Instructions to use JazerJu/VideoMiner 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 JazerJu/VideoMiner 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 JazerJu/VideoMiner:Q8_0 # Run inference directly in the terminal: llama cli -hf JazerJu/VideoMiner:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf JazerJu/VideoMiner:Q8_0 # Run inference directly in the terminal: llama cli -hf JazerJu/VideoMiner:Q8_0
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 JazerJu/VideoMiner:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf JazerJu/VideoMiner:Q8_0
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 JazerJu/VideoMiner:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf JazerJu/VideoMiner:Q8_0
Use Docker
docker model run hf.co/JazerJu/VideoMiner:Q8_0
- LM Studio
- Jan
- Ollama
How to use JazerJu/VideoMiner with Ollama:
ollama run hf.co/JazerJu/VideoMiner:Q8_0
- Unsloth Desktop
- Pi
How to use JazerJu/VideoMiner with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf JazerJu/VideoMiner:Q8_0
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": "JazerJu/VideoMiner:Q8_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use JazerJu/VideoMiner with Docker Model Runner:
docker model run hf.co/JazerJu/VideoMiner:Q8_0
- Lemonade
How to use JazerJu/VideoMiner with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull JazerJu/VideoMiner:Q8_0
Run and chat with the model
lemonade run user.VideoMiner-Q8_0
List all available models
lemonade list
- Hermes Agent
How to use JazerJu/VideoMiner with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf JazerJu/VideoMiner:Q8_0
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 JazerJu/VideoMiner:Q8_0
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use JazerJu/VideoMiner with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf JazerJu/VideoMiner:Q8_0
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 "JazerJu/VideoMiner:Q8_0" \ --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"
Add files using upload-large-folder tool
Browse files
wemm/WeMM-Embedding-4B-nf4/additional_chat_templates/sentence_transformers.jinja
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{#- Embedding template used by Sentence Transformers.
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Renders the same surface form as chat_template.jinja for text and images. Video
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is the exception: it renders as a bare <|video_pad|>, because the processor
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expands that token into one <seconds><|vision_start|>...<|vision_end|> block per
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frame and, from transformers 5.3, no longer consumes a surrounding pair. The
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wrapped form therefore gains a second, unwanted pair of vision boundaries there.
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This matches embedding_chat_template.jinja, which is bare for the same reason.
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The tokenizer normalizer drops the role newline before a leading image and the
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newline after the final <|im_end|>, then the post-processor appends <embedding>. #}
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{%- for message in messages %}
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{{- '<|im_start|>' + message.role + '\n' }}
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{%- if message.content is string %}
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{{- message.content }}
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{%- elif message.content is iterable and message.content is not mapping %}
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{%- for item in message.content %}
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{%- if 'image' in item or 'image_url' in item or item.type == 'image' %}
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{{- '<|vision_start|><|image_pad|><|vision_end|>' }}
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{%- elif 'video' in item or item.type == 'video' %}
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{{- '<|video_pad|>' }}
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{%- elif 'text' in item %}
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{{- item.text }}
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{%- else %}
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{{- raise_exception('Unexpected item type in content.') }}
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{%- endif %}
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{%- endfor %}
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{%- elif message.content is not none %}
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{{- raise_exception('Unexpected content type.') }}
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{%- endif %}
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{{- '<|im_end|>\n' }}
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{%- endfor %}
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