Instructions to use HelloSun/Nemotron74ba9 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/Nemotron74ba9 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/Nemotron74ba9 # Run inference directly in the terminal: llama cli -hf HelloSun/Nemotron74ba9
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf HelloSun/Nemotron74ba9 # Run inference directly in the terminal: llama cli -hf HelloSun/Nemotron74ba9
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 HelloSun/Nemotron74ba9 # Run inference directly in the terminal: ./build/bin/llama cli -hf HelloSun/Nemotron74ba9
Use Docker
docker model run hf.co/HelloSun/Nemotron74ba9
- Llama new
- LM Studio
- Jan
- Ollama
How to use HelloSun/Nemotron74ba9 with Ollama:
ollama run hf.co/HelloSun/Nemotron74ba9
- Unsloth Desktop
- Pi
How to use HelloSun/Nemotron74ba9 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf HelloSun/Nemotron74ba9
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": "HelloSun/Nemotron74ba9" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use HelloSun/Nemotron74ba9 with Docker Model Runner:
docker model run hf.co/HelloSun/Nemotron74ba9
- Lemonade
How to use HelloSun/Nemotron74ba9 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull HelloSun/Nemotron74ba9
Run and chat with the model
lemonade run user.Nemotron74ba9-{{QUANT_TAG}}List all available models
lemonade list
- Hermes Agent
How to use HelloSun/Nemotron74ba9 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf HelloSun/Nemotron74ba9
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 HelloSun/Nemotron74ba9
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use HelloSun/Nemotron74ba9 with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf HelloSun/Nemotron74ba9
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 "HelloSun/Nemotron74ba9" \ --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"
Stage 1: runtime knob sweep (poll, cpu-strict, load mode) and -ub sweep: no gains
Browse files- README.md +7 -0
- benchmarks/knobs_decode.csv +20 -0
- benchmarks/ubatch_prefill.csv +10 -0
README.md
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@@ -82,6 +82,13 @@ Interleaved runs on a shared host (see `benchmarks/ab_results.csv`; 12-thread re
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much slower. `ngram-mod` costs little on novel text and helps when output repeats earlier text (editing files, echoing tool
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output). "Warm" means the same text had already been generated once in the session, so it is a best case.
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Suggested server flags on a quota-limited shared host: `-t 12 -fa on -lm mlock --spec-type ngram-mod`.
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## Accuracy
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much slower. `ngram-mod` costs little on novel text and helps when output repeats earlier text (editing files, echoing tool
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output). "Warm" means the same text had already been generated once in the session, so it is a best case.
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- **Runtime knobs: nothing to gain** (`benchmarks/knobs_decode.csv`, tg64, 12 threads, 3 interleaved rounds, medians):
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default `--poll 50` 10.37, `--poll 0` 10.16, `--poll 100` 10.39, `--cpu-strict 1` 9.96, `-lm none` 10.20 t/s. All differences are
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inside the run-to-run noise (single outliers of ~6 t/s appeared once for `--cpu-strict 1` and `-lm none`). `--prio 2` did not run
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in this container. Transparent hugepages are `madvise` system-wide and were not changed.
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- **`-ub` for prefill** (`benchmarks/ubatch_prefill.csv`, pp512, 12 threads, 2 rounds): 128 -> 33-35 t/s, 256 -> 37, 512 -> 40,
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1024 -> 39-41 t/s. The default 512 is already the best choice.
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Suggested server flags on a quota-limited shared host: `-t 12 -fa on -lm mlock --spec-type ngram-mod`.
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## Accuracy
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benchmarks/knobs_decode.csv
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round,config,tg64_tps
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1,base,10.370985
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1,poll0,10.163646
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1,poll100,10.389921
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1,strict,6.191479
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1,lmnone,6.667170
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1,prio2,
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2,poll0,9.785995
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2,poll100,10.493260
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2,strict,9.958393
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2,lmnone,10.200113
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2,prio2,
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2,base,9.529010
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3,poll100,9.595270
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3,strict,9.990220
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3,lmnone,10.460284
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3,prio2,
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3,base,10.366802
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3,poll0,10.329963
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KNOBS_DONE
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benchmarks/ubatch_prefill.csv
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round,ub,pp512_tps
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1,128,33.298578
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1,256,37.306879
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1,512,40.081132
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1,1024,39.106427
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2,128,34.787931
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2,256,37.228463
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2,512,39.742511
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2,1024,40.676948
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UB_DONE
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