| --- |
| pretty_name: SmolDataEnvs Multi-harness SFT |
| language: |
| - en |
| task_categories: |
| - text-generation |
| tags: |
| - sft |
| - trl |
| - tool-use |
| - multi-harness |
| - synthetic |
| size_categories: |
| - 10K<n<100K |
| configs: |
| - config_name: all |
| default: true |
| data_files: |
| - split: train |
| path: conversations/*/*.parquet |
| - config_name: opencode |
| data_files: |
| - split: train |
| path: conversations/opencode/*.parquet |
| - config_name: claude-code |
| data_files: |
| - split: train |
| path: conversations/claude-code/*.parquet |
| - config_name: codex |
| data_files: |
| - split: train |
| path: conversations/codex/*.parquet |
| - config_name: mini-swe-agent |
| data_files: |
| - split: train |
| path: conversations/mini-swe-agent/*.parquet |
| - config_name: lfm25_2_6b |
| data_files: |
| - split: train |
| path: lfm25_2_6b/*/*.parquet |
| - config_name: qwen35_2b |
| data_files: |
| - split: train |
| path: qwen35_2b/*/*.parquet |
| --- |
| |
| # SmolDataEnvs Multi-harness SFT |
|
|
| Ready-to-load TRL SFT data from **Qwen3.8-27B** trajectories on data-analysis tasks. It contains **17,929 assistant responses from 3,189 successful rollouts**, covering **888 unique tasks** and four harnesses. |
|
|
| Each conversational row is one recorded prompt plus the next assistant completion and its available tool schemas. Previous assistant messages and tool outputs are context; only the current completion is supervised. No incompatible conversation roots are stitched together. |
|
|
| ## Choose a configuration |
|
|
| | Configuration | Examples | Rollouts | Use | |
| |---|---:|---:|---| |
| | `all` (default) | 17,929 | 3,189 | Portable prompt/completion messages, all harnesses | |
| | `opencode` | 4,825 | 801 | OpenCode messages | |
| | `claude-code` | 4,581 | 781 | Claude Code messages | |
| | `codex` | 4,078 | 797 | Codex messages | |
| | `mini-swe-agent` | 4,445 | 810 | Mini-SWE-Agent messages | |
| | `lfm25_2_6b` | 17,929 | 3,189 | Validated LFM2.5-2.6B input IDs and labels | |
| | `qwen35_2b` | 17,929 | 3,189 | Validated Qwen3.5-2B input IDs and labels | |
|
|
| ```python |
| from datasets import load_dataset |
| |
| # datasets 5.0.0 preserves the heterogeneous message/tool fields. |
| ds = load_dataset("FineEnvs/SmolDataEnvs-multiharness-sft", "all", split="train") |
| print(ds[0]["prompt"], ds[0]["completion"], ds[0]["tools"]) |
| ``` |
|
|
| `prompt`, `completion` and `tools` use the Datasets `Json` feature. They load as Python lists/dictionaries, not JSON strings. This preserves tool arguments without injecting null-valued keys or flattening nested structures. Files are Parquet; no custom dataset loader is required. |
|
|
| Metadata includes `example_id`, `task_id`, `rollout_id`, `turn_id`, `harness`, `difficulty`, `common_task`, `rollout_turn_count`, teacher/model revisions and the source rollout hash. |
|
|
| ## Quick SFT: LFM2.5-2.6B |
|
|
| The tokenized configuration is the simplest and fastest route for the two validated models. It avoids repeated tokenization and already contains exact completion-only labels (`-100` for context). Install [requirements.txt](requirements.txt) first; the TRL commit is pinned because label handling is version-sensitive. |
|
|
| ```python |
| import torch |
| from datasets import load_dataset |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| from trl import SFTConfig, SFTTrainer |
| |
| model_id = "LiquidAI/LFM2.5-2.6B" |
| revision = "654f9463ce32b05d0429d76fe1f580b27d4c1ac0" |
| ds = load_dataset("FineEnvs/SmolDataEnvs-multiharness-sft", "lfm25_2_6b", split="train") |
| ds = ds.select_columns(["input_ids", "labels"]) |
| tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision) |
| model = AutoModelForCausalLM.from_pretrained( |
| model_id, revision=revision, dtype=torch.bfloat16, attn_implementation="sdpa", |
| ) |
| trainer = SFTTrainer( |
| model=model, |
| processing_class=tokenizer, |
| train_dataset=ds, |
| args=SFTConfig( |
| output_dir="lfm-multiharness-sft", |
| num_train_epochs=2, learning_rate=3e-6, lr_scheduler_type="constant", |
| per_device_train_batch_size=1, gradient_accumulation_steps=8, |
| bf16=True, optim="paged_adamw_8bit", gradient_checkpointing=True, |
| max_length=None, packing=False, loss_type="chunked_nll", |
| dataset_kwargs={"skip_prepare_dataset": True}, |
| assistant_only_loss=False, save_strategy="epoch", report_to="none", |
| ), |
| ) |
| trainer.train() |
