Text Generation
MLX
Safetensors
GGUF
English
code
qwen2
go
golang
code-generation
guildlm
code-guild
coding-assistant
conversational
4-bit precision
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
File size: 5,175 Bytes
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license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
base_model_relation: finetune
language:
- en
- code
library_name: mlx
pipeline_tag: text-generation
tags:
- go
- golang
- code-generation
- guildlm
- code-guild
- mlx
- coding-assistant
---
# GuildLM Β· go-dev
**A small, sharp Go *development* specialist from the GuildLM Code Guild.**
`go-dev` writes clean, idiomatic, **standard-library-first** Go β types, functions, concurrency, and whole multi-file packages. It is one of three specialists in the GuildLM Code Guild (`go-dev` Β· [`go-test`](https://huggingface.co/guildlm/go-test) Β· [`go-review`](https://huggingface.co/guildlm/go-review)) designed to be wrapped in a **verification-driven agent loop** rather than used as a lone chatbot.
> **The bet:** capability = model Γ **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 β with no scaffolding β does not. `go-dev` is the *model* half. The [Builder](https://github.com/guildlm/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** β 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](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) β 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`) β **no reflexive third-party routers**.
- Working as the *implementation* role inside the [GuildLM Builder](https://github.com/guildlm/builder): decompose a spec β `go-dev` writes the code β `go-test` writes the tests β `go build/vet/test` β fix β `go-review` audits.
## Benchmarks
Measured locally with the real Go toolchain (no LLM-as-judge). See the [research log](https://guildlm.github.io/research/) for the full, honest story β including where fine-tuning helps and where the *base* and the *algorithm* are the real levers.
<!-- BENCH:go-dev -->
| 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) | β |
> **Honest note (this is the whole point of GuildLM):** on the solo unit benchmark `go-dev` lands within measurement noise of its base β 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](https://github.com/guildlm/builder) with retrieval grounding, it writes whole stdlib backends that **build, vet and test green on the first try** (`score_backend` 3/3) β which a lone model, prompted once, does not. Use it in the loop; that's where it shines.
## Quickstart
### Apple Silicon (MLX)
```bash
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)
```bash
ollama run guildlm/go-dev "Write a stdlib-only Go net/http key/value service with GET/PUT."
```
### Inside the agent loop (recommended)
```bash
# 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**](https://huggingface.co/guildlm/go-dev) | writes the implementation |
| [**go-test**](https://huggingface.co/guildlm/go-test) | writes thorough table-driven tests |
| [**go-review**](https://huggingface.co/guildlm/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](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) (Β© 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 β **total cloud spend: $0**.
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