SaaS Metrics
Gemini Executive Synthesis
Integration of `snap1-2b` (MiniCPM5-2B + LoRA) into Ollaya, leveraging GGUF format for efficient deployment.
Technical Positioning
Achieving high accuracy (0.655-0.660) with a compact 2B parameter model, optimized for one-forward-pass decision-making. Strict adherence to prompt versioning and byte-for-byte parity checks ensures consistent, reliable model behavior across different environments. Prioritizing Q8_0 quantization for broader compatibility while offering Q4_K_M for smaller machines.
SaaS Insight & Market Implications
This discussion reveals a critical focus on model accuracy, deployment efficiency, and rigorous validation within the decision model space. The `snap1-2b` proposal, utilizing LoRA and GGUF, targets optimized performance for specific decision tasks, demonstrating a clear market need for specialized, high-accuracy models that are also resource-efficient. The extensive discussion around prompt porting, byte-for-byte parity, and golden outputs underscores the significant developer pain point of ensuring consistent model behavior across diverse deployment environments. Ollaya's stringent validation process, including `llama-server` parity checks and detailed accuracy measurements, establishes a high bar for model integration, mitigating risks associated with model drift or unexpected behavior. This commitment to reliability is a strong market differentiator, appealing to enterprises requiring predictable and auditable AI deployments.
Proprietary Technical Taxonomy
Raw Developer Origin & Technical Request
GitHub Issue
Oct 1, 2026
Repo: ollaya-dev/ollaya
Support snap1-2b (MiniCPM5-2B + LoRA, letter readout, GGUF)
Hi! We'd like to propose **snap1-2b** for the Ollaya library.
- **Model:** huggingface.co/logitlab/snap1-2b... (Apache-2.0).
MiniCPM5-2B + LoRA (rank 16), merged. Files: `snap1-2b-q4_k_m.gguf` (1.5 GB,
default), `q8_0` (2.7 GB), `bf16` (5.0 GB). Q4_K_M sha256
`0bee5ed145f7d6db8427a68ca2b331083be6dc09347253fd279bec7ddeaece6a`.
- **Engine and prompt:** github.com/emnlmn/snap (MIT), tag
[`v0.5.0`](github.com/emnlmn/snap/relea... The prompt is
compiled in [`src/prompts.rs`](github.com/emnlmn/snap/blob/...
(`PROMPT_VERSION` 6): one system message, lettered options, one answer slot.
The model reads the answer from the option-letter logits, so like Cygnet it
fits the one-forward-pass design. It is trained on the v5 prompt bytes; v6
renders yes/no outcome descriptions as `Yes:` / `No:` lines and falls back
to the option key when a description is null, and ordinary requests are
byte-identical to v5. v0.4.0 (v5) works too, at 0.643 instead of 0.655.
- **Wire format:** `POST /v1/systemone` with Jev's request and response shapes.
- **Our measurement** (snap 0.5.0, Q4_K_M; typed-decisions test split, 400
states and 2,000 questions, zero-shot: the checkpoint never trained on any
typed-decisions split or workflow; raw probabilities, no calibration file):
| | MiniCPM5-2B | snap1-2b |
|---|---:|---:|
| accuracy (M1 Max) | 0.486 | 0.655 |
| accuracy (RTX 4090, CUDA) | – | 0.660 |
| KL from g...
Developer Debate & Comments
@emnlmn, thanks, this is a clear proposal and it fits the GGUF path well. Like Cygnet, it reads letter logits after one prompt, so it is one forward pass per question. We would like to add it. How Ollaya would bring it in: 1. **Port the prompt.** `src/prompts.rs` at v0.4.0 (`PROMPT_VERSION` 5) goes into a Python reference and the Rust layout. Both are checked byte for byte against snap's own prompts on our shared request set, which includes the edge cases: control tokens in user text, long JSON, and more options than letters. 2. **Gate on parity.** Goldens come from stock `llama-server` of our pinned llama.cpp build (b11146) on the device under test. The gate is identical prompt ids, the same decisions, and option logits within 1e-3. 3. **Measure.** Typed-decisions accuracy and latency through Ollaya's own runtime, published next to the other models on ollaya.dev/results. Your offer of golden outputs is welcome. The most useful form would be, for each request, the prompt token ids s...
@cobanov thanks, this plan works for me. Updates since your reply: - **Quantization**: we measured Q8_0 against Q4_K_M on typed-decisions (snap 0.5.0, M1 Max): 0.655 vs 0.654 accuracy, so Q4_K_M loses nothing measurable. Q4_K_M is our default for size; Q8_0 is equivalent, use whichever fits your policy. - **Prompt:** v0.5.0 is out (brew and release assets), `PROMPT_VERSION` 6. It renders the yes/no outcome descriptions as `Yes:` / `No:` lines and falls back to the option key for null descriptions; ordinary requests are byte-identical to v5. Accuracy on typed-decisions is 0.655, from 0.643 on v0.4.0 (v5). We'd suggest v0.5.0 as the porting target, with v0.4.0 (v5) as the fallback if you've already started. I've updated the numbers in the issue body and the reproduce steps. - **Goldens:** `snap export-prompts` dumps the rendered prompts and letter slots of a JSONL set. We can add the token ids and the stock llama-server logits in whatever format you prefer. Could y...
