Add fine-tuning artifact API example

api-docs/example-results/finetune-imdb-artifact.json

@@ -0,0 +1,266 @@
+{
+  "request": {
+    "slug": "finetune-imdb-artifact",
+    "title": "Fine-tune and publish a model artifact",
+    "model": "moonshotai/Kimi-K2.6:novita",
+    "instructions": "This is a longer public API example. Do not research, do not call plan_tool, and do not run a sandbox preflight. Launch exactly one CPU-only HF Job using the provided script as inline Python source. Use hardware=cpu-basic and timeout about 30 minutes. Set HUB_MODEL_ID to the requested repo id. Wait for the job to finish, then report the model URL, job URL, and eval metrics.",
+    "input": "Run this exact CPU-only fine-tuning script as one HF Job and publish the artifact to abidlabs/ml-intern-api-imdb-distilbert-20260613-020123. The script fine-tunes distilbert-base-uncased on a small IMDb subset and pushes the model. Do not modify the training objective; only make minimal fixes if the job errors.\n\n```python\nimport os\nimport json\nimport numpy as np\nfrom datasets import load_dataset\nfrom huggingface_hub import HfApi, create_repo\nfrom transformers import (\n    AutoTokenizer,\n    AutoModelForSequenceClassification,\n    DataCollatorWithPadding,\n    Trainer,\n    TrainingArguments,\n)\n\nrepo_id = os.environ[\"HUB_MODEL_ID\"]\nmodel_id = \"distilbert-base-uncased\"\n\nraw = load_dataset(\"imdb\")\ntrain_ds = raw[\"train\"].shuffle(seed=42).select(range(1000))\neval_ds = raw[\"test\"].shuffle(seed=42).select(range(200))\n\ntok = AutoTokenizer.from_pretrained(model_id)\n\ndef tokenize(batch):\n    return tok(batch[\"text\"], truncation=True, max_length=256)\n\ntrain_tok = train_ds.map(tokenize, batched=True, remove_columns=[\"text\"])\neval_tok = eval_ds.map(tokenize, batched=True, remove_columns=[\"text\"])\n\nmodel = AutoModelForSequenceClassification.from_pretrained(model_id, num_labels=2)\ncollator = DataCollatorWithPadding(tok)\n\ndef compute_metrics(eval_pred):\n    logits, labels = eval_pred\n    preds = np.argmax(logits, axis=-1)\n    return {\"accuracy\": float((preds == labels).mean())}\n\nargs = TrainingArguments(\n    output_dir=\"/tmp/imdb-distilbert\",\n    max_steps=60,\n    per_device_train_batch_size=16,\n    per_device_eval_batch_size=32,\n    learning_rate=5e-5,\n    warmup_steps=5,\n    logging_steps=10,\n    save_strategy=\"no\",\n    report_to=\"none\",\n    seed=42,\n)\n\ntrainer = Trainer(\n    model=model,\n    args=args,\n    train_dataset=train_tok,\n    eval_dataset=eval_tok,\n    tokenizer=tok,\n    data_collator=collator,\n    compute_metrics=compute_metrics,\n)\ntrainer.train()\nmetrics = trainer.evaluate()\n\ncreate_repo(repo_id, repo_type=\"model\", exist_ok=True)\nmodel.push_to_hub(repo_id)\ntok.push_to_hub(repo_id)\n\nreadme = f\"\"\"---\nlibrary_name: transformers\ntags:\n- text-classification\n- imdb\n- ml-intern-api-example\nbase_model: {model_id}\n---\n\n# ML Intern API IMDb DistilBERT Example\n\nThis model was produced by a public ML Intern API example run.\n\n- Base model: `{model_id}`\n- Dataset: `imdb`\n- Train subset: 1,000 examples\n- Eval subset: 200 examples\n- Training: 60 Trainer steps, batch size 16, learning rate 5e-5\n- Eval metrics: `{json.dumps(metrics, sort_keys=True)}`\n\"\"\"\nHfApi().upload_file(\n    path_or_fileobj=readme.encode(\"utf-8\"),\n    path_in_repo=\"README.md\",\n    repo_id=repo_id,\n    repo_type=\"model\",\n)\nprint(json.dumps({\"repo_id\": repo_id, \"model_url\": f\"https://huggingface.co/{repo_id}\", \"metrics\": metrics}, indent=2))\n```",
