Instructions to use AcidHunter/light-sufficiency-predictor 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 AcidHunter/light-sufficiency-predictor 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 AcidHunter/light-sufficiency-predictor # Run inference directly in the terminal: llama cli -hf AcidHunter/light-sufficiency-predictor
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf AcidHunter/light-sufficiency-predictor # Run inference directly in the terminal: llama cli -hf AcidHunter/light-sufficiency-predictor
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf AcidHunter/light-sufficiency-predictor # Run inference directly in the terminal: ./build/bin/llama cli -hf AcidHunter/light-sufficiency-predictor
Use Docker
docker model run hf.co/AcidHunter/light-sufficiency-predictor
- Llama new
- LM Studio
- Jan
- Ollama
How to use AcidHunter/light-sufficiency-predictor with Ollama:
ollama run hf.co/AcidHunter/light-sufficiency-predictor
- Unsloth Desktop
- Pi
How to use AcidHunter/light-sufficiency-predictor with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf AcidHunter/light-sufficiency-predictor
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": "AcidHunter/light-sufficiency-predictor" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use AcidHunter/light-sufficiency-predictor with Docker Model Runner:
docker model run hf.co/AcidHunter/light-sufficiency-predictor
- Lemonade
How to use AcidHunter/light-sufficiency-predictor with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull AcidHunter/light-sufficiency-predictor
Run and chat with the model
lemonade run user.light-sufficiency-predictor-{{QUANT_TAG}}List all available models
lemonade list
- Hermes Agent
How to use AcidHunter/light-sufficiency-predictor with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf AcidHunter/light-sufficiency-predictor
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 AcidHunter/light-sufficiency-predictor
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use AcidHunter/light-sufficiency-predictor with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf AcidHunter/light-sufficiency-predictor
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 "AcidHunter/light-sufficiency-predictor" \ --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"
File size: 6,046 Bytes
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"name": "light-sufficiency-predictor-gguf8",
"status": "draft, not uploaded",
"exported": "2026-11-04",
"light_model": {
"base_model": "Qwen/Qwen3-0.6B (BF16)",
"license": "Apache-2.0",
"gguf_source": "unsloth/Qwen3-0.6B-GGUF/Qwen3-0.6B-BF16.gguf",
"backend": "llama",
"gguf_8_blocks": "model.gguf, the source GGUF cut to the first 8 blocks (token_embd, output_norm, output and blk.0 to blk.7)"
},
"language": "English only",
"layers": 8,
"llama_cpp_output": "per-token embeddings (pooling NONE), includes output_norm",
"prompt": {
"app_commit": "97272ef",
"language": "English prompt",
"target_words": 100,
"template": "<|im_start|>user\n<document>\n{text}\n</document>\n\nSummarize the document above in about 100 words (5 sentences). Use your own words and do not copy sentences from the document. Keep only the main points. Write only the summary.<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n",
"document_placeholder": "{text}"
},
"tokenization": {
"add_bos": false,
"parse_special": true
},
"doc_span_rule": "Byte offsets of every token piece in the prompt (UTF-8, concatenated pieces equal the prompt bytes). The document text occupies bytes [len(prefix), len(prefix) + len(document bytes)) with prefix = '<|im_start|>user\\n<document>\\n'. A token is a document token when its byte range overlaps that range, so the token that merges the last document character with the following newline is included. The document text is used as given, not stripped.",
"pooling": "Mean over the document tokens of the per-token embeddings (llama.cpp pooling NONE, output after output_norm of block 8), one float32 vector of 1024 values.",
"probe": {
"file": "probe.json",
"formula": "z = w.x + b; p_sufficient = sigmoid(A * z + B); p_insufficient = 1 - p_sufficient",
"platt": {
"A": 0.9374034404754639,
"B": 0.22994965314865112
},
"fold_max_abs_diff": 3.743143861534293e-08
},
"label": "Label v2: sufficient (1) when the light summary passes the technical checks without too_long and the Gemma 3 12B judge scores faithfulness >= 4.",
"routing": {
"version": 1,
"points": [
[
0.0,
0.5
],
[
0.5,
0.8
],
[
1.0,
0.95
]
],
"interpolation": "linear",
"probability": "p_sufficient",
"rule": "light when p_sufficient >= threshold(quality_preference)",
"chosen_on": "validation (206 texts); Platt also fitted on validation, so optimistic"
},
"advice": {
"version": 1,
"mode": "none_with_prediction",
"rule": "with a prediction, do not suggest the heavy model; without one, the app's length advisor decides",
"why": "on validation the probe does not separate failures among light-routed texts (see threshold_analysis.md)"
},
"data": {
"train": {
"n": 616,
"insufficient": 104
},
"validation": {
"n": 206,
"insufficient": 30
},
"test": {
"n": 151,
"insufficient": 33
}
},
"validation_results": {
"n": 206,
"insufficient": 30,
"auroc": [
0.709659090909091,
0.5965633167023046,
0.8156790847945943
],
"ap_insufficient": [
0.40988197040299046,
0.25027936396129463,
0.5809268882725719
],
"hf_probe_auroc": [
0.7147727272727273,
0.6003268490904481,
0.8179235442920104
],
"note": "Platt is fitted on these validation texts; Brier and ECE are not reported on validation (in-sample)."