@emnlmn, thanks for the numbers and the v0.5.0 notes. Plan: - **Prompt.** We port v0.5.0 (`PROMPT_VERSION` 6). - **Quantization.** The default tag will be Q8_0: our rule is Q8_0 wherever it fits, and at 2.7 GB it fits everywhere. Since Q4_K_M measures the same, it can be a second tag for small machines. - **Goldens.** We write our own from stock `llama-server` (pinned b11146) on each device we support, so the gate is reproducible on our hardware. What helps most from your side is an independent check of the prompt port. Here is our shared request set as JSONL, one `{"id", "state", "questions"}` per line, the requests exactly as a client sends them: [ollaya-shared-requests.jsonl](https://gist.github.com/cobanov/dffe49d26ce328aa97716c3085249fef) (121 requests: the edge cases and 40 typed-decisions rows, sha256 `c95b85a2…24c09e30`). If you can run `snap export-prompts` on it and post the output, that is enough: for each request, the rendered prompt per question, the letter slots and, if ...
Adjacent Repository Pain Points
Other highly discussed features and pain points extracted from ollaya-dev/ollaya.
Extracted Positioning
Request for aarch64 CUDA support for Ollaya, specifically for NVIDIA GB10 GPUs on DGX Spark platforms.
Ollaya aims to provide GPU acceleration across diverse architectures, but prioritizes verification on real hardware. It leverages existing `llama.cpp` CUDA builds for GGUF models (e.g., `winnow`) as a realistic first step, while acknowledging the greater effort required for ONNX models due to lack of official aarch64 CUDA builds from Microsoft. The core principle is rigorous "parity gate" testing before shipping.
Top Replies
Good question. There are two engines, and aarch64 CUDA differs between them: - **GGUF models on llama.cpp** (`winnow`, `jevk5`): llama.cpp b11146, the build Ollaya pins, ships `ubuntu-cuda-13.4-arm...
I also have a DGX Spark (aarch64, GB10) and would be happy to help test the ARM64 CUDA pack if needed. Share the build and parity-check commands, and I can run them and report the results and logs.
Thanks @drsect0r and @Xe, that helps a lot. Here's the parity check for `winnow:e4b` on llama.cpp's CUDA backend on a GB10. It's the same gate every device passes: Ollaya's runner against a stock `...
Extracted Positioning
Feature request for integrated local training capabilities (`ollaya train`) for System 1 decision models, specifically using LoRA, to allow users to fine-tune models on proprietary datasets.
Ollaya maintains its core identity as an "ultra-fast, calibrated in-process runtime" for inference, strictly avoiding bloat from training dependencies. The platform aims to facilitate the *running* of custom-trained models by defining an import path for LoRA adapters and calibration parameters, rather than integrating the training process itself. This ensures a lightweight core while supporting user customization.
Top Replies
To follow up on this proposal, here are some thoughts on how the architecture could be cleanly designed **without bloating the core Ollaya runtime**: ### 1. Absolute Rule: Zero Bloat in Core Ollaya...
@solarpush, thanks for the proposal and the follow-up on keeping the core lean. Sorry it took a while to answer. We have decided not to put training into Ollaya itself. Ollaya runs authors' models ...
Extracted Positioning
Integration of Decima, a 122M parameter System One decision model, into the Ollaya library.
Targeting low-end CPU inference with high performance (~20 ms per decision on one x86 core, int8), supporting TypeSafe API for `choice`, `noul`, and `score` questions. Emphasizes local, efficient decision model serving.
Frequently Asked Questions
Market intelligence mapped to Integration of `snap1-2b` (MiniCPM5-2B + LoRA) into Ollaya, leveraging GGUF format for efficient deployment..
What is the technical positioning of Integration of `snap1-2b` (MiniCPM5-2B + LoRA) into Ollaya, leveraging GGUF format for efficient deployment.?
Based on our AI analysis of the original developer request, its primary technical positioning is: Achieving high accuracy (0.655-0.660) with a compact 2B parameter model, optimized for one-forward-pass decision-making. Strict adherence to prompt versioning and byte-for-byte parity checks ensures consistent, reliable model behavior across different environments. Prioritizing Q8_0 quantization for broader compatibility while offering Q4_K_M for smaller machines.
Are engineers actively discussing Integration of `snap1-2b` (MiniCPM5-2B + LoRA) into Ollaya, leveraging GGUF format for efficient deployment.?
Yes, we have tracked 3 direct responses and active debates regarding this specific topic originating from GitHub Issue.
What are the foundational technologies related to Integration of `snap1-2b` (MiniCPM5-2B + LoRA) into Ollaya, leveraging GGUF format for efficient deployment.?
Our proprietary extraction maps Integration of `snap1-2b` (MiniCPM5-2B + LoRA) into Ollaya, leveraging GGUF format for efficient deployment. to adjacent architectural concepts including snap1-2b, MiniCPM5-2B, LoRA (rank 16), GGUF.
Engagement Signals
Cross-Market Term Frequency
Quantifies the cross-market adoption of foundational terms like MIT license and accuracy by tracking occurrence frequency across active SaaS architectures and enterprise developer debates.