+    "background": true,
+    "max_cost_usd": 15.0,
+    "metadata": {
+      "example_slug": "finetune-imdb-artifact",
+      "repo_id": "abidlabs/ml-intern-api-imdb-distilbert-20260613-020123"
+    }
+  },
+  "response": {
+    "id": "resp_518beb5c9e7c4aeb94b67d763183fdae",
+    "object": "response",
+    "created_at": 1781316084,
+    "completed_at": 1781316903,
+    "status": "completed",
+    "model": "moonshotai/Kimi-K2.6:novita",
+    "background": true,
+    "previous_response_id": null,
+    "session_id": "512f6bad-6e24-42ca-9a72-c02f47716281",
+    "max_cost_usd": 15.0,
+    "instructions": "This is a longer public API example. Do not research, do not call plan_tool, and do not run a sandbox preflight. Launch exactly one CPU-only HF Job using the provided script as inline Python source. Use hardware=cpu-basic and timeout about 30 minutes. Set HUB_MODEL_ID to the requested repo id. Wait for the job to finish, then report the model URL, job URL, and eval metrics.",
+    "output": [
+      {
+        "type": "custom_tool_call",
+        "id": "hf_jobs_0",
+        "name": "hf_jobs",
+        "input": "{\"operation\": \"run\", \"script\": \"\\nimport os\\nimport json\\nimport numpy as np\\nfrom datasets import load_dataset\\nfrom huggingface_hub import HfApi, create_repo\\nfrom transformers import (\\n    AutoTokenizer,\\n    AutoModelForSequenceClassification,\\n    DataCollatorWithPadding,\\n    Trainer,\\n    TrainingArguments,\\n)\\n\\nrepo_id = os.environ[\\\"HUB_MODEL_ID\\\"]\\nmodel_id = \\\"distilbert-base-uncased\\\"\\n\\nraw = load_dataset(\\\"imdb\\\")\\ntrain_ds = raw[\\\"train\\\"].shuffle(seed=42).select(range(1000))\\neval_ds = raw[\\\"test\\\"].shuffle(seed=42).select(range(200))\\n\\ntok = AutoTokenizer.from_pretrained(model_id)\\n\\ndef tokenize(batch):\\n    return tok(batch[\\\"text\\\"], truncation=True, max_length=256)\\n\\ntrain_tok = train_ds.map(tokenize, batched=True, remove_columns=[\\\"text\\\"])\\neval_tok = eval_ds.map(tokenize, batched=True, remove_columns=[\\\"text\\\"])\\n\\nmodel = AutoModelForSequenceClassification.from_pretrained(model_id, num_labels=2)\\ncollator = DataCollatorWithPadding(tok)\\n\\ndef compute_metrics(eval_pred):\\n    logits, labels = eval_pred\\n    preds = np.argmax(logits, axis=-1)\\n    return {\\\"accuracy\\\": float((preds == labels).mean())}\\n\\nargs = TrainingArguments(\\n    output_dir=\\\"/tmp/imdb-distilbert\\\",\\n    max_steps=60,\\n    per_device_train_batch_size=16,\\n    per_device_eval_batch_size=32,\\n    learning_rate=5e-5,\\n    warmup_steps=5,\\n    logging_steps=10,\\n    save_strategy=\\\"no\\\",\\n    report_to=\\\"none\\\",\\n    seed=42,\\n)\\n\\ntrainer = Trainer(\\n    model=model,\\n    args=args,\\n    train_dataset=train_tok,\\n    eval_dataset=eval_tok,\\n    tokenizer=tok,\\n    data_collator=collator,\\n    compute_metrics=compute_metrics,\\n)\\ntrainer.train()\\nmetrics = trainer.evaluate()\\n\\ncreate_repo(repo_id, repo_type=\\\"model\\\", exist_ok=True)\\nmodel.push_to_hub(repo_id)\\ntok.push_to_hub(repo_id)\\n\\nreadme = f\\\"\\\"\\\"---\\nlibrary_name: transformers\\ntags:\\n- text-classification\\n- imdb\\n- ml-intern-api-example\\nbase_model: {model_id}\\n---\\n\\n# ML Intern API IMDb DistilBERT Example\\n\\nThis model was produced by a public ML Intern API example run.