Instructions to use solarpush/push-3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use solarpush/push-3b with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf solarpush/push-3b
# Run inference directly in the terminal:
llama cli -hf solarpush/push-3b

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf solarpush/push-3b
# Run inference directly in the terminal:
llama cli -hf solarpush/push-3b

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf solarpush/push-3b
# Run inference directly in the terminal:
./llama-cli -hf solarpush/push-3b

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf solarpush/push-3b
# Run inference directly in the terminal:
./build/bin/llama-cli -hf solarpush/push-3b

Use Docker

docker model run hf.co/solarpush/push-3b

LM Studio
Jan
Ollama
How to use solarpush/push-3b with Ollama:
```
ollama run hf.co/solarpush/push-3b
```
Unsloth Desktop

How to use solarpush/push-3b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf solarpush/push-3b

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "solarpush/push-3b"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use solarpush/push-3b with Docker Model Runner:
```
docker model run hf.co/solarpush/push-3b
```

Lemonade

How to use solarpush/push-3b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull solarpush/push-3b

Run and chat with the model

lemonade run user.push-3b-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use solarpush/push-3b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf solarpush/push-3b

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default solarpush/push-3b

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use solarpush/push-3b with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf solarpush/push-3b

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "solarpush/push-3b" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

push-3b / README.md

solarpush

Upload folder using huggingface_hub

435ea01 verified 4 days ago

preview code

Raw History Blame Contribute Delete

6.66 kB

---
license: apache-2.0
library_name: gguf
pipeline_tag: text-classification
base_model: mistralai/Ministral-3-3B-Instruct-2512
base-model: mistralai/Ministral-3-3B-Instruct-2512
language:
  - en
  - de
  - fr
  - es
  - multilingual
tags:
  - ollaya
  - ollama
  - trainaya
  - decision-model
  - system-1
  - llm-logits-v1
  - gguf
  - calibrated
  - ministral
datasets:
  - ZefanCai/Open-Jev-v1.1
  - SargeDev/jev-distill-corpus-v3
  - tasksource/tasksource-jev-typed-decisions
---
# push:3b — Ultra-Fast Calibrated System-1 Decision Model (3B GGUF)

**`push:3b`** (`solarpush/push-3b`) is an ultra-low-latency (`86 ms` p50, `2.31 GiB` on disk, `~4.2 GB` VRAM), calibrated **System-1 Decision Model** (`llm-logits-v1`) designed for single-forward-pass structured decision-making across three universal primitives:

- **`choice`** — Multi-way categorical selection (`A`..`Z`, 2 to 26 options),
- **`noul`** — Binary epistemic / policy / safety / entailment verification (`Yes` vs `No`),
- **`score`** — Ordinal rubric & severity evaluation (`0`..`9`, 3- to 5-level calibrated scales).

Built on a dense **3B parameter** GGUF backbone (`Q4_K_M`, `2.31 GiB`) with an untied **`F16` (`ttype=1`) subspace-orthogonal decision head** (`[131072, 3072]`) and cardinality-conditioned temperature scaling (`temperature_by_options`), `push:3b` achieves **`3.1%` Pooled Public ECE**, **`85.7%` accuracy on `civil_comments`**, **`84.0%` on `summeval-consistency`**, **`74.6%` on `squad2`**, and **`73.2%` on `pubmedqa`**.
---
## 1. Official Ollaya Decision Benchmark (`bench.ollaya.dev` — 14 Tasks, 5,880 Questions)
Evaluated strictly zero-shot / out-of-domain across all **14 official `bench.ollaya.dev` tasks** (`3,880` Public benchmark questions across 13 datasets + `2,000` `typed-decisions` workflow questions across 400 multi-question JSON states = **`5,880` total questions**, zero benchmark train/dev/test overlap):
| Model | Overall Skill `[95% CI]` | Public Skill *(13 Tasks)* | Public Macro Acc | Typed-Decisions Acc *(Skill)* | Public Pooled ECE *(lower is better)* | Latency *(p50)* | VRAM | License |
| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| **`push:3b`** *(Adapted 3B `v12+`)* | **`46.0` `[43.9, 48.2]`** | **`47.1`** | **`64.8%`** | **`53.9%` (`32.5`)** | **`3.1%` (`0.031`)** | **`86 ms`** | **`4.2 GB`** | `Apache-2.0` |
| `push_ministral:3b` *(Baseline Adapted 3B)* | `44.1` `[42.0, 46.2]` | `45.3` | `63.7%` | `50.6%` (`27.6`) | `6.5%` (`0.065`) | `86 ms` | `4.2 GB` | `Apache-2.0` |
| **`push:26b`** *(26B-A4B MoE)* | **`62.7` `[60.8, 64.7]`** | **`63.1`** | **`75.4%`** | **`71.4%` (`58.1`)** | **`2.8%` (`0.028`)** | `223 ms` | `15.0 GB` | `Apache-2.0` |

