checks fail.

PentaCoder addresses these limitations by uniting five specialized post-trained checkpoints of Qwen 3.5 9B via GeoDELLA (Geometric Drop-and-Rescale with Task-Covariance De-Biasing and Spectral Norm Anchoring). The model synthesizes the distinct mathematical distributions of each donor:

• Algorithmic Correctness & Architectural Decomposition from Qwopus3.5-Coder (Claude 3.5 Opus distillation trajectories).
• Multi-Turn SWE-bench Planning & Tool Protocol Integrity from MiMo-V2.6-Distill (Large-scale agentic execution traces).
• Terminal Execution Discipline & Error-Recovery Heuristics from Ornith-1.5 (Reinforcement learning for anti-looping).
• Low-Level Systems Implementation & Runtime Robustness from OxCoder (Deep API, CLI, and operational coding specialization).
• Abstract Structural Reasoning & Syntax Grounding from Qwen3.8-Distill (Distilled frontier chain-of-thought representations).

[!IMPORTANT] The result is a lean, blisteringly fast 9B pure-text causal engine with a native 256k context window that runs comfortably on consumer GPUs.

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Contents

• Architectural Specifications
• Composition & Donor Weighting
• Merge Methodology & Mathematical Formulation
• Layer-Stratified Component Policies
• Agentic Chat Template & Operational Directives
• Recommended Generation Parameters
• How to Use
• Citation & References

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Architectural Specifications

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Composition & Donor Weighting

The foundational weights of Qwen/Qwen3.5-9B serve as the shared topological base (W₀). Five specialized donor checkpoints provide non-overlapping task vectors mapped across normalized layer depth u ∈ [0, 1]:

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Merge Methodology & Mathematical Formulation

The merge was executed using the GeoDELLA-Coder pipeline, which solves weight interference through dynamic task-correlation de-biasing, row-wise sign consensus, sub-tensor GQA disentanglement, and power-iteration spectral norm stabilization.

1. Task Vector Formulation

For each donor checkpoint k ∈ {1, ..., 5}, the parameter update delta τ_k is computed relative to the base anchor W₀:

$$ \tau_k = D_k - W_0, \quad k \in {\text{Qwen3.8}, \text{Qwopus}, \text{Ornith}, \text{OxCoder}, \text{MiMo}} $$

2. Hybrid-Aware Continuous Depth Modulation

Task vector mixing coefficients are continuously modulated over normalized depth u = l / (L - 1), where l ∈ [0, 31] and L = 32:

$$ u_{\text{qwen38}}(u) = 0.15 + 0.35 \cos^2\left(\frac{\pi}{2} u\right) + 0.15 \sin^2(\pi u) $$

$$ u_{\text{qwopus}}(u) = 0.05 + 0.35 \sin^2(\pi u) $$

$$ u_{\text{ornith}}(u) = 0.05 + 0.35 \sin^2\left(\frac{\pi}{2} u\right) $$

$$ u_{\text{oxcoder}}(u) = 0.05 + 0.25 \sin^2\left(\frac{\pi}{2} u\right) $$

$$ u_{\text{mimo}}(u) = 0.05 + 0.15 \sin(\pi u) $$

The initial coefficients are normalized to form a partition of unity across all layers:

$$ w_k(l) = \frac{u_k(u)}{\sum_{j=1}^5 u_j(u)}, \quad \sum_{k=1}^5 w_k(l) = 1.0 $$

3. Dynamic Gram-Matrix Task De-Biasing

Fine-tuned models frequently share underlying distillation datasets, leading to collinearity that can drown out specialized task vectors. To correct for this over-representation, the empirical Gram correlation matrix G ∈ ℝ^{K × K} is evaluated per tensor:

$$ G_{ij} = \frac{|\langle \text{vec}(\tau_i), \text{vec}(\tau_j) \rangle|}{|\tau_i|_2 |\tau_j|_2} $$

A uniqueness coefficient U_k is derived from the inverse column sum of task vector correlations:

$$ U_k = \frac{1}{\sum_{j=1}^K G_{kj}} $$

The dynamic weights are then re-balanced and normalized:

$$ \tilde{w}k = \frac{w_k U_k}{\sum{j=1}^K w_j U_j} $$

This prevents dataset overlap from suppressing distinct algorithmic representations.

4. Asymmetric GQA Disentangled QKV Slicing

Qwen 3.5 9B features asymmetric head ratios in both its linear attention and full attention layers. Standard fused tensor merging causes cross-head pollution by treating routing projections identically to memory projections.

Fused projection tensors are sliced into their functional sub-matrices prior to merging:

• Gated DeltaNet Layers (8192 × d_model): Sliced into Query (2048), Key (2048), and Value (4096).
• Gated Attention Layers (6144 × d_model): Sliced into Query (4096), Key (1024), and Value (1024).

Query and Key slices are processed with a conservative retention density (ρ = 0.95) to preserve sharp context routing. Value matrices are processed with adaptive MLP density (ρ = 0.70) to maximize conceptual synthesis. The components are then re-concatenated along the head dimension.