\\n\\n- Base model: `{model_id}`\\n- Dataset: `imdb`\\n- Train subset: 1,000 examples\\n- Eval subset: 200 examples\\n- Training: 60 Trainer steps, batch size 16, learning rate 5e-5\\n- Eval metrics: `{json.dumps(metrics, sort_keys=True)}`\\n\\\"\\\"\\\"\\nHfApi().upload_file(\\n    path_or_fileobj=readme.encode(\\\"utf-8\\\"),\\n    path_in_repo=\\\"README.md\\\",\\n    repo_id=repo_id,\\n    repo_type=\\\"model\\\",\\n)\\nprint(json.dumps({\\\"repo_id\\\": repo_id, \\\"model_url\\\": f\\\"https://huggingface.co/{repo_id}\\\", \\\"metrics\\\": metrics}, indent=2))\\n\", \"dependencies\": [\"transformers\", \"datasets\", \"numpy\", \"huggingface_hub\", \"accelerate\"], \"hardware_flavor\": \"cpu-basic\", \"timeout\": \"30m\", \"env\": {\"HUB_MODEL_ID\": \"abidlabs/ml-intern-api-imdb-distilbert-20260613-020123\"}}",
+        "output": "Python job completed!\n\n**Job ID:** 6a2cba0a871c005b5352ba1e\n**Final Status:** RUNNING\n**View at:** https://huggingface.co/jobs/abidlabs/6a2cba0a871c005b5352ba1e\n\n**Logs:**\n```\n(no logs)\n```",
+        "status": "completed"
+      },
+      {
+        "type": "custom_tool_call",
+        "id": "hf_jobs:1",
+        "name": "hf_jobs",
+        "input": "{\"operation\": \"inspect\", \"job_id\": \"6a2cba0a871c005b5352ba1e\"}",
+        "output": "**Job Details** (1 job):\n\n```json\n[\n  {\n    \"id\": \"6a2cba0a871c005b5352ba1e\",\n    \"status\": {\n      \"stage\": \"RUNNING\",\n      \"message\": null\n    },\n    \"command\": [\n      \"/bin/sh\",\n      \"-lc\",\n      \"set -e; _ml_intern_artifacts_dir=\\\"$(mktemp -d)\\\"; printf %s 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\u2026 [truncated 16288 chars]",
+        "status": "completed"
+      },
+      {
+        "type": "custom_tool_call",
+        "id": "hf_jobs:2",
+        "name": "hf_jobs",
+        "input": "{\"operation\": \"logs\", \"job_id\": \"6a2cba0a871c005b5352ba1e\"}",
+        "output": "**Logs for 6a2cba0a871c005b5352ba1e:**\n\n```\nDownloading transformers (10.6MiB)\nDownloading hf-transfer (3.4MiB)\nDownloading setuptools (1.0MiB)\nDownloading pandas (10.4MiB)\nDownloading torch (507.6MiB)\nDownloading nvidia-cufft (204.2MiB)\nDownloading nvidia-cuda-cupti (10.2MiB)\nDownloading triton (192.1MiB)\nDownloading cuda-bindings (6.3MiB)\nDownloading sympy (6.0MiB)\nDownloading aiohttp (1.7MiB)\nDownloading nvidia-nccl-cu13 (196.4MiB)\nDownloading nvidia-cusolver (191.6MiB)\nDownloading nvidia-cufile (1.2MiB)\nDownloading hf-xet (4.3MiB)\nDownloading nvidia-cusparselt-cu13 (162.3MiB)\nDownloading tokenizers (3.1MiB)\nDownloading nvidia-cusparse (139.2MiB)\nDownloading nvidia-nvshmem-cu13 (57.6MiB)\nDownloading nvidia-cuda-runtime (2.1MiB)\nDownloading nvidia-curand (56.8MiB)\nDownloading nvidia-cuda-nvrtc (86.0MiB)\nDownloading pyarrow (46.6MiB)\nDownloading networkx (2.0MiB)\nDownloading nvidia-nvjitlink (38.8MiB)\nDownloading