Instructions to use patdev/k3-a40-bootstrap 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 patdev/k3-a40-bootstrap 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 patdev/k3-a40-bootstrap:BF16 # Run inference directly in the terminal: llama cli -hf patdev/k3-a40-bootstrap:BF16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf patdev/k3-a40-bootstrap:BF16 # Run inference directly in the terminal: llama cli -hf patdev/k3-a40-bootstrap:BF16
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 patdev/k3-a40-bootstrap:BF16 # Run inference directly in the terminal: ./llama-cli -hf patdev/k3-a40-bootstrap:BF16
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 patdev/k3-a40-bootstrap:BF16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf patdev/k3-a40-bootstrap:BF16
Use Docker
docker model run hf.co/patdev/k3-a40-bootstrap:BF16
- LM Studio
- Jan
- Ollama
How to use patdev/k3-a40-bootstrap with Ollama:
ollama run hf.co/patdev/k3-a40-bootstrap:BF16
- Unsloth Desktop
- Docker Model Runner
How to use patdev/k3-a40-bootstrap with Docker Model Runner:
docker model run hf.co/patdev/k3-a40-bootstrap:BF16
- Lemonade
How to use patdev/k3-a40-bootstrap with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull patdev/k3-a40-bootstrap:BF16
Run and chat with the model
lemonade run user.k3-a40-bootstrap-BF16
List all available models
lemonade list
- Atomic Chat
journal de demarrage
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etat/xtn8ts2meqsqb4.log
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=== bootstrap v86-journal-runtime-continu | 00:
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[VL] 23:36:01 bootstrap v86-journal-runtime-continu
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[VL] 23:36:01 pilote 595.91.07, CUDA runtime 13.2
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(APIServer pid=5703) INFO 09-06 00:08:23 [loggers.py:310] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 9.5 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 19.0%
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(APIServer pid=5703) INFO 09-06 00:08:23 [metrics.py:120] SpecDecoding metrics: Mean acceptance length: 1.00, Accepted throughput: 0.00 tokens/s, Drafted throughput: 28.50 tokens/s, Accepted: 0 tokens, Drafted: 285 tokens, Per-position acceptance rate: 0.000, 0.000, 0.000, Avg Draft acceptance rate: 0.0%
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(APIServer pid=5703) INFO 09-06 00:08:33 [loggers.py:310] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 10.3%, Prefix cache hit rate: 20.3%
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INFO: 100.64.1.3:38406 - "POST /v1/messages?beta=true HTTP/1.1" 200 OK
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=== bootstrap v86-journal-runtime-continu | 00:12:08 UTC ===
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[VL] 23:36:01 bootstrap v86-journal-runtime-continu
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[VL] 23:36:01 pilote 595.91.07, CUDA runtime 13.2
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[VL] 23:36:01 pilote 595.91.07 : compat CUDA non necessaire
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(APIServer pid=5703) INFO 09-06 00:08:23 [loggers.py:310] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 9.5 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 19.0%
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(APIServer pid=5703) INFO 09-06 00:08:23 [metrics.py:120] SpecDecoding metrics: Mean acceptance length: 1.00, Accepted throughput: 0.00 tokens/s, Drafted throughput: 28.50 tokens/s, Accepted: 0 tokens, Drafted: 285 tokens, Per-position acceptance rate: 0.000, 0.000, 0.000, Avg Draft acceptance rate: 0.0%
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(APIServer pid=5703) INFO 09-06 00:08:33 [loggers.py:310] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 10.3%, Prefix cache hit rate: 20.3%
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(APIServer pid=5703) INFO 09-06 00:10:23 [loggers.py:310] Engine 000: Avg prompt throughput: 24239.5 tokens/s, Avg generation throughput: 31.4 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 24.1%, Prefix cache hit rate: 20.3%
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(APIServer pid=5703) INFO 09-06 00:10:23 [metrics.py:120] SpecDecoding metrics: Mean acceptance length: 1.00, Accepted throughput: 0.00 tokens/s, Drafted throughput: 7.82 tokens/s, Accepted: 0 tokens, Drafted: 939 tokens, Per-position acceptance rate: 0.000, 0.000, 0.000, Avg Draft acceptance rate: 0.0%
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(APIServer pid=5703) INFO 09-06 00:10:33 [loggers.py:310] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 13.5 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 20.3%
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(APIServer pid=5703) INFO 09-06 00:10:33 [metrics.py:120] SpecDecoding metrics: Mean acceptance length: 1.00, Accepted throughput: 0.00 tokens/s, Drafted throughput: 40.50 tokens/s, Accepted: 0 tokens, Drafted: 405 tokens, Per-position acceptance rate: 0.000, 0.000, 0.000, Avg Draft acceptance rate: 0.0%
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(APIServer pid=5703) INFO 09-06 00:10:43 [loggers.py:310] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 10.3%, Prefix cache hit rate: 21.3%
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INFO: 100.64.1.2:43518 - "POST /v1/messages?beta=true HTTP/1.1" 200 OK
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INFO: 100.64.1.1:54690 - "POST /v1/messages?beta=true HTTP/1.1" 200 OK
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INFO: 100.64.1.3:38406 - "POST /v1/messages?beta=true HTTP/1.1" 200 OK
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