5. Neuron-Coherent Row Gating

To eliminate destructive interference in Feed-Forward Networks (MLPs), task vectors are gated at the single-neuron (row) level:

$$ \bar{\tau} = \frac{1}{K} \sum_{k=1}^K \tau_k $$

For each row r of donor delta τ_k, the directional alignment with the consensus mean is evaluated:

$$ \cos \theta_{k, r} = \frac{\langle \tau_{k, r}, \bar{\tau}r \rangle}{|\tau{k, r}|_2 |\bar{\tau}_r|_2 + \epsilon} $$

Rows exhibiting severe directional opposition (cos θ_{k, r} < -0.10) are masked out:

$$ \hat{\tau}{k, r} = \tau{k, r} \cdot \mathbb{I}\left(\cos \theta_{k, r} \ge -0.10\right) $$

This eliminates opposing gradient vectors that produce incoherent syntax generation.

6. Heavy-Tailed DELLA Adaptive Rescaling

Surviving parameters undergo non-linear magnitude-based sampling. Using parameter rank indices R_{k, ij} ∈ [0, 1] sorted by absolute magnitude, a Pareto-style retention probability p_{k, ij} is established:

$$ p_{k, ij} = p_{\min} + (p_{\max} - p_{\min}) \cdot \sqrt{R_{k, ij}} $$

Parameters are sampled via a Bernoulli trial and rescaled by their inverse survival probability:

$$ M_{k, ij} \sim \text{Bernoulli}(p_{k, ij}) $$

$$ \tilde{\tau}{k, ij} = \frac{\hat{\tau}{k, ij} \odot M_{k, ij}}{p_{k, ij}} $$

7. Coordinate-Wise Sign Consensus & Model Stock Scaling

Directional consensus is determined via weighted sign agreement:

$$ \Gamma = \operatorname{sgn}\left(\sum_{k=1}^K \tilde{w}_k \tilde{\tau}_k\right) $$

$$ A_k = \mathbb{I}\left(\operatorname{sgn}(\tilde{\tau}_k) = \Gamma\right) \odot \mathbb{I}\left(\tilde{\tau}_k \neq 0\right) $$

$$ \Delta_{\text{consensus}} = \frac{\sum_{k=1}^K \tilde{\tau}k \odot A_k}{\sum{k=1}^K A_k + \epsilon} $$

The aggregated delta is projected onto the non-linear manifold using the Model Stock analytic scaling factor t^*:

$$ \bar{\rho} = \frac{2}{K(K-1)} \sum_{i < j} \frac{\langle \text{vec}(\tau_i), \text{vec}(\tau_j) \rangle}{|\tau_i|_2 |\tau_j|_2} $$

$$ t^* = \frac{K \bar{\rho}}{1 + (K - 1)\bar{\rho}} $$

$$ \Delta_{\text{final}} = t^* \cdot \Delta_{\text{consensus}} $$

8. Spectral Norm & Attention Entropy Anchoring

Attention projection matrices (W_attn) are vulnerable to spectral explosion during merges, which contracts attention entropy and leads to repetitive generation loops.

The dominant singular value σ(W) is calculated via a three-iteration deterministic power iteration:

$$ v^{(t+1)} = \frac{W^T u^{(t)}}{|W^T u^{(t)}|_2}, \quad u^{(t+1)} = \frac{W v^{(t+1)}}{|W v^{(t+1)}|_2} $$

$$ \sigma(W) \approx {u^{(3)}}^T W v^{(3)} $$

If the merged spectral radius grows more than 5% relative to the base model, it is scaled down:

$$ W_{\text{final}} = \begin{cases} W_{\text{merged}} \cdot \left(\frac{1.05 \cdot \sigma(W_0)}{\sigma(W_{\text{merged}})}\right) & \text{if } \sigma(W_{\text{merged}}) > 1.05 \cdot \sigma(W_0) \ W_{\text{merged}} & \text{otherwise} \end{cases} $$

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Layer-Stratified Component Policies

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Agentic Chat Template & Operational Directives

This model uses the Improved Chat Template for Qwen 3.x by Olivia Rossi to support multi-tier Chain-of-Thought (CoT) reasoning, dual-format agentic tool execution, automatic error-recovery heuristics, and strict token-waste elimination.

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Recommended Generation Parameters

For deterministic software engineering and complex reasoning benchmarks:

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Citation & References

• Qwen/Qwen3.5-9B
• XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B
• ornith-ai/Ornith-1.5-9B
• Jackrong/Qwopus3.5-9B-Coder
• OrionLLM/OxCoder-9B
• empero-ai/Qwen3.8-9B-Distill
• Improved Chat Template for Qwen 3.x

@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{deep2024della,
  title={DELLA-Merging: Reducing Interference in Model Merging through Magnitude-Based Sampling},
  author={Deep, Pala Tej and Bhardwaj, Rishabh and Poria, Soujanya},
  journal={arXiv preprint arXiv:2406.11617},
  year={2024}
}

@article{jang2024modelstock,
  title={Model Stock: All We Need Is just a Few Fine-Tuned Models},
  author={Jang, Dong-Hwan and Yoon, Sang-Doo and Song, Gyeong-Moon},
  journal={arXiv preprint arXiv:2403.19522},
  year={2024}
}