[!NOTE]
NEW: QWEN3.8 27B DENSE — VAL-APEX-I
Independently computed using dedicated cloud compute clusters, funded 100% out-of-pocket.
If you consider these releases to be of high quality and they help you in your day-to-day workflow, consider supporting on Ko-fi. Every contribution directly sustains future cloud compute hours and helps support my university studies.
Explore the new V3 releases alongside the Huihui Abliterated and EfficientThink Uncensored editions. MiniPlus prioritizes Q5-class to near-Q6 fidelity without a fixed file-size ceiling; NanoPlus targets Q4-class quality at a practical footprint.
- Qwen3.8 27B — VAL-APEX-I MiniPlus V3 — 14.16 GB (+13.21% improvement in WikiText-2 PPL vs GSQ-RCO IQ3_S).
- Qwen3.8 27B — VAL-APEX-I NanoPlus V3 — 11.35 GB (+11.49% improvement in WikiText-2 PPL vs GSQ-RCO IQ3_S).
- Qwen3.8 27B Huihui Abliterated — VAL-APEX-I MiniPlus V2.1 — 15.33 GB (+13.06% improvement in WikiText-2 PPL vs GSQ-RCO IQ3_S).
- Qwen3.8 27B Huihui Abliterated — VAL-APEX-I NanoPlus — 11.90 GB (+9.14% improvement in WikiText-2 PPL vs GSQ-RCO IQ3_S).
- Qwen3.8 27B EfficientThink Uncensored — VAL-APEX-I MiniPlus V2.1 — 15.08 GB (+15.01% improvement in WikiText-2 PPL vs GSQ-RCO IQ3_S).
- Qwen3.8 27B EfficientThink Uncensored — VAL-APEX-I NanoPlus — 11.65 GB (+10.87% improvement in WikiText-2 PPL vs GSQ-RCO IQ3_S).
Percentages compare the reported WikiText-2 PPL with GSQ-RCO IQ3_S (7.07). The linked 27B cards document the evaluation protocol and model variants.
Explore the APEX-I Legacy Archive collection
EXPLORE THE ESTABLISHED 35B MoE MINIPLUS LINEUP
These are complementary APEX-I-MiniPlus V2.1 releases, not alternate downloads of the same model. Each receives the same tensor-by-tensor approach, integrated MTP where supported, and a design suitable for full or partial system-RAM inference. Choose the model whose native strengths best fit the work you want to do:
- Qwen3.6-35B-A3B MTP APEX-I-MiniPlus-V2.1 — a versatile frontier MoE for broad reasoning, multilingual work, agents, tool use, and multimodal tasks.
- Best for: General reasoning, agent workflows, tool calling, and flexible multimodal use.
- Qwen3.6-35B-A3B MTP APEX-I-MiniPlus-V2.1 Abliterated — the V2.1 refusal-ablated Qwen3.6 edition for users who deliberately prefer reduced refusal behavior.
- Best for: Workflows where an abliterated Qwen3.6 variant is explicitly desired.
- Ornith 1.5 APEX-I-MiniPlus-V2.1 — a software-engineering-focused MoE designed for repository-scale coding and autonomous engineering agents.
- Best for: Repository-scale development, multi-file code changes, and software-engineering agents.
- Tiel Coder APEX-I-MiniPlus-V2.1 — a specialist coding MoE tuned for agentic programming, iterative tool use, and implementation-heavy work.
- Best for: Focused coding sessions, iterative debugging, and tool-driven implementation.
These remain distinct model families and editions with their own behavior and empirical results. Pick by workload and intended alignment behavior rather than treating them as interchangeable quantization variants.
Quick Navigation Index
- Model Files & Specifications
- Comparative Quantization Analysis (vs. Flat Quants & Generic APEX)
- Independent Third-Party Benchmark (zephel01 / llmbench)
- Bundled Q8_0 High-Precision Vision Projector
- Everyday Laptop Benchmarks (DDR4 / DDR5 RAM)
- Estimated Projections on Consumer Hardware
- The 24GB Miracle: Full 256K Context Runs In VRAM!
- Hardware Throughput Projections (RTX 30 / 40 / 50)
- Surgical Tensor Quantization Map (Audited from GGUF)
- Recommended Configuration & Setup
- CLI Server Execution Example
- Recommended Generation Parameters (Accio-Lab Official & Empirical)
- Optional Support
[!TIP] Independent external evaluation: zephel01 tested this exact Occamy V2 GGUF on RTX 5090 with llama.cpp. Reported result: 90.0–93.3% L6 resolution and ~247–251 tok/s across repeated runs. Full methodology and limitations.
