code2lora (c2l-terminal)

A self-contained, Claude-Code-style terminal coding assistant powered by quantized Code2LoRA (C2L) and per-repository adapters — on-device and offline-capable.

pip install code2lora
c2lt                       # first run auto-provisions everything, then you're in

Project page: https://lilianahotsko.github.io/Code2LoRA/ · Checkpoint: code2lora/c2l-evo-multitask-v2 · Live demo: Hugging Face Space

Zero-config install

There is no manual setup. pip install code2lora, then run c2lt. On the first run the tool provisions everything it needs into ~/.cache/c2l/ (one-time, then cached):

That's it — no cloning llama.cpp, no building, no huggingface-cli download, no environment variables. The default backend is GGUF (llama.cpp on CPU), so it runs on a laptop with no GPU and no bitsandbytes.

Prefer to run setup explicitly (e.g. in a Dockerfile or CI)?

c2l-setup                  # provision everything now
c2l-setup --skip-base-gguf # everything except the ~1 GB base model
c2l-setup --offline        # just verify what's already cached
c2l-setup --force          # re-provision from scratch

C2L is a code-completion model (Qwen2.5-Coder-1.5B + repo LoRA), not a chat agent. Give it a code prefix and it completes it.

Quick start

cd /path/to/your/git/repo
c2lt

Inside the REPL:

c2l> /adapt                     # build the repo adapter (also makes the GGUF)
c2l> assert response.status_code ==
c2l> /task qa                   # switch to Q&A-style completion
c2l> /adapt
c2l> What does the retry helper in this repo do?

On first /adapt, the encoder + C2L checkpoint are already cached (from setup), so it just walks your commit history and emits the adapter — CPU-friendly, a few minutes for a small repo.

Install options

pip install code2lora            # CPU + GGUF (default, recommended)
pip install "code2lora[quant]"   # optional: bitsandbytes 4/8-bit backends

# from a source checkout:
cd c2l_terminal && pip install -e .

Commands installed:

REPL commands

Prompting: type a code prefix. Use @path/to/file.py to inject a file once (handy for grounding a qa question in a specific module).

The c2l CLI (scripting)

# 1. build a portable adapter for a repo (CPU, no base LLM loaded)
c2l adapt https://github.com/org/repo --task assert_rhs -o ./adapter --gguf

# 2. run it (gguf backend uses the auto-provisioned base GGUF + llama.cpp)
c2l run --adapter ./adapter --backend gguf --prefix "assert add(2, 2) == "

c2l run --backend gguf finds the base GGUF and llama.cpp automatically from ~/.cache/c2l — no flags needed. You can still override with --base-gguf, --lora-gguf, or the env vars below.

Configuration (all optional)

Everything works with no configuration. Override only if you want to:

Adapters cache under ~/.cache/c2l/adapters/ (keyed by fingerprint). GRU walk state for incremental /adapt lives under ~/.cache/c2l/adapters/.walk_state/.

What's inside this folder

c2l_terminal/
├── pyproject.toml        # installs c2lt + c2l + c2l-setup
├── c2l/                  # vendored Code2LoRA SDK (runtime modules)
│   ├── bootstrap.py      # one-command provisioning (models / llama.cpp / GGUF)
│   ├── pipeline.py       # repo → adapter generation
│   ├── export.py         # PEFT + GGUF export
│   ├── infer.py          # gguf / 4bit / 8bit / hf backends
│   └── ...
└── c2l_terminal/         # interactive REPL (repl, session, commands, ui)

Installing from source? Copy only this folder — it vendors the full c2l SDK; no parent repository is required.

System requirements

/adapt on CPU: a few minutes for a small repo. GGUF completion: ~5–30 s per prompt on a laptop CPU.

Choosing a backend

Troubleshooting

• First run is slow — it's downloading ~5 GB of models + base GGUF once. Subsequent runs are instant. Watch the progress bar; re-run c2l-setup if it was interrupted.
• No llama.cpp binary provisioned — your platform may lack a prebuilt release asset. Install cmake + a C/C++ compiler and re-run c2l-setup (it will build from source), or set C2L_LLAMACPP to an existing build.
• Air-gapped machine — run c2l-setup once on a networked box with the same C2L_HOME, copy ~/.cache/c2l over, then run with C2L_OFFLINE=1.
• Wrong base GGUF — must be Qwen2.5-Coder-1.5B; override with C2L_BASE_GGUF if you manage your own.
• bitsandbytes on CPU — needs ≥ 0.43; otherwise just use the default gguf.