Instructions to use JazerJu/VideoMiner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use JazerJu/VideoMiner with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="JazerJu/VideoMiner", filename="fun-asr/Fun-ASR-Nano-Decoder.q5_k.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- 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 Studio
How to use JazerJu/VideoMiner with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for JazerJu/VideoMiner to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for JazerJu/VideoMiner to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for JazerJu/VideoMiner to start chatting
- 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 @mariozechner/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
- Hermes Agent new
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 new
- OpenClaw new
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"
- 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
| { | |
| "architectures": [ | |
| "GlmOcrForConditionalGeneration" | |
| ], | |
| "image_end_token_id": 59257, | |
| "image_start_token_id": 59256, | |
| "image_token_id": 59280, | |
| "model_type": "glm_ocr", | |
| "text_config": { | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "dtype": "bfloat16", | |
| "eos_token_id": [ | |
| 59246, | |
| 59253 | |
| ], | |
| "head_dim": 128, | |
| "hidden_act": "silu", | |
| "hidden_size": 1536, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 4608, | |
| "max_position_embeddings": 131072, | |
| "model_type": "glm_ocr_text", | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 16, | |
| "num_key_value_heads": 8, | |
| "num_nextn_predict_layers": 1, | |
| "pad_token_id": 59246, | |
| "rms_norm_eps": 1e-05, | |
| "rope_parameters": { | |
| "mrope_section": [ | |
| 16, | |
| 24, | |
| 24 | |
| ], | |
| "partial_rotary_factor": 1.0, | |
| "rope_theta": 10000, | |
| "rope_type": "default" | |
| }, | |
| "tie_word_embeddings": false, | |
| "use_cache": true, | |
| "vocab_size": 59392 | |
| }, | |
| "tie_word_embeddings": false, | |
| "transformers_version": "5.3.0.dev0", | |
| "video_end_token_id": 59259, | |
| "video_start_token_id": 59258, | |
| "video_token_id": 59281, | |
| "vision_config": { | |
| "attention_bias": true, | |
| "attention_dropout": 0.0, | |
| "depth": 24, | |
| "hidden_act": "silu", | |
| "hidden_dropout_prob": 0.0, | |
| "hidden_size": 1024, | |
| "image_size": 336, | |
| "in_channels": 3, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 4096, | |
| "model_type": "glm_ocr_vision", | |
| "num_heads": 16, | |
| "out_hidden_size": 1536, | |
| "patch_size": 14, | |
| "rms_norm_eps": 1e-05, | |
| "spatial_merge_size": 2, | |
| "temporal_patch_size": 2 | |
| }, | |
| "transformers.js_config": { | |
| "use_external_data_format": { | |
| "vision_encoder.onnx": 1, | |
| "decoder_model_merged.onnx": 2, | |
| "embed_tokens.onnx": 1, | |
| "vision_encoder_fp16.onnx": 1, | |
| "decoder_model_merged_fp16.onnx": 1, | |
| "embed_tokens_fp16.onnx": 1 | |
| }, | |
| "kv_cache_dtype": { | |
| "q4f16": "float16", | |
| "fp16": "float16" | |
| } | |
| } | |
| } |