Instructions to use microtensor-archive/mt-code-3g-r1236-5ELAnLj3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use microtensor-archive/mt-code-3g-r1236-5ELAnLj3 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-r1236-5ELAnLj3
# Run inference directly in the terminal:
llama cli -hf microtensor-archive/mt-code-3g-r1236-5ELAnLj3

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-r1236-5ELAnLj3
# Run inference directly in the terminal:
llama cli -hf microtensor-archive/mt-code-3g-r1236-5ELAnLj3

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-r1236-5ELAnLj3
# Run inference directly in the terminal:
./llama-cli -hf microtensor-archive/mt-code-3g-r1236-5ELAnLj3

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-r1236-5ELAnLj3
# Run inference directly in the terminal:
./build/bin/llama-cli -hf microtensor-archive/mt-code-3g-r1236-5ELAnLj3

Use Docker

docker model run hf.co/microtensor-archive/mt-code-3g-r1236-5ELAnLj3

LM Studio
Jan
Ollama
How to use microtensor-archive/mt-code-3g-r1236-5ELAnLj3 with Ollama:
```
ollama run hf.co/microtensor-archive/mt-code-3g-r1236-5ELAnLj3
```

Unsloth Studio

How to use microtensor-archive/mt-code-3g-r1236-5ELAnLj3 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 microtensor-archive/mt-code-3g-r1236-5ELAnLj3 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 microtensor-archive/mt-code-3g-r1236-5ELAnLj3 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for microtensor-archive/mt-code-3g-r1236-5ELAnLj3 to start chatting

Docker Model Runner
How to use microtensor-archive/mt-code-3g-r1236-5ELAnLj3 with Docker Model Runner:
```
docker model run hf.co/microtensor-archive/mt-code-3g-r1236-5ELAnLj3
```

Lemonade

How to use microtensor-archive/mt-code-3g-r1236-5ELAnLj3 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull microtensor-archive/mt-code-3g-r1236-5ELAnLj3

Run and chat with the model

lemonade run user.mt-code-3g-r1236-5ELAnLj3-{{QUANT_TAG}}

List all available models

lemonade list

Atomic Chat

Microtensor archive · code/mt-3g · round 1236

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: 5ELAnLj3CmRCb1MNjVS43CAXXd15Q9LHCKMRj3rkoY52Y3N7
System digest: 759dbec6e0b610baef39b45a1d980a8b
Arena: code / mt-3g
Round: 1236
Standing this round: confirmed

Measured record

Quality: 0.828
Expected cost: 5356.0 ms per query
Replication: 1
Config hash: sha256:529c3abd98a5d09d5b6ca50560eb654fa86a97ed10846b92f12f67aefa4dc7f5
Reports root: sha256:c867fe89b3af4ba7524e5173bc7cd6c54e6aa7c8145dbc7f57b5dfb1e3d31b2b

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.

Downloads last month: 10

GGUF

Model size

0.8B params

Architecture

qwen3

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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