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
Upload etat/vie-fcbniqxcbq2uq0.log with huggingface_hub
Browse files
etat/vie-fcbniqxcbq2uq0.log
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poids telecharges : 135.3 / 125.9 Go (107 %) (a l'arret : 0.00 Go/min)
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python3 6.8% 101.1Go | VLLM::Worker 121% 3.3Go | vllm 3.5% 2.0Go | VLLM::EngineCor
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(APIServer pid=14538) INFO 08-27 19:04:50 [loggers.py:310] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 93.6%
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(APIServer pid=14538) INFO: 127.0.0.1:55966 - "GET /metrics HTTP/1.1" 200 OK
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(APIServer pid=14538) INFO: 127.0.0.1:52206 - "POST /v1/chat/completions HTTP/1.1" 200 OK
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(APIServer pid=14538) INFO 08-27 19:05:30 [loggers.py:310] Engine 000: Avg prompt throughput: 1.3 tokens/s, Avg generation throughput: 0.8 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 93.6%
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(APIServer pid=14538) INFO: 127.0.0.1:52222 - "POST /v1/chat/completions HTTP/1.1" 200 OK
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(APIServer pid=14538) INFO 08-27 19:05:40 [loggers.py:310] Engine 000: Avg prompt throughput: 1.3 tokens/s, Avg generation throughput: 0.8 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 93.6%
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[VIE] 19:06:02 pod=fcbniqxcbq2uq0
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NVIDIA RTX PRO 6000 Blackwell Server Edition, 96612 MiB, 97887 MiB, 0 %
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RAM conteneur : 238.3 / 251.0 Go (cgroup)
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disque / : 101 Go libres sur 236
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poids telecharges : 135.3 / 125.9 Go (107 %) (a l'arret : 0.00 Go/min)
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python3 6.8% 101.1Go | VLLM::Worker 121% 3.3Go | vllm 3.5% 2.0Go | VLLM::EngineCor 68.9% 1.5Go | python3 0.6% 0.3Go |
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(APIServer pid=14538) INFO: 127.0.0.1:52206 - "POST /v1/chat/completions HTTP/1.1" 200 OK
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(APIServer pid=14538) INFO 08-27 19:05:30 [loggers.py:310] Engine 000: Avg prompt throughput: 1.3 tokens/s, Avg generation throughput: 0.8 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 93.6%
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(APIServer pid=14538) INFO: 127.0.0.1:52222 - "POST /v1/chat/completions HTTP/1.1" 200 OK
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(APIServer pid=14538) INFO 08-27 19:05:40 [loggers.py:310] Engine 000: Avg prompt throughput: 1.3 tokens/s, Avg generation throughput: 0.8 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 93.6%
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(APIServer pid=14538) INFO 08-27 19:05:50 [loggers.py:310] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 93.6%
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(APIServer pid=14538) INFO: 127.0.0.1:59916 - "GET /metrics HTTP/1.1" 200 OK
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