nvidia-cublas (403.5MiB)\nDownloading nvidia-cudnn-cu13 (349.2MiB)\nDownloading numpy (15.9MiB)\nDownloading pygments (1.2MiB)\n Downloaded nvidia-cufile\n Downloaded aiohttp\n Downloaded nvidia-cuda-runtime\n Downloaded pygments\n Downloaded tokenizers\n Downloaded hf-transfer\n Downloaded setuptools\n Downloaded hf-xet\n Downloaded networkx\n Downloaded cuda-bindings\n Downloaded nvidia-cuda-cupti\n Downloaded sympy\n Downloaded numpy\n Downloaded pandas\n Downloaded nvidia-nvjitlink\n Downloaded transformers\n Downloaded nvidia-curand\n Downloaded nvidia-nvshmem-cu13\n Downloaded nvidia-cuda-nvrtc\n Downloaded pyarrow\n Downloaded nvidia-cusparse\n Downloaded nvidia-cusparselt-cu13\n Downloaded nvidia-cusolver\n Downloaded nvidia-nccl-cu13\n Downloaded nvidia-cufft\n Downloaded triton\n Downloaded nvidia-cudnn-cu13\n Downloaded nvidia-cublas\n Downloaded torch\nInstalled 75 packages in 248ms\n/root/.cache/uv/archive-v0/laJH7LQ0RxAmS-_K1Wijk/lib/python3.12/site-packages/huggingface_hub/constants.py:298: FutureWarning: The `HF_HUB_ENABLE_HF_TRANSFER` environment variable is deprecated as 'hf_transfer' is not used anymore. Please use `HF_XET_HIGH_PERFORMANCE` instead to enable high performance transfer with Xet. Visit https://huggingface.co/docs/huggingface_hub/package_reference/environment_variables#hfxethighperformance for more details.\n  warnings.warn(\nTraceback (most recent call last):\n  File \"<string>\", line 18, in <module>\n  File \"/root/.cache/uv/archive-v0/laJH7LQ0RxAmS-_K1Wijk/lib/python3.12/site-packages/datasets/load.py\", line 1698, in load_dataset\n    builder_instance = load_dataset_builder(\n                       ^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/.cache/uv/archive-v0/laJH7LQ0RxAmS-_K1Wijk/lib/python3.12/site-packages/datasets/load.py\", line 1325, in load_dataset_builder\n    dataset_module = dataset_module_factory(\n                     ^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/.cache/uv/archive-v0/laJH7LQ0RxAmS-_K1Wijk/lib/python3.12/site-packages/datasets/load.py\", line 1217, in dataset_module_factory\n    raise e1 from None\n  File \"/root/.cache/uv/archive-v0/laJH7LQ0RxAmS-_K1Wijk/lib/python3.12/site-packages/datasets/load.py\", line 1192, in dataset_module_factory\n    ).get_module()\n      ^^^^^^^^^^^^\n  File \"/root/.cache/uv/archive-v0/laJH7LQ0RxAmS-_K1Wijk/lib/python3.12/site-packages/datasets/load.py\", line 608, in get_module\n    standalone_yaml_path = cached_path(\n                           ^^^^^^^^^^^^\n  File \"/root/.cache/uv/archive-v0/laJH7LQ0RxAmS-_K1Wijk/lib/python3.12/site-packages/datasets/utils/file_utils.py\", line 180, in cached_path\n    ).resolve_path(url_or_filename)\n      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/.cache/uv/archive-v0/laJH7LQ0RxAmS-_K1Wijk/lib/python3.12/site-packages/huggingface_hub/hf_file_system.py\", line 305, in resolve_path\n    parsed = parse_hf_uri(f\"{constants.HF_PROTOCOL}{path}\")\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/root/.cache/uv/archive-v0/laJH7LQ0RxAmS-_K1Wijk/lib/python3.12/site-packages/huggingface_hub/utils/_hf_uris.py\", line 313, in parse_hf_uri\n    return _parse_repo_body(location, type_, raw=raw)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  Fil\u2026 [truncated 428 chars]",