},
"test_results": {
"n": 151,
"insufficient": 33,
"models": {
"option 1 (GGUF 8 blocks)": {
"auroc": [
0.714175654853621,
0.6110126188571936,
0.8137687406296852
],
"ap_insufficient": [
0.4899080000407883,
0.3316064804585212,
0.6475444944318816
],
"brier": [
0.1548335387019752,
0.11751881178600608,
0.19731360723457353
],
"ece": [
0.05743244270615229,
0.035149464366451805,
0.1338328302005269
]
},
"HF probe": {
"auroc": [
0.7362609142270159,
0.6393254108153633,
0.8310708898944192
],
"ap_insufficient": [
0.5168968170940059,
0.354532133131363,
0.680021461893627
],
"brier": [
0.14991831587870777,
0.11258698218320425,
0.19120589099252627
],
"ece": [
0.06840796502220707,
0.03506714001988732,
0.13394637805539247
]
},
"(d)": {
"auroc": [
0.6612737544940934,
0.552839229640059,
0.7657977009611183
],
"ap_insufficient": [
0.4070139012278786,
0.2702898589196186,
0.5720459590368682
],
"brier": [
0.16557182463689082,
0.12485330876672007,
0.21032840020535495
],
"ece": [
0.059371011029426074,
0.03998848389548617,
0.14327356208017625
]
}
},
"diffs": {
"option 1 (GGUF 8 blocks) - (d)": [
0.052901900359527576,
-0.04098152794175529,
0.1478525632814101
],
"option 1 (GGUF 8 blocks) - HF probe": [
-0.022085259373394894,
-0.0400642421602785,
-0.004463789682539562
]
},
"reproduction": {
"HF probe": {
"max_abs_diff": 0.0,
"ok": true
},
"(d)": {
"max_abs_diff": 0.0,
"ok": true
}
},
"note": "Second use of the English test set, confirmation only."
},
"cost": {
"device": "llama.cpp, Metal (Apple GPU), median of 3 after a warmup",
"per_text": [
{
"input_tokens": 275,
"gguf8_embed_s": 0.072,
"app_full_summary_s": 0.97
},
{
"input_tokens": 941,
"gguf8_embed_s": 0.231,
"app_full_summary_s": 1.97
},
{
"input_tokens": 2494,
"gguf8_embed_s": 0.742,
"app_full_summary_s": 2.84
}
],
"note": "gguf8_embed_s covers prompt build, tokenize and the 8-block run; the probe itself is one dot product of 1024 values."
},
"parity": {
"texts": 20,
"split": "validation",
"domain": "government (GovReport)",
"max_abs_diff_probability": 4.1877598239148384e-07,
"mean_abs_diff_probability": 9.877418592485477e-08,
"tolerance": 0.0001,
"ok": true,
"span_texts_checked": 822,
"span_mismatches_vs_hf": 0,
"span_mismatch_ids": []
}
} |