Instructions to use tchbcb/samai-9b 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 tchbcb/samai-9b 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 tchbcb/samai-9b:Q4_K_M # Run inference directly in the terminal: llama cli -hf tchbcb/samai-9b:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf tchbcb/samai-9b:Q4_K_M # Run inference directly in the terminal: llama cli -hf tchbcb/samai-9b: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 tchbcb/samai-9b:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf tchbcb/samai-9b: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 tchbcb/samai-9b:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf tchbcb/samai-9b:Q4_K_M
Use Docker
docker model run hf.co/tchbcb/samai-9b:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use tchbcb/samai-9b with Ollama:
ollama run hf.co/tchbcb/samai-9b:Q4_K_M
- Unsloth Desktop
- Pi
How to use tchbcb/samai-9b with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf tchbcb/samai-9b:Q4_K_M
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": "tchbcb/samai-9b:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use tchbcb/samai-9b with Docker Model Runner:
docker model run hf.co/tchbcb/samai-9b:Q4_K_M
- Lemonade
How to use tchbcb/samai-9b with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tchbcb/samai-9b:Q4_K_M
Run and chat with the model
lemonade run user.samai-9b-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use tchbcb/samai-9b with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf tchbcb/samai-9b:Q4_K_M
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 tchbcb/samai-9b:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use tchbcb/samai-9b with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf tchbcb/samai-9b:Q4_K_M
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 "tchbcb/samai-9b:Q4_K_M" \ --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"
Commit History
30 days ago
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transfer.md R14: GSQ-RCO champion + tunnel-native OpenAI API + queue
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train.md v4.6: pitfalls #67-#70 + R13/R14 records + serving recipe
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oneclick_gsqrco_serve: one-command GSQ-RCO serving (tunnel-aware)
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m13_r10_lora.gguf.sha256: R13 rebake digest
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m13_r10_lora.gguf: R13 rebake (newest wins, git-history rule)
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Upload m13_r13_alloc.json with huggingface_hub
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Upload m13_r13_report.json with huggingface_hub
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m13_r10 GSQ-RCO-lite: imatrix+learned-assignment q4_0/q5_0 body, budget-alloc
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r13 container imatrix mix (safety push)
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Upload m13_r10_q4_sha256.txt with huggingface_hub
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m13_r10_mixbit_4x8: R12 champion merged via llama-export-lora (r6..r10)
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train.md v4.5: pitfalls #57-#66 + zagent upstream-zero + envelope v=1 protocol + R10-R12 long-window rounds (R12 closure)
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deploy2 v5: lora-scale arg removed (new llama.cpp)
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Upload base_mixbit_4x8.gguf.sha256 with huggingface_hub
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base_mixbit_4x8.gguf: 4x8 base for lora mount (colab T4)
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Upload base_q4_k_m.gguf.sha256 with huggingface_hub
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base_q4_k_m.gguf: pure 4bit base for lora mount (colab T4)
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Upload m13_r10_lora.gguf.sha256 with huggingface_hub
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m13_r10_lora.gguf: R12 adapter as gguf-lora (run-time mount)
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deploy2 v4b: lora fmt + file-only check
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surgery3: recurrent_layers BOOL 33->32 (R10 logic, Colab)
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colab T4 (sm_75) CUDA build, first-push
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deploy2 v3: LD += /usr/lib64-nvidia (libcuda.so.1)
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compile v2: tar threshold 30MB (sm_75 single-arch)
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deploy2 v2: convert check by file-only (tqdm stderr flood pit#69)
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deploy2: lora-mount path (cgroup OOM evasion, RAM<3G)
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deploy v6: AutoConfig import fix
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deploy v5: meta-skel infer device_map (no disk offload)
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deploy v4: widen max_memory 14G+8G (no disk offload)
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deploy v3: offload_folder (correct kwarg)
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deploy v2: offload_dir fix (12G RAM offload)
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surgery v2 (public mirror for colab)
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colab T4 ammo fetch (adapter/mmproj/base 18G -> auto fire deploy)
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colab T4 m13_r10 deploy chain (merge auto-devmap/convert/surgery/dual-quant/push/serve)
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colab T4 llama.cpp CUDA sm_75 build chain
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transfer.md: R12 handover (public mirror)
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zagent_r12: empty tool_call rejection + orphan tool-call recovery + double-encoded code unwrap (R12 T5)
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Upload m13_r9_q4_sha256.txt with huggingface_hub
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m13_r9_mixbit_4x8: R11 long-context discipline champion (r6->r7->r8->r9)
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R11: web-path anti-loop guard + sig normalization
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R11: zagent binary with anti-loop guard + analyze_video local frames
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R10 probe artifacts: mixbit_build.log
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R10 probe artifacts: MIXBIT_BUILD_DONE
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R10 probe artifacts: probe_long_mixbithighlong.txt
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R10 probe artifacts: probe_serving_mixbithigh.txt
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R10 probe artifacts: probe_serving_q4_baseline.txt
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R10 probe artifacts: probe_long_mixbit4x8long.txt
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R10 probe artifacts: probe_serving_mixbit4x8.txt
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