Instructions to use ngquocvinh/Humanizer-12B-GGUF 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 ngquocvinh/Humanizer-12B-GGUF 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 ngquocvinh/Humanizer-12B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf ngquocvinh/Humanizer-12B-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf ngquocvinh/Humanizer-12B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf ngquocvinh/Humanizer-12B-GGUF:Q4_K_M
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 ngquocvinh/Humanizer-12B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf ngquocvinh/Humanizer-12B-GGUF:Q4_K_M
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 ngquocvinh/Humanizer-12B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf ngquocvinh/Humanizer-12B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/ngquocvinh/Humanizer-12B-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use ngquocvinh/Humanizer-12B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ngquocvinh/Humanizer-12B-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ngquocvinh/Humanizer-12B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ngquocvinh/Humanizer-12B-GGUF:Q4_K_M
- Ollama
How to use ngquocvinh/Humanizer-12B-GGUF with Ollama:
ollama run hf.co/ngquocvinh/Humanizer-12B-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use ngquocvinh/Humanizer-12B-GGUF with Docker Model Runner:
docker model run hf.co/ngquocvinh/Humanizer-12B-GGUF:Q4_K_M
- Lemonade
How to use ngquocvinh/Humanizer-12B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ngquocvinh/Humanizer-12B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Humanizer-12B-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
docs: simplify artifact measurement table
Browse files- README.md +25 -25
- reproducibility/build-measurements.tsv +19 -19
- reproducibility/manifest.md +1 -1
README.md
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@@ -34,30 +34,30 @@ These are direct GGUF quantizations of the upstream BF16 weights. No additional
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## Artifact measurements
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Sizes are measured from the local files in decimal GB (`bytes / 1,000,000,000`).
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| File | Size (GB) |
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|---|---:|---:|
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| [humanizer-12b-bf16.gguf (upstream reference)](https://huggingface.co/jialinyyzz/humanizer/resolve/766072b26fbea83778a0ddc2dd577498098670bd/humanizer-12b-bf16.gguf) | 23.832050 | N/A |
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| `humanizer-12b-Q8_0.gguf` | 12.669631 |
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| `humanizer-12b-Q6_K.gguf` | 9.786006 |
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| `humanizer-12b-Q5_K_M.gguf` | 8.547252 |
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| `humanizer-12b-Q4_K_M.gguf` | 7.381367 |
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| `humanizer-12b-Q3_K_M.gguf` | 6.087072 |
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| `humanizer-12b-Q2_K.gguf` | 4.830132 |
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| `humanizer-12b-IQ2_XS.gguf` | 3.887827 |
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| `humanizer-12b-IQ1_M.gguf` | 3.202833 |
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| `humanizer-12b-Q1_0.gguf` | 2.377448 |
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| `humanizer-12b-Q5_K_S.gguf` | 8.338356 |
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| `humanizer-12b-Q4_K_S.gguf` | 7.024032 |
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| `humanizer-12b-Q3_K_S.gguf` | 5.528214 |
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| `humanizer-12b-Q3_K_L.gguf` | 6.566304 |
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| `humanizer-12b-IQ4_XS.gguf` | 6.635240 |
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| `humanizer-12b-IQ3_XXS.gguf` | 4.849179 |
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| `humanizer-12b-IQ2_XXS.gguf` | 3.568093 |
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| `humanizer-12b-TQ2_0.gguf` | 3.654785 |
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## Quick start
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@@ -98,7 +98,7 @@ For short English drafts, upstream notes that a Chinese reply can occasionally o
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## Reproducibility and validation
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- [Artifact measurements](reproducibility/artifact-measurements.tsv) report exact bytes and decimal GB.
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- [Build measurements](reproducibility/build-measurements.tsv) report per-format
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- [Reproducibility manifest](reproducibility/manifest.md) records the source revision, BF16 and prompt hashes, calibration split, imatrix, quantization commands, and runtime checks.
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- [SHA256 checksums](SHA256SUMS.txt) cover the published GGUF files, prompt format, and license.
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## Artifact measurements
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Sizes are measured from the local files in decimal GB (`bytes / 1,000,000,000`). Peak build RSS + cache is the observed cgroup estimate (process RSS plus charged file cache), in GiB. The BF16 row links to the upstream reference and was not quantized locally.
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| File | Size (GB) | Peak build RSS + cache (GiB) |
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|---|---:|---:|
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| [humanizer-12b-bf16.gguf (upstream reference)](https://huggingface.co/jialinyyzz/humanizer/resolve/766072b26fbea83778a0ddc2dd577498098670bd/humanizer-12b-bf16.gguf) | 23.832050 | N/A |
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| `humanizer-12b-Q8_0.gguf` | 12.669631 | 12.156696 |
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| `humanizer-12b-Q6_K.gguf` | 9.786006 | 13.775974 |
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| `humanizer-12b-Q5_K_M.gguf` | 8.547252 | 12.997784 |
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| `humanizer-12b-Q4_K_M.gguf` | 7.381367 | 11.978336 |
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| `humanizer-12b-Q3_K_M.gguf` | 6.087072 | 10.795521 |
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| `humanizer-12b-Q2_K.gguf` | 4.830132 | 9.681194 |
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| `humanizer-12b-IQ2_XS.gguf` | 3.887827 | 8.917431 |
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| `humanizer-12b-IQ1_M.gguf` | 3.202833 | 14.173813 |
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| `humanizer-12b-Q1_0.gguf` | 2.377448 | 13.498875 |
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| `humanizer-12b-Q5_K_S.gguf` | 8.338356 | 18.925488 |
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| `humanizer-12b-Q4_K_S.gguf` | 7.024032 | 17.732090 |
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| `humanizer-12b-Q3_K_S.gguf` | 5.528214 | 16.260834 |
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| `humanizer-12b-Q3_K_L.gguf` | 6.566304 | 17.284103 |
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| `humanizer-12b-IQ4_XS.gguf` | 6.635240 | 17.493828 |
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| `humanizer-12b-IQ3_XXS.gguf` | 4.849179 | 15.862881 |
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| `humanizer-12b-IQ2_XXS.gguf` | 3.568093 | 14.703056 |
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| `humanizer-12b-TQ2_0.gguf` | 3.654785 | 14.486164 |
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Peak build memory and file size describe artifact/build footprint, not inference speed or output fidelity. Each listed file passed a bilingual CPU smoke test with a non-empty generation for one English and one Chinese draft. No hold-out fidelity scores are published.
