Instructions to use microtensor-archive/mt-code-3g-r1238-5EyYXepK 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-code-3g-r1238-5EyYXepK 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-code-3g-r1238-5EyYXepK # Run inference directly in the terminal: llama cli -hf microtensor-archive/mt-code-3g-r1238-5EyYXepK
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf microtensor-archive/mt-code-3g-r1238-5EyYXepK # Run inference directly in the terminal: llama cli -hf microtensor-archive/mt-code-3g-r1238-5EyYXepK
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-code-3g-r1238-5EyYXepK # Run inference directly in the terminal: ./llama-cli -hf microtensor-archive/mt-code-3g-r1238-5EyYXepK
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-code-3g-r1238-5EyYXepK # Run inference directly in the terminal: ./build/bin/llama-cli -hf microtensor-archive/mt-code-3g-r1238-5EyYXepK
Use Docker
docker model run hf.co/microtensor-archive/mt-code-3g-r1238-5EyYXepK
- LM Studio
- Jan
- Ollama
How to use microtensor-archive/mt-code-3g-r1238-5EyYXepK with Ollama:
ollama run hf.co/microtensor-archive/mt-code-3g-r1238-5EyYXepK
- Unsloth Desktop
- Docker Model Runner
How to use microtensor-archive/mt-code-3g-r1238-5EyYXepK with Docker Model Runner:
docker model run hf.co/microtensor-archive/mt-code-3g-r1238-5EyYXepK
- Lemonade
How to use microtensor-archive/mt-code-3g-r1238-5EyYXepK with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull microtensor-archive/mt-code-3g-r1238-5EyYXepK
Run and chat with the model
lemonade run user.mt-code-3g-r1238-5EyYXepK-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
Microtensor archive 路 code/mt-3g 路 round 1238
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:
5EyYXepK9oe8CaMv5KqV5p2kkgVTa57vtfXo8qtekoErFJtM - System digest:
6cd9c215d8187efb3a06120d36e83856 - Arena: code / mt-3g
- Round: 1238
- Standing this round: confirmed
Measured record
- Quality: 0.463
- Expected cost: 4000.0 ms per query
- Replication: 2
- Config hash:
sha256:221becaa24d6cb3d989dd7c790fa006e4a9de72d7c753894c7688fdc07b924c0 - Reports root:
sha256:25e76fe7c163deb54be513fa80060a03489956693f059c101613e33855c71107
The full signed record is in certificate.json. It is
recomputable from the round's published reports.
The artifact was fetched over a scheme that left no manifest file on disk; the bytes here are the component the network verified by digest and measured, retained without the submitted manifest.
- Downloads last month
- -
We're not able to determine the quantization variants.