[!NOTE]
ARCHITECTURE SELECTION GUIDE — MINIPLUS V2 & V2.1 EDITIONS
This repository hosts the MiniPlus V2 edition of Occamy-1.0. Our releases are precision-engineered for specific hardware budgets and memory topologies. V2 is NOT obsolete or "worse"; each edition serves distinct inference requirements:
- MiniPlus V2 (High Theoretical Layer Protection): On paper, V2 provides extra protective envelopes on edge layers (10 layers in
IQ3_S+IQ4_NLshared experts +Q8_0attention gates). However, in practical inference benchmarks—even across extreme long-context windows exceeding +160K tokens—there is virtually NO perceptible difference in quality or reasoning compared to V2.1.- MiniPlus V2.1 (System RAM Streaming Specialist with Deep Context): Specially prepared to run totally or partially in system RAM (DDR4/DDR5) across massive multimodal context windows (up to 256k tokens). By replacing non-linear codebooks with linear
Q3_Kedge experts, keepingQ8_0attention gates, and upgrading shared foundation experts toQ5_Kacross all 40 layers, it completely eliminates AVX2 CPU dequantization stalls (+24 to 28+ tok/s streaming). Depending on your processor and memory bandwidth (DDR4/DDR5), streaming generation in system RAM can be almost as fast as having everything in VRAM, while supporting deep context keeping the dedicatedQ8_0multimodal vision projector (mmproj) explicitly loaded in GPU VRAM for instant OCR and image parsing. It provides this massive RAM streaming acceleration for only approx. 100 MB more, which is completely negligible in system RAM.Which one should you choose? (Official Recommendation: V2.1)
- ⭐ PRIMARY RECOMMENDATION — Occamy-1.0 APEX-I-MiniPlus V2.1: For virtually all users and deployments, V2.1 is the strictly recommended release. Empirically verified on WikiText-2, V2.1 achieves an outstanding Perplexity of 6.2432 ± 0.1622 (ΔPPL ≈ +0.06 from unquantized baseline (approx. 6.18)), matching the token fidelity of Q5_K / Q6_K class quantizations while weighing only approx. 14.7 GB (same footprint as Q3_K_M). Furthermore, it completely eliminates AVX2 CPU stalls, providing blistering +24 to 28+ tok/s streaming under system RAM offload.
- MiniPlus V2 Legacy: Maintained for architectural transparency and users seeking theoretical non-linear edge envelopes on pure 100% VRAM configurations.
Both editions are handcrafted and vastly outperform flat 3-bit quants and generic community APEX-I-Mini releases. To explore or download the V2.1 edition of Occamy-1.0 optimized for system RAM streaming, visit: IsValorum/Occamy-1.0-APEX-I-MiniPlus-V2.1-GGUF
[!WARNING]
DO NOT CONFUSE APEX-I-MINIPLUS WITH GENERIC COMMUNITY APEX-I-MINI!
Regardless of release version (whether V1, V2, or V2.1), NEVER confuse handcrafted APEX-I-MiniPlus builds with generic community APEX-I-Mini releases:
- Generic Community APEX-I-Mini: Uniformly compresses all core MoE experts down to aggressive 2-bit
IQ2_S(dropping below the critical quality floor), leaves the sensitive token output head unarmored at 3-bitQ3_K_M, and compresses attention projections down toQ3_K. In deep reasoning models, this triggers severe perplexity spikes, syntax errors, and broken code brackets.- Handcrafted APEX-I-MiniPlus (All Editions by IsValorum): Every single MiniPlus release—from V1 and V2 to V2.1—is a custom tensor-by-tensor architecture that preserves uncompressed
F32router gates, armors the token output head in high-precisionQ6_K, safeguards attention gates inQ8_0, and keeps core reasoning experts at or above calibrated 3-bit (IQ3_XXS/IQ3_S). Even our earlier builds vastly outperform generic community APEX recipes and flat 3-bit quants.
[!TIP]
SYSTEM RAM INFERENCE: FULL OR PARTIAL
This APEX-I-MiniPlus release is designed for full or partial system-RAM inference. Depending on the processor, memory bandwidth, and DDR4/DDR5 configuration, generation can range from 20 to 45 tok/s. With partial GPU offload, systems that cannot fit 128K or more context entirely in VRAM can place the remaining model and context load in system RAM, maintaining stable, responsive generation at longer context lengths.
