--- pretty_name: Elixir preference optimization — DPO and KTO sets license: apache-2.0 language: - en tags: - elixir - code - code-generation - preference-optimization - dpo - kto - rlhf - otp task_categories: - text-generation size_categories: - n<1K configs: - config_name: dpo-failv0 data_files: data/dpo/dpo_failv0.jsonl default: true - config_name: kto-v0 data_files: data/kto/kto_v0.jsonl - config_name: onpolicy-micro-dpo-v1 data_files: data/onpolicy/qwen25c3b_micro_dpo_v1.jsonl - config_name: onpolicy-round02-dpo data_files: data/onpolicy/qwen25c3b_micro_round2_dpo.jsonl - config_name: onpolicy-round04-dpo data_files: data/onpolicy/qwen25c3b_micro_round4_dpo.jsonl - config_name: onpolicy-round13-dpo data_files: data/onpolicy/qwen25c3b_micro_round13_dpo.jsonl - config_name: onpolicy-round03-kto data_files: data/onpolicy/qwen25c3b_micro_round3_kto.jsonl - config_name: onpolicy-round06-kto data_files: data/onpolicy/qwen25c3b_micro_round6_kto.jsonl - config_name: onpolicy-round11-kto data_files: data/onpolicy/qwen25c3b_micro_round11_kto.jsonl --- # Elixir preference optimization — DPO and KTO sets **909 preference rows in 9 configs** for Elixir code models: 654 DPO pairs, 76 balanced KTO examples, and 179 rows from the on-policy Qwen2.5-Coder-3B refinement rounds. Every chosen completion was executed and passed the project's isolated Elixir scorer (compile + ExUnit + static contract checks); every rejected completion was confirmed to fail the intended gate. The frozen evaluation packs are held out: measured prompt-level and module-level collisions are **zero** for every config. > **Do not execute the rejected completions.** They are deliberately broken, and some > contain intentional defects (swallowed errors, wrong endianness, unsafe state handling). > Treat every `rejected` field as hostile input. ## What "preference" means here These pairs are not style opinions. They encode **executable correctness and static contracts** for Elixir: - `preference_kind: correctness` — the chosen module compiles and passes its tests; the rejected one fails to compile, fails a test, or drops a required contract. - `preference_kind: functional-style` — both sides may behave correctly, but the rejected side uses process state, side effects or a non-idiomatic construct where the task explicitly asked for pure functional code. The rejection is a violated static gate, not a matter of taste. The failure modes were mined from real model output, then re-targeted onto original tasks with new module names and specifications, so no benchmark task is copied into the data. ## Configs | config | rows | format | what it is | | --- | ---: | --- | --- | | `dpo-failv0` | 654 | DPO pairs | Mined from evaluation failures of `gemma12b-elixir-cpt-q4` (40/128) and `qwen35-9b-elixir-cpt-sft-q5` (46/128). 534 correctness pairs + 120 functional-style pairs. | | `kto-v0` | 76 | KTO | Balanced 38 accepted / 38 rejected. Each original task contributes exactly one accepted and one rejected completion; 40 functional-style + 36 correctness rows. | | `onpolicy-micro-dpo-v1` | 36 | DPO pairs | First on-policy DPO round for the Qwen2.5-Coder-3B depth-expanded checkpoint. | | `onpolicy-round02-dpo` | 25 | DPO pairs | Round 2: up to three fresh, scorer-gated prompts per construction weakness; format-only pairs excluded. | | `onpolicy-round04-dpo` | 11 | DPO pairs | Round 4. | | `onpolicy-round13-dpo` | 17 | DPO pairs | Round 13. | | `onpolicy-round03-kto` | 34 | KTO | Round 3 KTO: OTP and bit-syntax focus. | | `onpolicy-round06-kto` | 22 | KTO | Round 6 KTO: functional-style focus (12 functional rows). | | `onpolicy-round11-kto` | 34 | KTO | Round 11 KTO: four rows per family. | DPO family coverage (`dpo-failv0`): otp 134, functional 120, binaries 119, beam 66, stdlib 49, errors 47, abstractions 41, core 41, collections 37. The weakness taxonomy is distilled from the failing completions — the largest buckets are `format-hygiene` (114), `enum-state` (99), `err-swallow` (42), `edge-empty` (42), `functional-style-process-state` (40), `functional-style-agent-state` (40), `bin-size-guard` (33) and `bin-endian` (24); the complete per-weakness counts are in `metadata/dataset_stats.json`. KTO family coverage: functional 40, otp 12, binaries 10, protocol 4, stdlib 4, errors 2, core 2, format 2. The `onpolicy-*` configs are **checkpoint-specific** artifacts of one model's targeted rounds; each round's build manifest (base checkpoint, selection rules, counts, validation) is in `metadata/manifests/`. If you want a single general preference set, use `dpo-failv0` (or `kto-v0` for the balanced KTO formulation). ## Row schemas DPO (`dpo-failv0` and the on-policy DPO configs) — ShareGPT prompt plus two string completions: ```json { "id": "dpo.binaries.s16be.bin-endian", "family": "binaries", "source": "elixir_dpo_failv0", "weakness": "bin-endian", "origin": "designed", "preference_kind": "correctness", "models": ["gemma12b-elixir-cpt-q4", "qwen35-9b-elixir-cpt-sft-q5"], "conversations": [ {"from": "system", "value": "You are a friendly coding assistant ..."