Instructions to use guildlm/go-dev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use guildlm/go-dev with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("guildlm/go-dev") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - llama-cpp-python
How to use guildlm/go-dev with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="guildlm/go-dev", filename="go-dev.Q4_K_M.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use guildlm/go-dev 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 guildlm/go-dev:Q4_K_M # Run inference directly in the terminal: llama cli -hf guildlm/go-dev:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf guildlm/go-dev:Q4_K_M # Run inference directly in the terminal: llama cli -hf guildlm/go-dev:Q4_K_M
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 guildlm/go-dev:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf guildlm/go-dev:Q4_K_M
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 guildlm/go-dev:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf guildlm/go-dev:Q4_K_M
Use Docker
docker model run hf.co/guildlm/go-dev:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use guildlm/go-dev with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "guildlm/go-dev" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "guildlm/go-dev", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/guildlm/go-dev:Q4_K_M
- Ollama
How to use guildlm/go-dev with Ollama:
ollama run hf.co/guildlm/go-dev:Q4_K_M
- Unsloth Studio
How to use guildlm/go-dev 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 guildlm/go-dev 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 guildlm/go-dev to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for guildlm/go-dev to start chatting
- Pi
How to use guildlm/go-dev with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "guildlm/go-dev"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "guildlm/go-dev" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use guildlm/go-dev with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "guildlm/go-dev"
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 guildlm/go-dev
Run Hermes
hermes
- Atomic Chat new
- MLX LM
How to use guildlm/go-dev with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "guildlm/go-dev"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "guildlm/go-dev" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "guildlm/go-dev", "messages": [ {"role": "user", "content": "Hello"} ] }' - Docker Model Runner
How to use guildlm/go-dev with Docker Model Runner:
docker model run hf.co/guildlm/go-dev:Q4_K_M
- Lemonade
How to use guildlm/go-dev with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull guildlm/go-dev:Q4_K_M
Run and chat with the model
lemonade run user.go-dev-Q4_K_M
List all available models
lemonade list
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 guildlm/go-dev to start chattingUsing HuggingFace Spaces for Unsloth
# No setup required# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for guildlm/go-dev to start chattingGuildLM · go-dev
A small, sharp Go development specialist from the GuildLM Code Guild.
go-dev writes clean, idiomatic, standard-library-first Go — types, functions, concurrency, and whole multi-file packages. It is one of three specialists in the GuildLM Code Guild (go-dev · go-test · go-review) designed to be wrapped in a verification-driven agent loop rather than used as a lone chatbot.
The bet: capability = model × algorithm. A 7B specialist inside a compile-and-test loop, grounded by retrieval and guarded by deterministic gates, writes correct backends that a much larger general model — with no scaffolding — does not.
go-devis the model half. The Builder agent loop is the algorithm half.
Why this isn't "just Qwen with a name"
go-dev is a fused, standalone model (no separate adapter) with its own identity — ask it who it is and it answers GuildLM go-dev, not the base model. It is fine-tuned for one job (writing Go) and shipped as part of a guild that works together. Under the hood it is an honest Apache-2.0 derivative of Qwen2.5-Coder-7B-Instruct — we attribute the base proudly, and the value we add is specialization + the agent algorithm around it.
What it's for
- Generating idiomatic Go: structs, methods, generics, error handling, concurrency.
- Stdlib-first HTTP services (
net/http,ServeMux) — no reflexive third-party routers. - Working as the implementation role inside the GuildLM Builder: decompose a spec →
go-devwrites the code →go-testwrites the tests →go build/vet/test→ fix →go-reviewaudits.
Benchmarks
Measured locally with the real Go toolchain (no LLM-as-judge). See the research log for the full, honest story — including where fine-tuning helps and where the base and the algorithm are the real levers.
| Benchmark | Metric | go-dev | base 7B |
|---|---|---|---|
crucible go_dev_bench (24 tasks) |
pass@1 (real go build+go test) |
17/24 | 19/24 |
project-level score_backend (in the Builder loop) |
build + vet + test | 3/3 first try on tractable stdlib specs (numkit, jsonapi, worker-pool) | — |
Honest note (this is the whole point of GuildLM): on the solo unit benchmark
go-devlands within measurement noise of its base — for pure code-generation, per-role fine-tuning is not the lever; base choice and the agent loop are.go-dev's real edge shows up at the project level: driven by the Builder with retrieval grounding, it writes whole stdlib backends that build, vet and test green on the first try (score_backend3/3) — which a lone model, prompted once, does not. Use it in the loop; that's where it shines.
Quickstart
Apple Silicon (MLX)
pip install mlx-lm
python -m mlx_lm generate --model guildlm/go-dev \
--prompt "Write an idiomatic Go function MergeIntervals(intervals [][]int) [][]int." \
--max-tokens 400
Ollama (GGUF)
ollama run guildlm/go-dev "Write a stdlib-only Go net/http key/value service with GET/PUT."
Inside the agent loop (recommended)
# serve OpenAI-compatible, then let the Builder drive it
python -m mlx_lm server --model guildlm/go-dev --port 8080
guildlm-build --spec specs/myservice.yaml --out ./out \
--base-url http://localhost:8080/v1 \
--test-model guildlm/go-test --review-model guildlm/go-review \
--examples examples/verified_contracts.jsonl --candidates 3
Prompting
go-dev is trained with the system prompt:
You are GuildLM go-dev, a Go development specialist from the GuildLM Code Guild.
Ask for complete, runnable Go. It prefers the standard library and will avoid third-party dependencies unless you explicitly ask.
The Guild
| Specialist | Job |
|---|---|
| go-dev | writes the implementation |
| go-test | writes thorough table-driven tests |
| go-review | audits for bugs a green build hides |
- Agent loop: https://github.com/guildlm/builder
- Research log (every experiment, wins and losses): https://guildlm.github.io/research/
License & attribution
Apache-2.0, inherited from the base model Qwen2.5-Coder-7B-Instruct (© Alibaba Cloud). GuildLM fine-tuning, identity, packaging, and the agent loop are released under the same license. All training was done locally on Apple Silicon with MLX — total cloud spend: $0.
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Install Unsloth Studio (macOS, Linux, WSL)
# Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for guildlm/go-dev to start chatting