Model Files & Specifications
| File Name | File Size | Memory Footprint | BPW | Description |
|---|---|---|---|---|
Occamy-1.0.APEX-I-MiniPlus-V2.gguf |
14.62 GB (13.62 GiB) |
13.62 GiB |
3.38 BPW | Core hybrid linear attention, math, logic & multimodal vision |
mmproj-Accio-Lab_occamy-1.0-Q8_0.gguf |
614 MB (585 MiB) |
585 MiB |
8.50 BPW | Dedicated Q8_0 vision projector for optical document parsing |
- Base Architecture:
Qwen3_5MoeForConditionalGeneration(hybrid linear attention with DeltaNet recurrent layers and 256 micro-experts). - Active Parameters: approx. 3.2B active parameters per token.
- Memory Footprint: 13.62 GiB weight size engineered to fit 256K context natively in 24GB VRAM.
Comparative Quantization Analysis (vs. Flat Quants & Generic APEX)
Also, don't confuse APEX-I-MiniPlus-V2 with a generic baseline APEX-I-Mini. Traditional APEX-I-Mini drops core experts aggressively to 2-bit IQ2_S and leaves output.weight at 3-bit Q3_K_M, which creates a noticeable perplexity hit on complex reasoning tasks. V2 was specifically re-engineered to avoid that quality floor (keeping core experts at calibrated IQ3_XXS, output in Q6_K, shared expert in non-linear IQ4_NL, and routers in F32).
To put the numbers in perspective: this cuts nearly 2 GB off a flat 3-bit quant (approx. 15.6 GB), and weighs only about approx. 1 GB more than a generic APEX-I-Mini (approx. 12.5 GB). For that single extra gigabyte of VRAM, you get a massive jump in reasoning and syntactic stability.
Take a look at the tensor-by-tensor comparison table below to inspect the exact architectural differences and see why this specific allocation is optimal:
| Architectural Component | Generic Automated Quants (Flat Q3_K_S / IQ3_S) |
Generic APEX-I-Mini (Baseline Recipe) | Our Handcrafted APEX-I-MiniPlus-V2 (IsValorum) | Perceived Quality & Real-World Impact |
|---|---|---|---|---|
Output Head (output.weight) |
Flat IQ3_S / Q3_K_S (approx. 3.44 BPW) |
Inherits base type Q3_K_M (approx. 3.44 BPW unarmored) |
Q6_K (approx. 6.56 BPW uncompromised) |
Eliminates Syntax & Vocabulary Hallucinations: Low-bit output heads cause tokenizer classification noise, breaking code indentation, brackets ({}, []), math symbols, and domain terms. Q6_K preserves near-FP16 output classification. |
Expert Routers (ffn_gate_inp.weight) |
Blindly quantized to 3-bit / unoptimized | Inherits base type Q3_K_M (approx. 3.44 BPW compressed) |
F32 uncompressed (32.0 BPW, 2 MB/layer) |
Zero Router Drift: In micro-expert models, even minuscule quantization errors in router logits misdirect tokens to wrong experts. Retaining uncompressed F32 guarantees 100% routing fidelity with virtually zero memory overhead (approx. 80 MB total). |
Attention & Language (attn_output, attn_qkv) |
Flat IQ3_S / Q3_K_S |
Q3_K on 34 middle layers (L3–36), Q4_K on 6 edge layers |
Q6_K for attn_output, IQ3_S for attn_qkv |
Contextual Retrieval Precision: Generic APEX reduces attention and language projections to Q3_K across 85% of layers. Our V2 build protects attention output in high-precision Q6_K and uses calibrated non-linear IQ3_S, ensuring flawless needle-in-a-haystack retrieval across deep 128k–256k context windows. |
Attention Gates (attn_gate.weight) |
Blindly compressed to 3-bit | Compressed to Q3_K (middle) / Q4_K (edges) |
Q8_0 (8.50 BPW) |
Attention Head Stability: Attention gates modulate query-key routing across hybrid attention layers. Keeping them in 8-bit prevents attention crosstalk and hallucination over long contexts. |
Shared Foundation Expert (ffn_*_shexp) |
Flat IQ3_S / Q3_K_S (3.44 BPW) |
Linear Q4_K (middle) / Q5_K (edges) |
IQ4_NL (4.50 BPW non-linear codebook) |
Foundational Knowledge Armor: The shared expert executes for 100% of tokens. In 256 micro-expert models, IQ4_NL non-linear codebooks preserve heavy-tailed outlier representations far better than standard linear quantization. |