---

## 2. Per-Task Breakdown (14 Tasks — 5,880 Questions)

| Benchmark Task | Primitive | Questions | `push:3b` (`v12+`) Accuracy | `push:3b` Skill *(0–100)* | `push_ministral:3b` Baseline *(Skill)* | Delta vs Baseline | `push:3b` ECE *(10-bin)* | Median Latency |
| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| **`civil_comments`** *(Toxicity Detection)* | `noul` | 300 | **`85.7%`** | **`71.3`** | `83.7%` (`67.3`) | **`+2.0%` (`+4.0` Skill)** | **`0.043`** | `51 ms` |
| **`summeval-consistency`** *(Factual Consistency)* | `score` | 144 | **`84.0%`** | **`80.0`** | `83.3%` (`79.2`) | **`+0.7%` (`+0.8` Skill)** | `0.151` | `102 ms` |
| **`massive-en-US`** *(18-Way Intent)* | `choice` | 350 | **`76.0%`** | **`74.6`** | `75.7%` (`74.3`) | **`+0.3%` (`+0.3` Skill)** | **`0.064`** | `65 ms` |
| **`squad2`** *(Unanswerable QA)* | `noul` | 299 | **`74.6%`** | **`49.2`** | `75.6%` (`51.2`) | `-1.0%` | **`0.056`** | `67 ms` |
| **`boolq`** *(Reading Verification)* | `noul` | 300 | **`74.3%`** | **`48.7`** | `75.7%` (`51.3`) | `-1.4%` | **`0.039`** | `59 ms` |
| **`pubmedqa`** *(Biomedical 3-Way QA)* | `choice` | 250 | **`73.2%`** | **`59.8`** | `74.4%` (`61.6`) | `-1.2%` | **`0.069`** | `90 ms` |
| **`massive-de-DE`** *(18-Way Intent DE)* | `choice` | 350 | **`72.3%`** | **`70.7`** | `72.3%` (`70.7`) | `0.0%` | **`0.037`** | `67 ms` |
| **`aegis2`** *(AI Safety Guardrail)* | `noul` | 250 | **`64.8%`** | **`29.6`** | `68.4%` (`36.8`) | `-3.6%` | `0.125` | `54 ms` |
| **`multinli`** *(3-Way NLI)* | `choice` | 299 | **`62.2%`** | **`43.3`** | `55.5%` (`33.3`) | **`+6.7%` (`+10.0` Skill)** | `0.072` | `46 ms` |
| **`paws`** *(Adversarial Paraphrase)* | `noul` | 250 | **`60.0%`** | **`20.0`** | `61.2%` (`22.4`) | `-1.2%` | `0.105` | `50 ms` |
| **`vitaminc-dev`** *(3-Way Fact Verification)* | `choice` | 599 | **`58.1%`** | **`37.1`** | `52.9%` (`29.4`) | **`+5.2%` (`+7.7` Skill)** | **`0.048`** | `55 ms` |
| **`typed-decisions`** *(400 JSON Workflows)* | `mixed` | 2000 | **`53.9%`** | **`32.5`** | `50.6%` (`27.6`) | **`+3.3%` (`+4.9` Skill)** | `0.153` | `110 ms` |
| **`helpsteer2`** *(5-Level Helpfulness)* | `score` | 249 | **`33.7%`** | **`17.2`** | `35.3%` (`19.2`) | `-1.6%` | `0.127` | `109 ms` |
| **`summeval-relevance`** *(5-Level Relevance)* | `score` | 240 | **`24.2%`** | **`5.2`** | `13.8%` (`-7.8`) | **`+10.4%` (`+13.0` Skill)** | `0.175` | `124 ms` |
| **OVERALL (14 Tasks)** | **All** | **5880** | **`64.1%` (`64.8%` Public)** | **`46.0`** | `62.8%` (`44.1`) | **`+1.3%` (`+1.9` Skill)** | **`0.090` (`0.031` Pooled)** | **`86 ms`** |
### `typed-decisions` Enterprise Workflow Breakdown (2,000 Questions / 400 JSON States)
- **`security_incidents`**: **`64.0%`**
- **`agent_trace_observability`**: **`53.0%`** *(+6.4% vs baseline)*
- **`customer_service`**: **`52.8%`** *(+4.8% vs baseline)*
- **`invoice_processing`**: **`45.8%`** *(+2.4% vs baseline)*