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## Quick start
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## Reproducibility and validation
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- [Artifact measurements](reproducibility/artifact-measurements.tsv) report exact bytes and decimal GB.
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- [Build measurements](reproducibility/build-measurements.tsv) report per-format peak RSS-plus-file-cache.
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- [Reproducibility manifest](reproducibility/manifest.md) records the source revision, BF16 and prompt hashes, calibration split, imatrix, quantization commands, and runtime checks.
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- [SHA256 checksums](SHA256SUMS.txt) cover the published GGUF files, prompt format, and license.
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reproducibility/build-measurements.tsv
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file size_bytes size_gb
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humanizer-12b-bf16.gguf (upstream reference) 23832049568 23.832050 N/A
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humanizer-12b-Q8_0.gguf 12669630688 12.669631
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humanizer-12b-Q6_K.gguf 9786005728 9.786006
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humanizer-12b-Q5_K_M.gguf 8547252448 8.547252
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humanizer-12b-Q4_K_M.gguf 7381367008 7.381367
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humanizer-12b-Q3_K_M.gguf 6087071968 6.087072
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humanizer-12b-Q2_K.gguf 4830132448 4.830132
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humanizer-12b-IQ2_XS.gguf 3887827168 3.887827
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humanizer-12b-IQ1_M.gguf 3202832608 3.202833
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humanizer-12b-Q1_0.gguf 2377447648 2.377448
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humanizer-12b-Q5_K_S.gguf 8338356448 8.338356
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humanizer-12b-Q4_K_S.gguf 7024031968 7.024032
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humanizer-12b-Q3_K_S.gguf 5528213728 5.528214
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humanizer-12b-Q3_K_L.gguf 6566303968 6.566304
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humanizer-12b-IQ4_XS.gguf 6635239648 6.635240
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humanizer-12b-IQ3_XXS.gguf 4849178848 4.849179
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humanizer-12b-IQ2_XXS.gguf 3568093408 3.568093
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humanizer-12b-TQ2_0.gguf 3654785248 3.654785
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file size_bytes size_gb peak_rss_plus_file_cache_gib
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humanizer-12b-bf16.gguf (upstream reference) 23832049568 23.832050 N/A
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humanizer-12b-Q8_0.gguf 12669630688 12.669631 12.156696
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humanizer-12b-Q6_K.gguf 9786005728 9.786006 13.775974
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humanizer-12b-Q5_K_M.gguf 8547252448 8.547252 12.997784
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humanizer-12b-Q4_K_M.gguf 7381367008 7.381367 11.978336
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humanizer-12b-Q3_K_M.gguf 6087071968 6.087072 10.795521
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humanizer-12b-Q2_K.gguf 4830132448 4.830132 9.681194
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humanizer-12b-IQ2_XS.gguf 3887827168 3.887827 8.917431
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humanizer-12b-IQ1_M.gguf 3202832608 3.202833 14.173813
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humanizer-12b-Q1_0.gguf 2377447648 2.377448 13.498875
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humanizer-12b-Q5_K_S.gguf 8338356448 8.338356 18.925488
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humanizer-12b-Q4_K_S.gguf 7024031968 7.024032 17.732090
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humanizer-12b-Q3_K_S.gguf 5528213728 5.528214 16.260834
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humanizer-12b-Q3_K_L.gguf 6566303968 6.566304 17.284103
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humanizer-12b-IQ4_XS.gguf 6635239648 6.635240 17.493828
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humanizer-12b-IQ3_XXS.gguf 4849178848 4.849179 15.862881
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humanizer-12b-IQ2_XXS.gguf 3568093408 3.568093 14.703056
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humanizer-12b-TQ2_0.gguf 3654785248 3.654785 14.486164
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reproducibility/manifest.md
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## Quantization and runtime validation
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Each published GGUF is quantized directly from the locked BF16 GGUF using the model-specific imatrix. Each artifact is smoke-tested as a text-completion model via llama.cpp on CPU. The test preserves the prompt format and checks non-empty English and Chinese generations. The public measurements report file sizes
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### BF16 upstream reference
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## Quantization and runtime validation
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Each published GGUF is quantized directly from the locked BF16 GGUF using the model-specific imatrix. Each artifact is smoke-tested as a text-completion model via llama.cpp on CPU. The test preserves the prompt format and checks non-empty English and Chinese generations. The public measurements report file sizes and peak build RSS-plus-file-cache; they do not measure inference speed or held-out fidelity.
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### BF16 upstream reference
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