| trainer.save_model("lfm-multiharness-sft/final") |
| tokenizer.save_pretrained("lfm-multiharness-sft/final") |
| ``` |
|
|
| These tokenized configurations are specific to the pinned student model/tokenizer revisions. Do not use them for other models. Labels are unshifted; TRL applies the causal shift. Teacher IDs/logprobs are not student targets. |
|
|
| ## Run either model with one script |
|
|
| [train_sft.py](train_sft.py) supports both models, tokenized or conversational input, harness filtering, and matched-task selection. Defaults reproduce the training hyperparameters above, with epoch checkpoint saves. It saves a dataset revision receipt and the model's original inference template. |
|
|
| ```bash |
| hf download FineEnvs/SmolDataEnvs-multiharness-sft --repo-type dataset \ |
| --include train_sft.py requirements.txt --local-dir multiharness-sft |
| cd multiharness-sft |
| pip install -r requirements.txt |
| |
| python train_sft.py --model lfm --output-dir lfm-sft |
| python train_sft.py --model qwen --output-dir qwen-sft |
| ``` |
|
|
| Run the two commands in separate GPU allocations to train concurrently. Full fine-tuning uses long sequences: maximum 68,673 tokens for LFM and 73,577 for Qwen. The reference configuration uses one 80 GB H100 per trainer, without truncation. |
|
|
| ```bash |
| # Use readable messages and native TRL completion-only tokenization instead. |
| python train_sft.py --model qwen --dataset-config all --output-dir qwen-messages |
| |
| # Restrict to OpenCode, or to tasks that have successes in all four harnesses. |
| python train_sft.py --model lfm --harness opencode --output-dir lfm-opencode |
| python train_sft.py --model lfm --common-tasks --output-dir lfm-matched |
| ``` |
|
|
| The conversational recipe uses `completion_only_loss=True`. LFM needs the bundled training template to avoid a generation-prefix mismatch. Qwen's training prefix follows the recorded response's reasoning presence. The script restores the original inference template before saving; reference evaluation uses Qwen thinking disabled and LFM thinking preserved. Other models require their own prefix/mask checks. |
|
|
| ## Selection and provenance |
|
|
| - Source: [FineEnvs/qwen38-27b-harbor-rollouts](https://huggingface.co/datasets/FineEnvs/qwen38-27b-harbor-rollouts), revision `3d826b6854acdb4e4918e5047cdf9f70eb38602e`. |
| - Teacher: `Qwen/Qwen3.8-27B`, revision `1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0`. Collection used Harbor with Daytona sandboxes and retained the first correct, valid captured rollout per task/harness, with at most three attempts. |
| - The attempted pool contained 1,000 tasks: 400 medium and 600 hard. Published successes cover **377 medium and 511 hard unique tasks**. This is success-selected demonstration data, not an estimate of pass@1. |
| - Four credential-containing rollouts remain excluded. No test-task, test-notebook or normalized test-question overlap was found with the fixed 250-task evaluation set used in these experiments. |
| - `common_task=True` identifies **692 tasks with successes in every harness**, or **2,768 matched rollouts**. `common_tasks.json` lists their IDs. If making an additional train/validation split, group by `task_id` so related turns and harnesses remain together. |
| - Sampling is standard turn-level SFT. `rollout_turn_count` is metadata; it does not automatically equalize rollout or harness weights. For a controlled OpenCode-versus-multi-harness comparison, also match task selection and training budget. |
| - `manifest.json` records source checksums, counts, tokenizer revisions, template hashes and student token statistics. Original full captures and teacher logprobs remain in the source dataset. |
|
|
| ## Validation |
|
|
| Every exported row is round-trip checked through Parquet. Student input IDs and labels match the completed full-corpus native TRL audits; historical context is masked and completions contain supervised tokens. The reference training setup also passed save/resume, longest-example and four-harness evaluation smokes. Hub loading and a CPU TRL update are checked separately in `verification.json`. |
|
|
| The default configuration is portable. Model-specific features, APIs and mask semantics were tested with the exact versions in `requirements.txt`; changing tokenizers/templates requires revalidation. Task/data licenses from the source dataset continue to apply. |
|
|