---

## 3. Repository Contents

| File | Size | Description |
| :--- | :---: | :--- |
| `model.gguf` | `2.31 GiB` | 3B GGUF (`Q4_K_M` backbone + untied `F16` calibrated `output.weight` decision head) |
| `decision.json` | `2.5 KB` | Ollaya `llm-logits-v1` decision specification (`labels`, `template`, `temperature_by_options`, `llama` runtime config) |
| `calibration.json` | `751 B` | Post-hoc temperature scaling parameters and out-of-fold / post-quantization accuracy metrics |
| `ollaya_config.json` | `302 B` | Ollaya runtime manifest configuration |
| `Modelfile` | `559 B` | Deterministic single-token Ollama `Modelfile` (`temperature 0`, `top_k 1`, `num_predict 1`) |

---

## 4. Quickstart (Ollaya & Ollama)

### Option A: Register in both Ollaya and Ollama via `trainaya`

```bash
git clone https://huggingface.co/solarpush/push-3b
cd push-3b
trainaya register --model . --name push:3b --runtime both
```

### Option B: Upload to Hugging Face Hub from Trainaya Repo

```bash
make login
make push:3b
```

	---
	license: apache-2.0
	library_name: gguf
	pipeline_tag: text-classification
	base_model: mistralai/Ministral-3-3B-Instruct-2512
	base-model: mistralai/Ministral-3-3B-Instruct-2512
	language:
	- en
	- de
	- fr
	- es
	- multilingual
	tags:
	- ollaya
	- ollama
	- trainaya
	- decision-model
	- system-1
	- llm-logits-v1
	- gguf
	- calibrated
	- ministral
	datasets:
	- ZefanCai/Open-Jev-v1.1
	- SargeDev/jev-distill-corpus-v3
	- tasksource/tasksource-jev-typed-decisions
	---

	# push:3b — Ultra-Fast Calibrated System-1 Decision Model (3B GGUF)

	`push:3b` (`solarpush/push-3b`) is an ultra-low-latency (`86 ms` p50, `2.31 GiB` on disk, `~4.2 GB` VRAM), calibrated System-1 Decision Model (`llm-logits-v1`) designed for single-forward-pass structured decision-making across three universal primitives:

	- `choice` — Multi-way categorical selection (`A`..`Z`, 2 to 26 options),
	- `noul` — Binary epistemic / policy / safety / entailment verification (`Yes` vs `No`),
	- `score` — Ordinal rubric & severity evaluation (`0`..`9`, 3- to 5-level calibrated scales).

