Instructions to use guildlm/go-review with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use guildlm/go-review 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-review") 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-review with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="guildlm/go-review", filename="go-review.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-review 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-review:Q4_K_M # Run inference directly in the terminal: llama cli -hf guildlm/go-review: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-review:Q4_K_M # Run inference directly in the terminal: llama cli -hf guildlm/go-review: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-review:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf guildlm/go-review: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-review:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf guildlm/go-review:Q4_K_M
Use Docker
docker model run hf.co/guildlm/go-review:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use guildlm/go-review with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "guildlm/go-review" # 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-review", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/guildlm/go-review:Q4_K_M
- Ollama
How to use guildlm/go-review with Ollama:
ollama run hf.co/guildlm/go-review:Q4_K_M
- Unsloth Studio
How to use guildlm/go-review 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-review 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-review 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-review to start chatting
- Pi
How to use guildlm/go-review 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-review"
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-review" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use guildlm/go-review 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-review"
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-review
Run Hermes
hermes
- Atomic Chat new
- MLX LM
How to use guildlm/go-review 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-review"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "guildlm/go-review" # 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-review", "messages": [ {"role": "user", "content": "Hello"} ] }' - Docker Model Runner
How to use guildlm/go-review with Docker Model Runner:
docker model run hf.co/guildlm/go-review:Q4_K_M
- Lemonade
How to use guildlm/go-review with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull guildlm/go-review:Q4_K_M
Run and chat with the model
lemonade run user.go-review-Q4_K_M
List all available models
lemonade list
GuildLM · go-review
A small, sharp Go code-review specialist from the GuildLM Code Guild.
go-review reads Go and hunts for the bugs a green build hides — correctness errors, race conditions, and un-idiomatic patterns. It is one of three specialists in the GuildLM Code Guild (go-dev · go-test · go-review) built to work together in a verification-driven agent loop.
Why a reviewer in the loop? A passing test suite proves the cases you thought of.
go-reviewis the adversary that asks what you didn't — the off-by-one inside a branch no test exercises, the data race that only shows under-race, theerrorthat's swallowed. Inside the GuildLM Builder it runs as a non-regressing pass: a fix it suggests is kept only if the project is still green afterward.
Why this isn't "just Qwen with a name"
go-review is a fused, standalone model (no separate adapter) with its own identity — ask who it is and it answers GuildLM go-review. It is an honest Apache-2.0 derivative of Qwen2.5-Coder-7B-Instruct, fine-tuned for one craft: reviewing Go.
What it's for
- Reviewing diffs and files for real bugs: nil derefs, index errors, unhandled errors, races, deadlocks.
- Flagging un-idiomatic Go (ignored
errors, needless allocations, non-stdlib reflexes). - Acting as the review role inside the Builder, catching what a green build and the tests both missed.
Benchmarks
Measured locally with the real Go toolchain.
| Benchmark | Metric | go-review | base 7B |
|---|---|---|---|
crucible go_review_bench (8 planted bugs) |
identify@1 | 6/8 | 7/8 |
Honest note from the research log: for review, the base is already strong (7/8) and per-role fine-tuning is within noise — we don't pretend otherwise.
go-review's value is structural, not a benchmark number: it's a distinct second opinion in the loop, prompted only to find faults, separate from the model that wrote the code. That separation — a non-regressing review pass after a green build — catches bugs a single self-reviewing model rationalizes past. Use it as the third member of the guild, not as a solo linter.
Quickstart
Apple Silicon (MLX)
pip install mlx-lm
python -m mlx_lm generate --model guildlm/go-review \
--prompt "Review this Go for bugs:\n\nfunc Sum(xs []int) int { s := 1; for _, x := range xs { s += x }; return s }" \
--max-tokens 300
Ollama (GGUF)
ollama run guildlm/go-review "Review this handler for race conditions and unhandled errors: ..."
Inside the agent loop (recommended)
python -m mlx_lm server --model guildlm/go-review --port 8082
guildlm-build --spec specs/myservice.yaml --out ./out \
--base-url http://localhost:8080/v1 \
--test-model guildlm/go-test \
--review-model guildlm/go-review --review-base-url http://localhost:8082/v1
Prompting
Trained with the system prompt:
You are GuildLM go-review, a Go code-review specialist from the GuildLM Code Guild.
Give it Go code and ask what's wrong. It reports concrete, located findings rather than vague praise.
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: https://guildlm.github.io/research/
License & attribution
Apache-2.0, inherited from Qwen2.5-Coder-7B-Instruct (© Alibaba Cloud). GuildLM fine-tuning, identity, and packaging under the same license. Trained locally on Apple Silicon with MLX — total cloud spend: $0.
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