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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@@ -36,3 +36,19 @@ 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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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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## Comparison with the HF model-card numbers (same machine type, llama-bench pp512 / tg)
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Interleaved A/B, 3 rounds, taskset 0-15, same host load (results/interleaved.jsonl):
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| Config | pp512 t/s | tg t/s |
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| default flags (96 threads, repack on) | 185 / 191 / 191 (median 190.5) | 5.7 / 5.7 / 5.6 (median 5.7) |
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| model-card config (-t 16 -fa on -ctk/-ctv f16 --repack 0) | 225.6 / 229.5 / 233.7 (median 229.5) | 16.8 / 17.1 / 17.3 (median 17.1) |
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| model card as published | 212 | 14.5 |
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- The model-card config IS the optimized config; I found nothing faster than it. Differences between
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my 230/17 and the published 212/14.5 are within host-load variance (+-15%), not an improvement.
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- Gain over default flags: prefill about 1.2x, decode about 3.0x (decode gain is all from -t 16).
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- The same command at a different time gave 108 / 4.1 (readme_exact.jsonl) while host load was 54:
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results on this shared host depend heavily on neighbours. Always compare interleaved.
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- CLI prefill numbers (73-148 t/s) use a ~19-token prompt and are not comparable to pp512.
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