	Built on a dense 3B parameter GGUF backbone (`Q4_K_M`, `2.31 GiB`) with an untied `F16` (`ttype=1`) subspace-orthogonal decision head (`[131072, 3072]`) and cardinality-conditioned temperature scaling (`temperature_by_options`), `push:3b` achieves `3.1%` Pooled Public ECE, `85.7%` accuracy on `civil_comments`, `84.0%` on `summeval-consistency`, `74.6%` on `squad2`, and `73.2%` on `pubmedqa`.

	---

	## 1. Official Ollaya Decision Benchmark (`bench.ollaya.dev` — 14 Tasks, 5,880 Questions)

	Evaluated strictly zero-shot / out-of-domain across all 14 official `bench.ollaya.dev` tasks (`3,880` Public benchmark questions across 13 datasets + `2,000` `typed-decisions` workflow questions across 400 multi-question JSON states = `5,880` total questions, zero benchmark train/dev/test overlap):

	\| Model \| Overall Skill `[95% CI]` \| Public Skill (13 Tasks) \| Public Macro Acc \| Typed-Decisions Acc (Skill) \| Public Pooled ECE (lower is better) \| Latency (p50) \| VRAM \| License \|
	\| :--- \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \|
	\| `push:3b` (Adapted 3B `v12+`) \| `46.0` `[43.9, 48.2]` \| `47.1` \| `64.8%` \| `53.9%` (`32.5`) \| `3.1%` (`0.031`) \| `86 ms` \| `4.2 GB` \| `Apache-2.0` \|
	\| `push_ministral:3b` (Baseline Adapted 3B) \| `44.1` `[42.0, 46.2]` \| `45.3` \| `63.7%` \| `50.6%` (`27.6`) \| `6.5%` (`0.065`) \| `86 ms` \| `4.2 GB` \| `Apache-2.0` \|
	\| `push:26b` (26B-A4B MoE) \| `62.7` `[60.8, 64.7]` \| `63.1` \| `75.4%` \| `71.4%` (`58.1`) \| `2.8%` (`0.028`) \| `223 ms` \| `15.0 GB` \| `Apache-2.0` \|

