Instructions to use appvoid/void.0 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 appvoid/void.0 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 appvoid/void.0 # Run inference directly in the terminal: llama cli -hf appvoid/void.0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf appvoid/void.0 # Run inference directly in the terminal: llama cli -hf appvoid/void.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 appvoid/void.0 # Run inference directly in the terminal: ./llama-cli -hf appvoid/void.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 appvoid/void.0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf appvoid/void.0
Use Docker
docker model run hf.co/appvoid/void.0
- LM Studio
- Jan
- Ollama
How to use appvoid/void.0 with Ollama:
ollama run hf.co/appvoid/void.0
- Unsloth Desktop
- Docker Model Runner
How to use appvoid/void.0 with Docker Model Runner:
docker model run hf.co/appvoid/void.0
- Lemonade
How to use appvoid/void.0 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull appvoid/void.0
Run and chat with the model
lemonade run user.void.0-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
Delete continued_pretraining_manifest.json
Browse files
continued_pretraining_manifest.json
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{
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"base_model": "appvoid/void.0",
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"sources": {
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"cache_version": 3,
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"rewrite6_source": "appvoid/rewrite6",
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"rewrite6_split": "train",
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"fineweb_dataset": "HuggingFaceFW/fineweb-edu",
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"fineweb_config": "sample-10BT",
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"fineweb_split": "train",
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"no_prompt_dataset": "appvoid/no-prompt-15k",
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"no_prompt_split": "train",
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"fields": {
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"rewrite6": [
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"instruction",
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"text",
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"output"
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],
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"fineweb_edu": [
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"text"
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],
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"no_prompt_15k": [
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},
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"counts": {
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"rewrite6": 205404,
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"fineweb_edu": 205404,
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"no_prompt_15k": 15000
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},
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"seed": 3407
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},
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"packing": {
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"signature": "2a7abc0abb945816678da7dce3e6df927fc40e60c55d56ea18972abfd374e20a",
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"block_size": 4096,
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"blocks": 88660,
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"real_tokens": 363149667,
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"examples_seen": {
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"rewrite6": 205404,
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"fineweb_edu": 205404,
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"no_prompt_15k": 15000
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},
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"source_token_counts": {
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"rewrite6": 116587156,
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"fineweb_edu": 241348518,
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"no_prompt_15k": 5213993
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},
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"source_counts": {
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"rewrite6": 205404,
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"fineweb_edu": 205404,
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"no_prompt_15k": 15000
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},
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"token_dtype": "uint32",
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"length_dtype": "uint16",
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"two_epoch_tokens": 726299334.0
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},
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"epochs": 2.0,
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"max_context_length": 4096,
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"learning_rate": 5e-05,
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"micro_batch_size": 32,
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"gradient_accumulation_steps": 1,
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"precision": "bf16",
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"attention_implementation": "sdpa",
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"rewrite_layout": "instruction\\n\\n\"text bytes\"\\n\\n\"output bytes\""
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}
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