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

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metadata

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`

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

make login
make push:3b

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

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

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

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

3. Repository Contents

4. Quickstart (Ollaya & Ollama)

Option A: Register in both Ollaya and Ollama via trainaya

Option B: Upload to Hugging Face Hub from Trainaya Repo

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

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

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