	---

	## 2. Per-Task Breakdown (14 Tasks — 5,880 Questions)

	\| Benchmark Task \| Primitive \| Questions \| `push:3b` (`v12+`) Accuracy \| `push:3b` Skill (0–100) \| `push_ministral:3b` Baseline (Skill) \| Delta vs Baseline \| `push:3b` ECE (10-bin) \| Median Latency \|
	\| :--- \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \|
	\| `civil_comments` (Toxicity Detection) \| `noul` \| 300 \| `85.7%` \| `71.3` \| `83.7%` (`67.3`) \| `+2.0%` (`+4.0` Skill) \| `0.043` \| `51 ms` \|
	\| `summeval-consistency` (Factual Consistency) \| `score` \| 144 \| `84.0%` \| `80.0` \| `83.3%` (`79.2`) \| `+0.7%` (`+0.8` Skill) \| `0.151` \| `102 ms` \|
	\| `massive-en-US` (18-Way Intent) \| `choice` \| 350 \| `76.0%` \| `74.6` \| `75.7%` (`74.3`) \| `+0.3%` (`+0.3` Skill) \| `0.064` \| `65 ms` \|
	\| `squad2` (Unanswerable QA) \| `noul` \| 299 \| `74.6%` \| `49.2` \| `75.6%` (`51.2`) \| `-1.0%` \| `0.056` \| `67 ms` \|
	\| `boolq` (Reading Verification) \| `noul` \| 300 \| `74.3%` \| `48.7` \| `75.7%` (`51.3`) \| `-1.4%` \| `0.039` \| `59 ms` \|
	\| `pubmedqa` (Biomedical 3-Way QA) \| `choice` \| 250 \| `73.2%` \| `59.8` \| `74.4%` (`61.6`) \| `-1.2%` \| `0.069` \| `90 ms` \|
	\| `massive-de-DE` (18-Way Intent DE) \| `choice` \| 350 \| `72.3%` \| `70.7` \| `72.3%` (`70.7`) \| `0.0%` \| `0.037` \| `67 ms` \|
	\| `aegis2` (AI Safety Guardrail) \| `noul` \| 250 \| `64.8%` \| `29.6` \| `68.4%` (`36.8`) \| `-3.6%` \| `0.125` \| `54 ms` \|
	\| `multinli` (3-Way NLI) \| `choice` \| 299 \| `62.2%` \| `43.3` \| `55.5%` (`33.3`) \| `+6.7%` (`+10.0` Skill) \| `0.072` \| `46 ms` \|
	\| `paws` (Adversarial Paraphrase) \| `noul` \| 250 \| `60.0%` \| `20.0` \| `61.2%` (`22.4`) \| `-1.2%` \| `0.105` \| `50 ms` \|
	\| `vitaminc-dev` (3-Way Fact Verification) \| `choice` \| 599 \| `58.1%` \| `37.1` \| `52.9%` (`29.4`) \| `+5.2%` (`+7.7` Skill) \| `0.048` \| `55 ms` \|
	\| `typed-decisions` (400 JSON Workflows) \| `mixed` \| 2000 \| `53.9%` \| `32.5` \| `50.6%` (`27.6`) \| `+3.3%` (`+4.9` Skill) \| `0.153` \| `110 ms` \|
	\| `helpsteer2` (5-Level Helpfulness) \| `score` \| 249 \| `33.7%` \| `17.2` \| `35.3%` (`19.2`) \| `-1.6%` \| `0.127` \| `109 ms` \|
	\| `summeval-relevance` (5-Level Relevance) \| `score` \| 240 \| `24.2%` \| `5.2` \| `13.8%` (`-7.8`) \| `+10.4%` (`+13.0` Skill) \| `0.175` \| `124 ms` \|
	\| OVERALL (14 Tasks) \| All \| 5880 \| `64.1%` (`64.8%` Public) \| `46.0` \| `62.8%` (`44.1`) \| `+1.3%` (`+1.9` Skill) \| `0.090` (`0.031` Pooled) \| `86 ms` \|

	### `typed-decisions` Enterprise Workflow Breakdown (2,000 Questions / 400 JSON States)

	- `security_incidents`: `64.0%`
	- `agent_trace_observability`: `53.0%` (+6.4% vs baseline)
	- `customer_service`: `52.8%` (+4.8% vs baseline)
	- `invoice_processing`: `45.8%` (+2.4% vs baseline)

	---

	## 3. Repository Contents

	\| File \| Size \| Description \|
	\| :--- \| :---: \| :--- \|
	\| `model.gguf` \| `2.31 GiB` \| 3B GGUF (`Q4_K_M` backbone + untied `F16` calibrated `output.weight` decision head) \|
	\| `decision.json` \| `2.5 KB` \| Ollaya `llm-logits-v1` decision specification (`labels`, `template`, `temperature_by_options`, `llama` runtime config) \|
	\| `calibration.json` \| `751 B` \| Post-hoc temperature scaling parameters and out-of-fold / post-quantization accuracy metrics \|
	\| `ollaya_config.json` \| `302 B` \| Ollaya runtime manifest configuration \|
	\| `Modelfile` \| `559 B` \| Deterministic single-token Ollama `Modelfile` (`temperature 0`, `top_k 1`, `num_predict 1`) \|

	---

	## 4. Quickstart (Ollaya & Ollama)

	### Option A: Register in both Ollaya and Ollama via `trainaya`

	```bash
	git clone https://huggingface.co/solarpush/push-3b
	cd push-3b
	trainaya register --model . --name push:3b --runtime both
	```

	### Option B: Upload to Hugging Face Hub from Trainaya Repo

	```bash
	make login
	make push:3b
	```