| Core MoE Layers (Middle: 10–29) | Flat IQ3_S / Q3_K_S (uniform bit-rate across all layers) |
Aggressive IQ2_S (2.50 BPW) |
IQ3_XXS (3.06 BPW) + calibrated imatrix |
Above the Quality Threshold: Generic 2-bit IQ2_S baselines drop below the critical quality floor for 35B MoEs, resulting in perplexity spikes on reasoning tasks. Our IQ3_XXS with imatrix achieves deep compression (272 MiB → 98 MiB per block) without sacrificing logic. |
| Edge MoE Layers (Layers 0–9 & 30–39) | Flat IQ3_S / Q3_K_S (no layer-wise gradient) |
Q3_K (limited to first/last 5 layers only: L0–4, L35–39) |
IQ3_S (expanded to 10 input & 10 output layers) |
Protected Ingestion & Synthesis: Half of the model's layers (10 at input, 10 at output) form a non-linear armored envelope, preventing prompt misunderstanding and token degeneration across 256 micro-experts. |
Multimodal Vision (mmproj) |
Often omitted, or left as uncompressed FP16 (approx. 900 MB) |
Often omitted or separate uncompressed FP16 |
Bundled Q8_0 (582 MB) with 27 critical F32/F16 fallbacks |
Saves approx. 320 MB VRAM with Zero Loss: Handcrafted quantization preserves normalization and bias tensors in F32/F16, ensuring razor-sharp OCR, DOM viewport reading, and coordinate detection without visual noise. |
| Normalization & Biases | Often degraded | Standard | F32 uncompressed |
Numerical Stability: Prevents cumulative floating-point underflow/overflow across deep 40-layer computation. |
🏅 Independent Third-Party Benchmark (zephel01 / llmbench)
An extensive independent evaluation of Occamy-1.0-APEX-I-MiniPlus-V2-GGUF was published by Japanese AI researcher and evaluator zephel01 (CoolZero) on an NVIDIA RTX 5090 (32GB) workstation running llama.cpp CUDA b11027 with FlashAttention (-fa on -ctk q8_0 -ctv q8_0 -ngl 99).
The model was subjected to 348 unseeded trials across SWE-bench style multi-file Python bug-fixing tasks verified with hidden pytest suites (llmbench):
- L6 Multi-File Code Generation (60 tasks):
- Context 32,768 (32K): 93.3% Resolved (46/60 tasks passed 5/5 consecutive trials; 20/20 on Easy–Hard).
- Context 65,536 (65K): 90.0% Resolved (45/60 tasks passed 5/5 consecutive trials).
- Match with 25–28 GB Models: Matches or exceeds the resolution rate of full 25–28 GB models (such as
Ornith-1.5andTiel-Coder35B-A3B) while consuming over 10 GB less VRAM (14.6 GB vs approx. 26 GB).
- Extreme Context VRAM Scaling (The Hybrid DeltaNet SSM Advantage):
- 32K Context: 14.6 GB total VRAM allocation.
- 65K Context: 15.1 GB total VRAM allocation (only +0.5 GB VRAM added when doubling context!).
- Architectural Explanation: Because 30 of the 40 layers utilize Linear Attention / DeltaNet SSM ($O(1)$ constant recurrence memory), only the 10 full-attention anchor layers expand the KV cache. This proves empirically that 65,536 context runs 100% in VRAM on consumer 16GB GPUs (RTX 4080 / RTX 5080) without offloading to system RAM.
- Measured Real-World Throughput: Sustained single-stream generation of approx. 247 – 251 tok/s on NVIDIA RTX 5090.
Bundled Q8_0 High-Precision Vision Projector
Standard community uploads often omit the multimodal projector or supply uncompressed FP16 files (approx. 857 MB), bloating memory.
- Bundled Q8_0 Projector: Pre-quantized to
Q8_0(585 MiB / 614 MB), saving approx. 300 MB of VRAM. - Audited Layer Fallbacks:
llama.cppautomatically preserved 27 critical normalization and embedding tensors in F32/F16, ensuring razor-sharp OCR of tiny contract footnotes, financial balance sheets, and scanned legal filings without artifacts.
Everyday Laptop Benchmarks (DDR4 / DDR5 RAM)
Estimated Projections on Consumer Hardware
- GPU VRAM Allocation: Uses only approx. 3.8 GB VRAM (fits effortlessly on budget laptop GPUs).