+        "status": "completed"
+      },
+      {
+        "type": "custom_tool_call",
+        "id": "hf_jobs:3",
+        "name": "hf_jobs",
+        "input": "{\"operation\": \"run\", \"script\": \"\\nimport os\\nimport json\\nimport numpy as np\\nfrom datasets import load_dataset\\nfrom huggingface_hub import HfApi, create_repo\\nfrom transformers import (\\n    AutoTokenizer,\\n    AutoModelForSequenceClassification,\\n    DataCollatorWithPadding,\\n    Trainer,\\n    TrainingArguments,\\n)\\n\\nrepo_id = os.environ[\\\"HUB_MODEL_ID\\\"]\\nmodel_id = \\\"distilbert-base-uncased\\\"\\n\\nraw = load_dataset(\\\"stanfordnlp/imdb\\\")\\ntrain_ds = raw[\\\"train\\\"].shuffle(seed=42).select(range(1000))\\neval_ds = raw[\\\"test\\\"].shuffle(seed=42).select(range(200))\\n\\ntok = AutoTokenizer.from_pretrained(model_id)\\n\\ndef tokenize(batch):\\n    return tok(batch[\\\"text\\\"], truncation=True, max_length=256)\\n\\ntrain_tok = train_ds.map(tokenize, batched=True, remove_columns=[\\\"text\\\"])\\neval_tok = eval_ds.map(tokenize, batched=True, remove_columns=[\\\"text\\\"])\\n\\nmodel = AutoModelForSequenceClassification.from_pretrained(model_id, num_labels=2)\\ncollator = DataCollatorWithPadding(tok)\\n\\ndef compute_metrics(eval_pred):\\n    logits, labels = eval_pred\\n    preds = np.argmax(logits, axis=-1)\\n    return {\\\"accuracy\\\": float((preds == labels).mean())}\\n\\nargs = TrainingArguments(\\n    output_dir=\\\"/tmp/imdb-distilbert\\\",\\n    max_steps=60,\\n    per_device_train_batch_size=16,\\n    per_device_eval_batch_size=32,\\n    learning_rate=5e-5,\\n    warmup_steps=5,\\n    logging_steps=10,\\n    save_strategy=\\\"no\\\",\\n    report_to=\\\"none\\\",\\n    seed=42,\\n)\\n\\ntrainer = Trainer(\\n    model=model,\\n    args=args,\\n    train_dataset=train_tok,\\n    eval_dataset=eval_tok,\\n    tokenizer=tok,\\n    data_collator=collator,\\n    compute_metrics=compute_metrics,\\n)\\ntrainer.train()\\nmetrics = trainer.evaluate()\\n\\ncreate_repo(repo_id, repo_type=\\\"model\\\", exist_ok=True)\\nmodel.push_to_hub(repo_id)\\ntok.push_to_hub(repo_id)\\n\\nreadme = f\\\"\\\"\\\"---\\nlibrary_name: transformers\\ntags:\\n- text-classification\\n- imdb\\n- ml-intern-api-example\\nbase_model: {model_id}\\n---\\n\\n# ML Intern API IMDb DistilBERT Example\\n\\nThis model was produced by a public ML Intern API example run.\\n\\n- Base model: `{model_id}`\\n- Dataset: `imdb`\\n- Train subset: 1,000 examples\\n- Eval subset: 200 examples\\n- Training: 60 Trainer steps, batch size 16, learning rate 5e-5\\n- Eval metrics: `{json.dumps(metrics, sort_keys=True)}`\\n\\\"\\\"\\\"\\nHfApi().upload_file(\\n    path_or_fileobj=readme.encode(\\\"utf-8\\\"),\\n    path_in_repo=\\\"README.md\\\",\\n    repo_id=repo_id,\\n    repo_type=\\\"model\\\",\\n)\\nprint(json.dumps({\\\"repo_id\\\": repo_id, \\\"model_url\\\": f\\\"https://huggingface.co/{repo_id}\\\", \\\"metrics\\\": metrics}, indent=2))\\n\", \"dependencies\": [\"transformers\", \"datasets\", \"numpy\", \"huggingface_hub\", \"accelerate\"], \"hardware_flavor\": \"cpu-basic\", \"timeout\": \"30m\", \"env\": {\"HUB_MODEL_ID\": \"abidlabs/ml-intern-api-imdb-distilbert-20260613-020123\"}}",
