Publish TRL-ready multi-harness SFT messages and validated student labels

README.md

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+---
+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: [AdithyaSK/qwen38-27b-harbor-rollouts](https://huggingface.co/datasets/AdithyaSK/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.

common_tasks.json

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+[
+  "0000_526_526258_qa_2",
+  "0000_539_539873_qa_3",
+  "0001_074_1074738_qa_1",
+  "0001_170_1170198_qa_3",
+  "0001_170_1170198_qa_4",
+  "0001_234_1234901_qa_3",
+  "0001_239_1239559_qa_4",
+  "0001_257_1257756_qa_3",
+  "0001_261_1261978_qa_5",
+  "0001_293_1293142_qa_5",
+  "0001_379_1379568_qa_1",
+  "0001_579_1579979_qa_2",
+  "0001_599_1599781_qa_5",
+  "0001_660_1660748_qa_2",
+  "0001_667_1667317_qa_1",
+  "0001_690_1690621_qa_2",
+  "0001_866_1866353_qa_1",
+  "0001_916_1916491_qa_4",
+  "0001_930_1930122_qa_2",
+  "0001_955_1955907_qa_3",
+  "0001_969_1969476_qa_3",
+  "0002_094_2094844_qa_1",
+  "0002_375_2375931_qa_3",
+  "0010_376_10376491_qa_3",
+  "0010_482_10482813_qa_1",
+  "0010_489_10489077_qa_1",
+  "0010_515_10515511_qa_1",
+  "0010_624_10624322_qa_3",
+  "0010_624_10624322_qa_4",
+  "0010_637_10637554_qa_1",
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+  "0010_909_10909980_qa_3",
+  "0011_054_11054618_qa_1",
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+  "0012_112_12112066_qa_4",
+  "0012_556_12556896_qa_1",
+  "0012_593_12593899_qa_2",
+  "0012_689_12689524_qa_4",
+  "0012_748_12748314_qa_1",
+  "0012_761_12761066_qa_2",
+  "0012_949_12949007_qa_4",
+  "0012_992_12992267_qa_5",
+  "0013_126_13126909_qa_3",
+  "0014_358_14358635_qa_1",
+  "0014_367_14367970_qa_1",
+  "0014_579_14579474_qa_3",
+  "0015_817_15817041_qa_2",
+  "0016_714_16714217_qa_4",
+  "0016_952_16952441_qa_4",
+  "0016_971_16971389_qa_1",
+  "0017_257_17257933_qa_3",
+  "0017_257_17257933_qa_4",
+  "0017_291_17291057_qa_5",
+  "0017_914_17914820_qa_1",
+  "0017_914_17914820_qa_5",
+  "0018_278_18278432_qa_1",
+  "0018_395_18395380_qa_5",
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