--- license: apache-2.0 base_model: Qwen/Qwen3.6-27B library_name: peft pipeline_tag: text-generation language: - en tags: - common-lisp - macros - code-generation - lora - unsloth - chain-of-thought - thinking-traces datasets: - j14i/cl-macros-thinking --- # cl-macro-27b-lora LoRA fine-tune of **Qwen3.6-27B** for generating Common Lisp macros with explicit ` ... ` reasoning traces, followed by an idiomatic `(defmacro ...)` form. This is **Phase 1 (SFT)** of a two-phase training plan. Phase 2 (GRPO with SBCL-execution reward) will follow. ## Intended use - **Drafting `defmacro` forms** that a human (or a downstream compiler / test harness) reviews before execution. - **Teaching tool / exploration:** the reasoning traces walk through variable-capture risks, hygiene decisions, expansion structure — useful for learning the *thinking style* of macro-heavy CL. - **Foundation for RL** (GRPO with `sbcl --script` as a reward signal). Not intended for **auto-executing generated code without review** — see *Limitations* below. ## Training | | | |---|---| | **Base model** | [`Qwen/Qwen3.6-27B`](https://huggingface.co/Qwen/Qwen3.6-27B) (hybrid Gated DeltaNet + full-attention, `model_type: qwen3_5`) | | **Method** | LoRA (rank 32, alpha 64, dropout 0.05) on q/k/v/o/gate/up/down projections | | **Framework** | [Unsloth](https://unsloth.ai/) 2026.5.2 + transformers v5 + trl 0.24 | | **Schedule** | 3 epochs, effective batch 8, max_seq_length 4096, cosine LR 2e-5, warmup 5% | | **Hardware** | Single A100 80 GB (RunPod Community Cloud) | | **Wall clock** | 2 h 07 min | | **Trainable params** | 159 M / 27.5 B (0.58%) | | **Optimizer** | adamw_8bit | | **Precision** | bf16 (`load_in_16bit=True`) | ### Training data [`j14i/cl-macros-thinking`](https://huggingface.co/datasets/j14i/cl-macros-thinking) — 1,828 train / 203 validation examples. Chat-format messages with system + user + assistant turns. The assistant turn contains a ` ` block (real reasoning, not placeholder) followed by an idiomatic `(defmacro ...)`. Derived from the public [`j14i/cl-macros`](https://huggingface.co/datasets/j14i/cl-macros) instruction-tuning dataset. Trace generation was done locally with Qwen3.6-27B (MLX 4-bit) and is documented in the source repo. ### Loss trajectory | step | train loss | eval loss | |---:|---:|---:| | 50 | 0.551 | 0.556 | | 200 | 0.400 | 0.439 | | 400 | 0.313 | 0.383 | | 687 (final) | 0.389 (avg) | **0.367** | Eval loss decreased monotonically by 34% with no upward inflection. `load_best_model_at_end=True` was active, so this is the final saved checkpoint. ## Usage ### Unsloth (recommended for Qwen3.6 — handles Gated DeltaNet kernels) ```python from unsloth import FastLanguageModel model, tokenizer = FastLanguageModel.from_pretrained( model_name="j14i/cl-macro-27b-lora", # adapter dir auto-loads Qwen3.6 base max_seq_length=4096, load_in_16bit=True, full_finetuning=False, ) FastLanguageModel.for_inference(model) SYSTEM = ( "You are an expert Common Lisp macro programmer. Think step by step " "before writing the macro. Always explain your reasoning in ... " "tags, then provide the defmacro form." ) messages = [ {"role": "system", "content": SYSTEM}, {"role": "user", "content": "Write a Common Lisp macro `when-let` that..."}, ] inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device) out = model.generate(inputs, max_new_tokens=2048, temperature=0.6, top_p=0.95) print(tokenizer.