}, {"from": "human", "value": "Write a module `S16BE` with: ..."} ], "chosen": "```elixir\ndefmodule S16BE do ... end\n```", "rejected": "```elixir\ndefmodule S16BE do ... end\n```" } ``` KTO (`kto-v0` and the on-policy KTO configs) — TRL conversational schema with a boolean label: ```json { "id": "kto.functional.map-pipeline.chosen", "family": "functional", "label": true, "preference_kind": "functional-style", "weakness": "functional-pipeline", "prompt": [ {"role": "system", "content": "You are a friendly coding assistant ..."}, {"role": "user", "content": "Write a module `KtoFnMapPipeline` with: ..."} ], "completion": [{"role": "assistant", "content": "```elixir ... ```"}] } ``` In DPO files, `models` records which checkpoints' failures motivated the pair and `origin` distinguishes `designed`, `handmade` and `functional-style` rows. The on-policy files carry the same fields plus the round-specific annotations. ## Loading ```python from datasets import load_dataset dpo = load_dataset("jmarceno/elixir-preference", "dpo-failv0", split="train") kto = load_dataset("jmarceno/elixir-preference", "kto-v0", split="train") print(dpo[0]["chosen"][:200]) print(kto[0]["label"], kto[0]["prompt"]) ``` Raw JSONL: ```python from huggingface_hub import hf_hub_download path = hf_hub_download( "jmarceno/elixir-preference", "data/dpo/dpo_failv0.jsonl", repo_type="dataset" ) ``` Shaping the DPO file for TRL `DPOTrainer`: ```python from datasets import load_dataset ROLE = {"system": "system", "human": "user", "gpt": "assistant"} def to_dpo(row): return { "prompt": [ {"role": ROLE[turn["from"]], "content": turn["value"]} for turn in row["conversations"] ], "chosen": [{"role": "assistant", "content": row["chosen"]}], "rejected": [{"role": "assistant", "content": row["rejected"]}], } ds = load_dataset("jmarceno/elixir-preference", "dpo-failv0", split="train") ds = ds.map(to_dpo, remove_columns=ds.column_names) ``` The KTO configs already match the TRL `KTOTrainer` columns (`prompt`, `completion`, `label`) and need no conversion. ## Quality gates - Every `chosen` completion was executed through the real scorer: compile, generated ExUnit tests, and static contract checks. A row only ships when the chosen side has zero test failures and a clean static result; compile warnings are counted and reported. - Every `rejected` completion fails the declared gate for a reason recorded in `weakness`. For functional-style pairs, the rejected side is behaviourally correct and fails only the declared style gate — that is the teaching signal. - Ids are unique per file, and prompts are unique per file. - Zero exact prompt collisions and zero module-name collisions against both frozen evaluation packs. - `metadata/sha256sums.txt` lets you confirm the bytes you downloaded are the bytes that passed those gates. ## Intended use and limitations - **Intended:** DPO or KTO preference tuning of Elixir code models that already produce working code, especially for OTP timeouts, error handling, edge cases, bit syntax and functional style. - **Not intended:** as a standalone fine-tuning dataset (use an SFT set first), for instruction-following evaluation, for multi-turn agent data, or for any non-Elixir language. - 909 rows is small by design. These sets target specific failure families observed in this project's models; they are not a general Elixir preference corpus and will not teach unrelated skills. - Rejected completions are broken on purpose; never execute them in an unsandboxed process. - Do not treat the size of a configuration as its importance: the on-policy rounds exist to document a training loop, not to be pooled blindly into a new run. ## Provenance and licensing Rows were authored for this project and mined from this project's own model evaluations on original tasks. No benchmark task and no upstream repository code is redistributed here. The CPT corpora (which do contain upstream Elixir/Erlang sources) are published separately. Released under **Apache-2.0**. "Elixir" and "Erlang" are trademarks of their respective owners; this dataset is not affiliated with or endorsed by the Elixir or Erlang teams. ## Repository layout ``` data/dpo/dpo_failv0.jsonl dpo-failv0 data/kto/kto_v0.jsonl kto-v0 data/onpolicy/qwen25c3b_micro_*_dpo.jsonl onpolicy-*-dpo configs data/onpolicy/qwen25c3b_micro_*_kto.jsonl onpolicy-*-kto configs metadata/dataset_stats.json per-file rows, hashes, families, pack overlap metadata/sha256sums.txt data-file checksums metadata/provenance.json source commit, exclusions, build note metadata/manifests/*.json per-round build manifests ``` ## Citation ```bibtex @dataset{jmarceno_elixir_preference_2026, author = {jmarceno}, title = {Elixir preference optimization — DPO and KTO sets}, year = {2026}, publisher = {Hugging Face}, url = {https://huggingface.co/datasets/jmarceno/elixir-preference}, note = {Apache-2.0} } ```