Instructions to use HelloSun/Experiment_1 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 HelloSun/Experiment_1 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 HelloSun/Experiment_1:Q4_K_M # Run inference directly in the terminal: llama cli -hf HelloSun/Experiment_1:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf HelloSun/Experiment_1:Q4_K_M # Run inference directly in the terminal: llama cli -hf HelloSun/Experiment_1: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 HelloSun/Experiment_1:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf HelloSun/Experiment_1: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 HelloSun/Experiment_1:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf HelloSun/Experiment_1:Q4_K_M
Use Docker
docker model run hf.co/HelloSun/Experiment_1:Q4_K_M
- Llama new
- LM Studio
- Jan
- Ollama
How to use HelloSun/Experiment_1 with Ollama:
ollama run hf.co/HelloSun/Experiment_1:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use HelloSun/Experiment_1 with Docker Model Runner:
docker model run hf.co/HelloSun/Experiment_1:Q4_K_M
- Lemonade
How to use HelloSun/Experiment_1 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull HelloSun/Experiment_1:Q4_K_M
Run and chat with the model
lemonade run user.Experiment_1-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Upload report.md with huggingface_hub
Browse files
report.md
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# Experiment_1: openPangu-VL-7B-Q4_K_M
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Model:
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RAM: ~2 TB host, ~2 GB usable in this container.
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Backend: llama.cpp 1268edac5 + openpangu-vl-llama.cpp.patch (pangu_vl RoPE), built with GGML_NATIVE=ON, AMX on.
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Build: /root/app/pangu/llama-pinned/build-amx (libggml-cpu with AMX).
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##
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##
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|---|---:|---:|
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| 1 | 16.28 | 1.65 |
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| 2 | 31.82 | 3.23 |
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| 4 | 57.46 | 6.11 |
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| 6 | 95.77 | 8.44 |
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| 8 | 125.15 | 11.20 |
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| 10 | 157.58 | 13.00 |
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| 12 | 189.09 | 14.66 |
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| 14 | 210.57 | 16.58 |
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| 16 | 208.93 | 18.31 |
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| 20 | 156.98 | 9.95 |
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| 24 | 163.43 | 8.90 |
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KV cache type (16 threads, repack on): F16 pp 208.93 / tg 18.31; Q8_0 pp 173.87 / tg 19.10; Q4_0 pp 170.14 / tg 18.59. F16 fastest prefill; Q8_0 slightly faster decode.
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Long context decode (16 threads, no-repack, F16 KV): tg64 @ 2048 ctx = 16.63, @ 4096 = 14.88, @ 8192 = 13.44 t/s.
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ubatch sweep (pp512): 64=189.00, 128=206.44, 256=201.25, 512=208.93, 1024=183.61.
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## Files
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- results/amx_threads.jsonl: raw llama-bench samples
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- build: llama-pinned/build-amx (binaries)
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## CLI measured (real generation, prompt ~19 tokens + 128 gen, 16 threads, F16 KV, FA on)
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| Build | repack | Prompt t/s (median) | Gen t/s (median) |
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| AMX-enabled | on (AMX VNNI) | 141.9 | 18.7 |
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| AMX-enabled | off (AVX-512) | 147.4 | 19.4 |
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| stock (AMX disabled) | on (CPU_REPACK) | 129.3 | 18.7 |
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| stock (AMX disabled) | off | 134.3 | 19.3 |
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Best: AMX-enabled build with repack off → 147.4 t/s prefill, 19.4 t/s decode.
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## Default vs optimized (CLI, same prompt, 128 gen)
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| Config | Prompt t/s | Gen t/s |
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| default (no flags, 96 threads) | 73.4 | 5.6 |
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| 16 threads only | 147.7 | 18.3 |
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| optimized (16 thr, no-repack, FA on, F16 KV) | 147.4 | 19.4 |
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# Experiment_1: openPangu-VL-7B-Q4_K_M, text only, CPU
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Model file: openPangu-VL-7B-Q4_K_M.gguf (unchanged, no re-quantization)
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CPU: Intel Xeon Platinum 8559C, cgroup quota 16 CPUs, shared host (run-to-run variance about +-15%)
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Runtime: llama.cpp 1268edac5 + openpangu-vl-llama.cpp.patch, CLI, 128 generated tokens, pinned to CPUs 0-15
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## Default vs optimized (llama-cli, real generation)
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| Config | Prefill t/s | Decode t/s |
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| default (no flags, 96 threads) | 73.4 | 5.6 |
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| optimized (-t 16 -fa on, F16 KV, no-repack) | 147.4 | 19.4 |
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| speedup | ~2.0x | ~3.5x |
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## What actually caused the gain
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| Config | Prefill t/s | Decode t/s |
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| -t 16 only | 147.7 | 18.3 |
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| -t 16 + FA + no-repack | 147.4 | 19.4 |
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The gain comes from using 16 threads. FA, repack on/off and the AMX build differ within noise
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(same config repeated: prefill 119-148 t/s). AMX gave no reliable speedup.
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## Thread scaling (llama-bench, repack on, AMX build)
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| threads | pp512 | tg128 |
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|---:|---:|---:|
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| 1 | 16.28 | 1.65 |
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| 4 | 57.46 | 6.11 |
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| 8 | 125.15 | 11.20 |
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| 12 | 189.09 | 14.66 |
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| 16 | 208.93 | 18.31 |
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| 20 | 156.98 | 9.95 |
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| 24 | 163.43 | 8.90 |
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More than 16 threads degrades sharply (CPU quota throttling).
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Long context decode (tg64): 2048 ctx 16.63, 4096 ctx 14.88, 8192 ctx 13.44 t/s.
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Raw data: amx_threads.jsonl
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