- System Memory Offload: Standard 32GB system RAM accommodates the remaining layers.
- Estimated Document Ingestion (Prefill): 300 to 410+ tokens/second sustained across dense inputs.
- Estimated Streaming Generation: 20 to 22.5+ tokens/second sustained output across system RAM!
The 24GB Miracle: Full 256K Context Runs In VRAM!
Occamy APEX-I-MiniPlus-V2 fits the entire 256K context window within 24GB VRAM:
| Context Length | Model Weights (Est.) | KV Cache (q8_0, 4 slots) | Compute Buffers | Total GPU VRAM (Est.) | Hardware Feasibility |
|---|---|---|---|---|---|
| 32,768 (32k) | 13.62 GiB |
0.58 GiB |
1.80 GiB |
16.00 GiB |
Full offload on 24GB; partial on 16GB |
| 65,536 (64k) | 13.62 GiB |
0.92 GiB |
1.95 GiB |
16.49 GiB |
Effortless fit on 24GB GPUs |
| 131,072 (128k) | 13.62 GiB |
1.58 GiB |
2.22 GiB |
17.42 GiB |
Effortless fit on 24GB GPUs |
| 262,144 (256k) | 13.62 GiB |
2.92 GiB |
2.80 GiB |
19.34 GiB |
FULL 256K NATIVE IN VRAM! |
Note: Leaves approx. 4.66 GiB of headroom for display drivers and the Q8 vision projector on 24GB cards.
[!TIP]
💡 Empirical 16GB GPU Verification (Single Stream / Desktop)
While theoretical multi-slot server buffers estimate approx. 16.49 GiB, independent hardware testing by zephel01 on an RTX 5090 confirmed that single-stream desktop inference consumes only 14.6 GB at 32,768 ctx and only 15.1 GB at 65,536 ctx (
-ctk q8_0 -ctv q8_0 -fa on). This empirically proves that full 65K context runs completely in VRAM on 16GB cards (RTX 4080 / RTX 5080) without system RAM offload!
Hardware Throughput Projections (RTX 30 / 40 / 50)
| Hardware Target | Offload Mode | Generation Speed (Est.) | Prompt Prefill Speed (Est.) | Highlights |
|---|---|---|---|---|
| NVIDIA RTX 5080 / 5090 (Blackwell) | Full GPU (-ngl 99) |
approx. 247 – 251 tok/s | 2,800 – 3,900+ tok/s | Empirically verified on RTX 5090 by zephel01 |
| NVIDIA RTX 4090 (24GB GDDR6X) | Full GPU (-ngl 99) |
80 – 105+ tok/s | 1,800 – 2,600+ tok/s | Linear attention layers slash prefill latency |
| NVIDIA RTX 3090 (24GB GDDR6) | Full GPU (-ngl 99) |
66 – 80+ tok/s | 1,400 – 2,000+ tok/s | Full 256k native window in VRAM |
| Consumer Laptop (RTX 3050 + DDR4) | Hybrid (3.8GB VRAM) | 20 – 22.5+ tok/s | 300 – 410+ tok/s | Smooth streaming from system RAM |
Surgical Tensor Quantization Map (Audited from GGUF)
The exact tensor breakdown below has been verified directly from the compiled binary weights:
| Layer Group | Sub-Component / Tensor | Qty | Precision | Engineering Rationale |
|---|---|---|---|---|
| Global Output Head | output.weight |
1 | Q6_K |
Preserves near-FP16 token classification; eliminates syntax errors, bracket drops, and hallucinations. |
| Global Embeddings | token_embd.weight |
1 | Q4_K |
High-fidelity vocabulary embedding representation. |
| All Normalizations | output_norm, attn_*_norm, ssm_norm |
171 | F32 |
100% uncompressed numerical stability across all 40 layers. |
| Expert Routers | blk.*.ffn_gate_inp, ffn_gate_inp_shexp |
80 | F32 |
100% uncompressed routing fidelity across 256 micro-experts; zero router drift. |
| Attention Gates | blk.*.attn_gate.weight (30 Hybrid Layers) |
30 | Q8_0 |
High-precision attention gating across hybrid DeltaNet recurrence layers; eliminates crosstalk. |
| Shared Foundation Experts | blk.*.ffn_{gate,down,up}_shexp (All 40 Layers) |
120 | IQ4_NL |
Foundational knowledge backbone; non-linear codebook preserves heavy-tailed outlier activations. |
| Periodic Full Attention | blk.{3,7,11,...}.attn_output (10 Anchor Layers) |
10 | Q6_K |
Armored attention output projection over deep context. |
| Recurrent SSM Scales | blk.*.ssm_alpha, ssm_a, ssm_conv1d, ssm_dt |
120 | F32 |
Guarded in uncompressed FP32 to prevent DeltaNet recurrent state drift. |
