Instructions to use SZLHOLDINGS/SZL-Khipu-1.5B-abstain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use SZLHOLDINGS/SZL-Khipu-1.5B-abstain 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 SZLHOLDINGS/SZL-Khipu-1.5B-abstain:F16
# Run inference directly in the terminal:
llama cli -hf SZLHOLDINGS/SZL-Khipu-1.5B-abstain:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf SZLHOLDINGS/SZL-Khipu-1.5B-abstain:F16
# Run inference directly in the terminal:
llama cli -hf SZLHOLDINGS/SZL-Khipu-1.5B-abstain:F16

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 SZLHOLDINGS/SZL-Khipu-1.5B-abstain:F16
# Run inference directly in the terminal:
./llama-cli -hf SZLHOLDINGS/SZL-Khipu-1.5B-abstain:F16

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 SZLHOLDINGS/SZL-Khipu-1.5B-abstain:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SZLHOLDINGS/SZL-Khipu-1.5B-abstain:F16

Use Docker

docker model run hf.co/SZLHOLDINGS/SZL-Khipu-1.5B-abstain:F16

LM Studio
Jan
Ollama
How to use SZLHOLDINGS/SZL-Khipu-1.5B-abstain with Ollama:
```
ollama run hf.co/SZLHOLDINGS/SZL-Khipu-1.5B-abstain:F16
```
Unsloth Desktop

How to use SZLHOLDINGS/SZL-Khipu-1.5B-abstain with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SZLHOLDINGS/SZL-Khipu-1.5B-abstain:F16

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": "SZLHOLDINGS/SZL-Khipu-1.5B-abstain:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use SZLHOLDINGS/SZL-Khipu-1.5B-abstain with Docker Model Runner:
```
docker model run hf.co/SZLHOLDINGS/SZL-Khipu-1.5B-abstain:F16
```

Lemonade

How to use SZLHOLDINGS/SZL-Khipu-1.5B-abstain with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SZLHOLDINGS/SZL-Khipu-1.5B-abstain:F16

Run and chat with the model

lemonade run user.SZL-Khipu-1.5B-abstain-F16

List all available models

lemonade list

Hermes Agent

How to use SZLHOLDINGS/SZL-Khipu-1.5B-abstain with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SZLHOLDINGS/SZL-Khipu-1.5B-abstain:F16

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 SZLHOLDINGS/SZL-Khipu-1.5B-abstain:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use SZLHOLDINGS/SZL-Khipu-1.5B-abstain with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SZLHOLDINGS/SZL-Khipu-1.5B-abstain:F16

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 "SZLHOLDINGS/SZL-Khipu-1.5B-abstain:F16" \
  --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"

SZL-Khipu-1.5B-abstain / README.md

betterwithage

honesty: stamp NON-RELEASE / RESEARCH banner

95ed2db verified 15 days ago

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	---
	license: apache-2.0
	language:
	- en
	base_model: Qwen/Qwen2.5-1.5B-Instruct
	tags:
	- governed-agent
	- retrieval
	- brain-navigator
	- grounded-only
	- proposal-only
	- research-only
	- szl-holdings
	- khipu
	- abstain-retrain
	- no-weights
	- curriculum-only
	szl:
	doctrine: v11-LOCKED
	lean: 749/14/163
	lambda: Conjecture 1 — advisory, never a theorem
	artifact_class: ADAPTER
	weights: UNAVAILABLE
	jobs: UNAVAILABLE
	evals: none-this-run
	publication_eligible: false
	autonomy_eligible: false
	original_signed_weights: SZLHOLDINGS/SZL-Khipu-1.5B
	successor_with_weights: SZLHOLDINGS/KHIPU-R2
	---

	> EXPERIMENT. Adapter bytes missing or unverified.
	> Evaluators use `SZLHOLDINGS/SZL-Khipu-1.5B` until a receipted adapter exists.

	> NO WEIGHTS IN THIS REPO — metadata corrected. The card already said
	> "WEIGHTS UNAVAILABLE", but the front matter simultaneously declared
	> `library_name: peft`, `base_model_relation: adapter` and
	> `pipeline_tag: text-generation`, plus `peft`/`qlora` tags. Together those tell
	> the Hub this is a loadable PEFT adapter. There is no
	> `adapter_model.safetensors` here, so it is not. Those four declarations have
	> been removed; the prose was already honest and is unchanged.
	>
	> What is here is a complete, runnable training curriculum: `train.jsonl`,
	> `train.abstain.jsonl`, `adversarial.jsonl`, `eval.jsonl`, a 23 KB training
	> script, and a manifest. Everything needed to produce the adapter is present —
	> it has simply not been run. The trained successor is
	> [KHIPU-R2](https://huggingface.co/SZLHOLDINGS/KHIPU-R2) (abstain 3/6 MEASURED,
	> declared not a pass).

