Instructions to use csoai/sov34-1p5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use csoai/sov34-1p5b 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 csoai/sov34-1p5b:F16 # Run inference directly in the terminal: llama cli -hf csoai/sov34-1p5b:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf csoai/sov34-1p5b:F16 # Run inference directly in the terminal: llama cli -hf csoai/sov34-1p5b: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 csoai/sov34-1p5b:F16 # Run inference directly in the terminal: ./llama-cli -hf csoai/sov34-1p5b: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 csoai/sov34-1p5b:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf csoai/sov34-1p5b:F16
Use Docker
docker model run hf.co/csoai/sov34-1p5b:F16
- LM Studio
- Jan
- vLLM
How to use csoai/sov34-1p5b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "csoai/sov34-1p5b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "csoai/sov34-1p5b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/csoai/sov34-1p5b:F16
- Ollama
How to use csoai/sov34-1p5b with Ollama:
ollama run hf.co/csoai/sov34-1p5b:F16
- Unsloth Studio
How to use csoai/sov34-1p5b with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for csoai/sov34-1p5b to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for csoai/sov34-1p5b to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for csoai/sov34-1p5b to start chatting
- Pi
How to use csoai/sov34-1p5b with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf csoai/sov34-1p5b:F16
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "csoai/sov34-1p5b:F16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- OpenClaw new
How to use csoai/sov34-1p5b with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf csoai/sov34-1p5b: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 "csoai/sov34-1p5b: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"
- Docker Model Runner
How to use csoai/sov34-1p5b with Docker Model Runner:
docker model run hf.co/csoai/sov34-1p5b:F16
- Lemonade
How to use csoai/sov34-1p5b with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull csoai/sov34-1p5b:F16
Run and chat with the model
lemonade run user.sov34-1p5b-F16
List all available models
lemonade list
- Hermes Agent
How to use csoai/sov34-1p5b with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf csoai/sov34-1p5b: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 csoai/sov34-1p5b:F16
Run Hermes
hermes
- Atomic Chat
sov34-1.5b — EXPERIMENTAL (dual-gate candidate: FAILED gate 2, published with failures included)
Register: measurement/attestation only. This model makes no capability claims. Read the numbers before use.
LoRA (r16) on Qwen2.5-1.5B, trained on 4,853 governance-corpus rows (eval_loss 0.821). This was a candidate successor to sov33-unified under a dual-gate rule: beat it on generality AND on the frozen governance split, or make no claims. Gate 2 failed. We publish the data anyway — failures included is the point of the measurement platform.
Measured gates (identical 170 held-out items, frozen split v1, set-F1)
| Model | set-F1 | CI95 (BCa n=2000) |
|---|---|---|
| sov33-unified | 0.2508 | [0.221, 0.283] |
| sov34 (this model) | 0.1975 | [0.169, 0.226] |
| base qwen2.5-1.5b | 0.1767 | [0.151, 0.205] |
Generality (lm-eval arc_easy, validated hf lane): sov34 0.7504 ± 0.0089; base 0.7551 ± 0.0088 (preserved within noise). sov33-unified for contrast: 0.2534 (near-random — disclosed specialist profile).
Verdict
Gate 1 (generality): PASS. Gate 2 (governance frozen split): FAIL vs 0.2508. Dual-gate NOT MET. The LoRA nudged governance retrieval above its own base but does not match the specialist. Consistent with the published canon: closed-book small models cannot hold statutory citation — retrieval grounding is required, not optional.
What this model is for
Experimentation, reproduction, router-substrate research (a 1.5B that keeps generality while carrying governance signal). NOT for compliance decisions, NOT a certified anything.
Copyright & training-data provenance (EU AI Act Art 53(1)(c) / (d))
The open-source GPAI exemption waives Art 53(1)(a)/(b) but not the copyright policy (c) or the training-content summary (d). This release states both, because our own OSSBench measures their presence and we hold ourselves to it.
Training content. A LoRA (r16) adapter over Qwen2.5-1.5B, tuned on 4,853 rows of governance/regulatory text — the EU AI Act (Regulation (EU) 2024/1689) and related official legal instruments. Official legal texts are not subject to restrictive copyright. The frozen harness and split are public: dataset
csoai/aiact-frozen-split-harness.Copyright policy. CSOAI respects rights-holders' text-and-data-mining reservations under the DSM Directive Art 4(3). No paywalled, scraped, or opt-out-reserved corpora were used for this adapter; the base model (Qwen2.5-1.5B) is used under its own licence.
Deliberately silent on two axes. This HF upload does not yet ship a components manifest or a cryptographic verification artefact; OSSBench therefore reads those two checks as ABSENT, which is the correct and honest reading. We do not name them here, because naming an artefact you do not ship is how a keyword scan is fooled into reporting it present — the exact failure this benchmark exists to catch.
Reproduce
Harness + results: HF dataset csoai/aiact-frozen-split-harness (incl. sov34_dualgate_triangulation_2026-08-03.json). Triangulation: sov33-unified / sov34 / base on byte-identical item sets.
Lane warning
All numbers measured via validated lanes only (lm_eval --model hf; direct ollama /api/generate; OpenRouter API). The llama.cpp llama-server + lm_eval local-completions lane was invalidated 2026-08-03 (instrument fault; see csoai/lmeval-official-format INVALIDATED.md).
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Base model
Qwen/Qwen2.5-1.5B
docker model run hf.co/csoai/sov34-1p5b:F16