| Edge MoE Experts | Layers 0–9 & 30–39 (ffn_*_exps) |
60 | IQ3_S |
Non-linear codebook protective envelope at model ingestion and terminal synthesis. |
| Core MoE Experts | Layers 10–29 (ffn_*_exps) |
60 | IQ3_XXS |
Calibrated with importance matrix (imatrix) for maximum compactness in deep layers. |
Recommended Configuration & Setup
CLI Server Execution Example
llama-server.exe \
-m Occamy-1.0.APEX-I-MiniPlus-V2.gguf \
--port 8080 \
--flash-attn on \
--fit on \
-c 65536 \
--cache-type-k q8_0 \
--cache-type-v q8_0
⚙️ Recommended Generation Parameters (Accio-Lab Official & Empirical)
Recommended sampling configuration tailored from Accio-Lab and third-party SWE-bench evaluations:
| Scenario / Workload | Temperature | Top-P | Top-K | Additional Hyperparameters | Rationale |
|---|---|---|---|---|---|
| Co-Work, Agentic & Tool-Use (Official) | 1.00 |
0.95 |
20 |
presence_penalty: 1.5, enable_thinking: true |
Official Accio-Lab recommendation for multi-turn co-work and tool execution. |
| Deterministic Coding & Scripting (Author Recommended) | 0.20 – 0.35 |
0.95 |
20 |
min_p: 0.05, repetition_penalty: 1.05 |
Author recommendation for strict deterministic syntax, code completion, and preventing token repetition in automated agent loops. |
| Independent SWE-Bench Baseline (zephel01 / llmbench) | 0.35 |
0.95 |
20 |
min_p: 0.0 (disabled), repetition_penalty: 1.0 (none) |
Fixed comparative evaluation setting utilized by zephel01 across 348 unseeded trials in llmbench (achieving 90.0%–93.3% SWE-bench resolution). |
| General Multilingual Instruction | 0.60 – 0.70 |
0.95 |
20 |
repetition_penalty: 1.08 |
Balanced creativity and factual accuracy across 13+ languages. |
[!IMPORTANT]
🔍 Model Inherent Behavior vs. Quantization Fidelity Notice
Any behavioral limitations, stylistic habits, or syntactic oversights (such as occasional omitted standard library imports in automated zero-shot scripts, e.g., missing
from decimal import ROUND_HALF_UPor@dataclass) stem entirely from the original unquantized base model weights and pre-training distribution, NOT from the APEX-I quantization process. Handcrafted APEX-I-MiniPlus strictly preserves mathematical tensor fidelity—keeping 100% of expert routing matrices (gate_inp) in uncompressedF32(zero router drift), armoring the token output head inQ6_K, and safeguarding attention gates inQ8_0. Empirical verification confirms near-zero perplexity loss (ΔPPL ≈ +0.06), ensuring that token logits, routing decisions, and reasoning trajectories are mathematically faithful to the original base model.
[!TIP]
💡 Developer Tip for Autonomous Coding & CI Agents (Import Discipline)
In third-party evaluations, reasoning and architectural code generation scored a remarkable 90%–93.3% resolution rate on multi-file SWE benchmarks. Any rare failures observed were not reasoning flaws, but occasional omitted standard library imports in zero-shot scripts (e.g.
from decimal import ROUND_HALF_UPor@dataclass). When deploying in autonomous coding agents, include in your system prompt: "Always declare complete, explicit import statements at the beginning of the file" or pair with an automated linter (ruff).
7. Community Compute Fund & Student Support
All IsValorum quantizations will always remain completely free and open to the public without paywalls.
However, cloud GPU compute is expensive and funded entirely out-of-pocket. If you find these handcrafted builds to be of high quality and they benefit your day-to-day workflow, you can support future Day-0 releases on Ko-fi. Every contribution directly funds the cloud compute hours required for new model runs and helps support my university education:
(When supporting on Ko-fi, feel free to leave a note with your Hugging Face handle and the specific model you would like prioritized).