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True)) ``` ### MLX (Apple Silicon) After merging the adapter into the base and converting to MLX format (see [`mac_to_mlx.sh`](https://github.com/jborkowski/cl-macro-llm/blob/main/scripts/mac_to_mlx.sh)): ```bash uv run --with mlx-lm python -m mlx_lm.generate \ --model ~/models/cl-macro-27b-lora-mlx-bf16 \ --prompt "Write a Common Lisp macro ..." \ --max-tokens 2048 --temp 0.6 ``` Benchmarks on M-series Mac: | precision | size | speed | peak mem | |---|---:|---:|---:| | bf16 MLX | 50 GB | 9.4 tok/s | 54 GB | | 4-bit MLX | 14 GB | 33 tok/s | 16 GB | ### Adapter-only loading (transformers + peft) ```python from transformers import AutoModelForCausalLM, AutoTokenizer from peft import PeftModel import torch tok = AutoTokenizer.from_pretrained("j14i/cl-macro-27b-lora") base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.6-27B", torch_dtype=torch.bfloat16) model = PeftModel.from_pretrained(base, "j14i/cl-macro-27b-lora") ``` Note: vanilla `transformers` requires v5+ for Qwen3.6 (`model_type: qwen3_5`). Inference quality is the same as Unsloth; throughput is lower because the hybrid Mamba/DeltaNet kernels fall back to PyTorch. ## End-to-end validation A `with-stopwatch` macro generated by this model was verified in **SBCL 2.6.4**: ```lisp (defmacro with-stopwatch (&body body) (let ((start (gensym)) (end (gensym)) (result (gensym))) `(let ((,start (get-internal-real-time))) (let ((,result (progn ,@body))) (let ((,end (get-internal-real-time))) (values (float (/ (- ,end ,start) internal-time-units-per-second)) ,result)))))) ;; (with-stopwatch (sleep 0.3) (* 7 6)) ;; => 0.303329 s, 42 ``` The bf16 model produced this on the first try with no manual edits. The macroexpansion is hygienic (three independent gensyms), the return-value contract matches the prompt, and `get-internal-real-time` / `internal-time-units-per-second` are the correct CL standard names. ## Limitations 1. **Function-name hallucinations under heavy quantization.** The 4-bit MLX version of this model occasionally produces plausible-sounding but non-existent CL function names (e.g., `internal-real-time` instead of the correct `get-internal-real-time`). The bf16 version does not — the model has the correct knowledge in its weights, but 4-bit quantization blurs the distinction. For correctness-sensitive output, prefer bf16. 2. **Novelty ceiling.** Trained on 1,828 macros from established CL traditions (Let Over Lambda, On Lisp, Alexandria, Serapeum, Iterate, Trivia). For genuinely novel macro requirements, the model will tend to remix seen patterns rather than invent. This is expected for SFT on a small corpus. 3. **Edge cases of CL semantics are under-represented:** readtable manipulation, package-system gymnastics, compiler-macros, `defstruct` intricacies, condition system internals. Watch outputs in these areas. 4. **Long reasoning traces can exceed `max_new_tokens`.** The model sometimes "thinks past" the available budget without closing ` ` and emitting the final `(defmacro ...)`. Allow 2048+ tokens for non-trivial prompts; for very complex macros, 4096+. 5. **No tool use, no execution feedback.** This is SFT-only. The model has no way to know whether a function it cites actually exists in CLHS or in any specific implementation. **Always verify generated code in a sandbox.** ## Reproduction Full code, data prep, RunPod launcher, and troubleshooting are at [github.com/jborkowski/cl-macro-llm](https://github.com/jborkowski/cl-macro-llm). ```bash git clone https://github.com/jborkowski/cl-macro-llm cd cl-macro-llm cp .env.example .env # fill in HF_TOKEN, RUNPOD_API_KEY, RUNPOD_EXPECTED_EMAIL, etc. bash scripts/cloud/launch.sh # boots an A100, runs the whole pipeline ``` Approximate cost: **~$3 of A100 time** on RunPod Community Cloud. ## Roadmap - **Phase 2 (planned):** GRPO with an SBCL-execution reward harness. Generated macros that compile and pass an expected-expansion test get positive reward; those that hit `UNDEFINED-FUNCTION` or fail `macroexpand` get negative. The function-name hallucination class (limitation 1) is precisely the failure mode RL with executable feedback eliminates fastest. - More training data, especially edge cases (readtable, package, condition system) and "anti-examples" (subtly broken macros for the model to learn what *not* to do). - Try `lora_dropout=0` after seeing GRPO behavior, paired with early stopping on eval. ## License Released under **Apache 2.0**, inheriting the base model's license. The training dataset is BSD-2-Clause.
