Instructions to use microtensor-archive/mt-classify-3g-r1239-5CLfzJdv 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 microtensor-archive/mt-classify-3g-r1239-5CLfzJdv 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 microtensor-archive/mt-classify-3g-r1239-5CLfzJdv # Run inference directly in the terminal: llama cli -hf microtensor-archive/mt-classify-3g-r1239-5CLfzJdv
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf microtensor-archive/mt-classify-3g-r1239-5CLfzJdv # Run inference directly in the terminal: llama cli -hf microtensor-archive/mt-classify-3g-r1239-5CLfzJdv
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 microtensor-archive/mt-classify-3g-r1239-5CLfzJdv # Run inference directly in the terminal: ./llama-cli -hf microtensor-archive/mt-classify-3g-r1239-5CLfzJdv
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 microtensor-archive/mt-classify-3g-r1239-5CLfzJdv # Run inference directly in the terminal: ./build/bin/llama-cli -hf microtensor-archive/mt-classify-3g-r1239-5CLfzJdv
Use Docker
docker model run hf.co/microtensor-archive/mt-classify-3g-r1239-5CLfzJdv
- LM Studio
- Jan
- Ollama
How to use microtensor-archive/mt-classify-3g-r1239-5CLfzJdv with Ollama:
ollama run hf.co/microtensor-archive/mt-classify-3g-r1239-5CLfzJdv
- Unsloth Desktop
- Docker Model Runner
How to use microtensor-archive/mt-classify-3g-r1239-5CLfzJdv with Docker Model Runner:
docker model run hf.co/microtensor-archive/mt-classify-3g-r1239-5CLfzJdv
- Lemonade
How to use microtensor-archive/mt-classify-3g-r1239-5CLfzJdv with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull microtensor-archive/mt-classify-3g-r1239-5CLfzJdv
Run and chat with the model
lemonade run user.mt-classify-3g-r1239-5CLfzJdv-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
Microtensor archive 路 classify/mt-3g 路 round 1239
This repository is an archival copy of a system submitted to the Microtensor subnet (Bittensor netuid 92) and certified by its validators. The figures below were measured by the network on reference hardware. They are not self-reported.
- Miner hotkey:
5CLfzJdvimUczt7WEUi7TqREzKCzLgSnarf93cYofFKyNaP2 - System digest:
68a037b3a45ffb5514a3e226062a8517 - Arena: classify / mt-3g
- Round: 1239
- Standing this round: confirmed
Measured record
- Quality: 0.9722
- Expected cost: 686.0 ms per query
- Replication: 3
- Config hash:
sha256:95e6d70bc601ce8db6691e3d5baf3a6df985ada42fd1534503ceef210345d315 - Reports root:
sha256:6be6966f3e39e53f2c4bf6dd58226293b66c60184bbd558b866c41ecfbeb83ad
The full signed record is in certificate.json. It is
recomputable from the round's published reports.
The manifest in manifest.json is the submission exactly as the
miner shipped it; this repository's contents hash to the digest
committed on chain for this round.
Licence
Released under apache-2.0, inherited from the base model Qwen/Qwen2.5-0.5B-Instruct@7ae557604adf67be50417f59c2c2f167def9a775 this system was built on.
Submitting granted the network the right to retain, archive and
redistribute this artifact, with emissions as the consideration.
Anyone may serve it, including commercially, on the terms of that licence.
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We're not able to determine the quantization variants.