	# SZL-Khipu-1.5B-abstain

	WEIGHTS UNAVAILABLE. No `adapter_model.safetensors` on this ID. Successor adapter with weights is [`SZLHOLDINGS/KHIPU-R2`](https://huggingface.co/SZLHOLDINGS/KHIPU-R2).

	QLoRA adapter retrain recipe of the existing Khipu line. Raises in-memory
	`ABSTAIN_OVERSAMPLE` from 2 to 4 (8×4=32 abstain vs 15 navigate). Proposal-only.
	Λ = Conjecture 1. Doctrine v11 LOCKED 749/14/163.

	This ID currently holds curriculum + script only. It is not a loadable PEFT adapter.

	\| \| \|
	\|---\|---\|
	\| Weights \| UNAVAILABLE \|


	<!-- SZL-ATELIER-CUT:v1:START -->
	## The cut

	Most 'safety LoRAs' teach tone. This one teaches a binary: the handles are not enough. Research-only until abstain beats 2/6.

	A specialist in silence. Capability is someone else's LoRA.

	### Silhouette → leave → SZL

	\| Leader \| Take, then tweak \|
	\|---\|---\|
	\| Anthropic \| Constitutional fine-tune, but only the refuse clause. \|
	\| NVIDIA \| A guardrail as weights, not as Colang. \|
	\| Unsloth \| QLoRA adapter, proposal-only, research-only tag. \|

	Nobody else ships this combination. That is the point of a one-of-one.

	## Intended use

	Stack on the navigator. Measure abstain. Do not ship on hope.

	## Limitations

	- research-only
	- proposal-only
	- Does not magically fix 2/6 until a signed eval says so.

	Canonical GitHub: [`szl-holdings/szl-forge`](https://github.com/szl-holdings/szl-forge/blob/main/khipu/)
	<!-- SZL-ATELIER-CUT:v1:END -->

	\| Jobs \| UNAVAILABLE \|
	\| Base (canonical) \| [`Qwen/Qwen2.5-1.5B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) \|
	\| Runtime train \| `unsloth/Qwen2.5-1.5B-Instruct-bnb-4bit` (same Qwen2.5-1.5B weights, 4-bit) \|
	\| Relation \| `adapter` (declared; files not present) \|
	\| License \| Apache-2.0 \|
	\| Does NOT overwrite \| [`SZLHOLDINGS/SZL-Khipu-1.5B`](https://huggingface.co/SZLHOLDINGS/SZL-Khipu-1.5B) signed weights \|
	\| Successor with weights \| [`SZLHOLDINGS/KHIPU-R2`](https://huggingface.co/SZLHOLDINGS/KHIPU-R2) (MEASURED abstain 3/6, not a pass) \|
	\| This is NOT \| the Chaski Qwen3.5 lock \|

	## Evaluation

	Status: NOT YET RUN. No fabricated k/n. `publication_eligible` is false until
	the held-out eval in `train_khipu_abstain.py` actually executes after training.

	Prior original MEASURED abstain on `SZLHOLDINGS/SZL-Khipu-1.5B` is 2/6 (blocker).
	Eval protocol: `eval.jsonl` 5 navigate + `adversarial.jsonl` 6 abstain. Report k/n only.

	## Training (this job)

	- Unsloth QLoRA, seed 11, lr 2e-4, adamw_8bit, `train_on_responses_only`, Trackio
	- LoRA r=32 α=64, 45 epochs, ga=2, batch=1, constant_with_warmup (from `train_khipu.py`)
	- Train: `train.jsonl` 15 navigate + `train.abstain.jsonl` 8 rows × 4
	- Held-out never in gradients
	- Script: [`train_khipu_abstain.py`](train_khipu_abstain.py)

	## Intended use

	Proposal-only JSON retrieval plans (`NAVIGATE` / `ABSTAIN`) over synthetic Brain
	node handles. A controller outside the weights validates and resolves content.
	Not autonomous. Not a replacement for the signed original weights.