+        "output": "Python job completed!\n\n**Job ID:** 6a2cba40234ca64b6012104f\n**Final Status:** RUNNING\n**View at:** https://huggingface.co/jobs/abidlabs/6a2cba40234ca64b6012104f\n\n**Logs:**\n```\n[installs truncated]\nInstalled 75 packages in 241ms\n/root/.cache/uv/archive-v0/DdwQhNe2FyP0JLEpfvuoU/lib/python3.12/site-packages/huggingface_hub/constants.py:298: FutureWarning: The `HF_HUB_ENABLE_HF_TRANSFER` environment variable is deprecated as 'hf_transfer' is not used anymore. Please use `HF_XET_HIGH_PERFORMANCE` instead to enable high performance transfer with Xet. Visit https://huggingface.co/docs/huggingface_hub/package_reference/environment_variables#hfxethighperformance for more details.\n  warnings.warn(\n```",
+        "status": "completed"
+      },
+      {
+        "type": "custom_tool_call",
+        "id": "hf_jobs:4",
+        "name": "hf_jobs",
+        "input": "{\"operation\": \"logs\", \"job_id\": \"6a2cba40234ca64b6012104f\"}",
+        "output": "**Logs for 6a2cba40234ca64b6012104f:**\n\n```\nDownloading networkx (2.0MiB)\nDownloading pygments (1.2MiB)\nDownloading nvidia-cuda-cupti (10.2MiB)\nDownloading transformers (10.6MiB)\nDownloading nvidia-cublas (403.5MiB)\nDownloading nvidia-cudnn-cu13 (349.2MiB)\nDownloading nvidia-cusparselt-cu13 (162.3MiB)\nDownloading nvidia-cusparse (139.2MiB)\nDownloading nvidia-curand (56.8MiB)\nDownloading nvidia-nvjitlink (38.8MiB)\nDownloading torch (507.6MiB)\nDownloading hf-transfer (3.4MiB)\nDownloading setuptools (1.0MiB)\nDownloading nvidia-cuda-runtime (2.1MiB)\nDownloading hf-xet (4.3MiB)\nDownloading numpy (15.9MiB)\nDownloading pyarrow (46.6MiB)\nDownloading aiohttp (1.7MiB)\nDownloading sympy (6.0MiB)\nDownloading nvidia-cufile (1.2MiB)\nDownloading pandas (10.4MiB)\nDownloading nvidia-cuda-nvrtc (86.0MiB)\nDownloading nvidia-nvshmem-cu13 (57.6MiB)\nDownloading tokenizers (3.1MiB)\nDownloading cuda-bindings (6.3MiB)\nDownloading nvidia-cusolver (191.6MiB)\nDownloading nvidia-cufft (204.2MiB)\nDownloading triton (192.1MiB)\nDownloading nvidia-nccl-cu13 (196.4MiB)\n Downloaded nvidia-cufile\n Downloaded pygments\n Downloaded aiohttp\n Downloaded setuptools\n Downloaded networkx\n Downloaded nvidia-cuda-runtime\n Downloaded tokenizers\n Downloaded hf-transfer\n Downloaded hf-xet\n Downloaded cuda-bindings\n Downloaded sympy\n Downloaded nvidia-cuda-cupti\n Downloaded numpy\n Downloaded pandas\n Downloaded transformers\n Downloaded nvidia-nvjitlink\n Downloaded pyarrow\n Downloaded nvidia-curand\n Downloaded nvidia-nvshmem-cu13\n Downloaded nvidia-cuda-nvrtc\n Downloaded nvidia-cusparse\n Downloaded nvidia-cusparselt-cu13\n Downloaded nvidia-cusolver\n Downloaded nvidia-nccl-cu13\n Downloaded nvidia-cufft\n Downloaded triton\n Downloaded nvidia-cudnn-cu13\n Downloaded nvidia-cublas\n Downloaded torch\nInstalled 75 packages in 241ms\n/root/.cache/uv/archive-v0/DdwQhNe2FyP0JLEpfvuoU/lib/python3.12/site-packages/huggingface_hub/constants.py:298: FutureWarning: The `HF_HUB_ENABLE_HF_TRANSFER` environment variable is deprecated as 'hf_transfer' is not used anymore. Please use `HF_XET_HIGH_PERFORMANCE` instead to enable high performance transfer with Xet. Visit https://huggingface.co/docs/huggingface_hub/package_reference/environment_variables#hfxethighperformance for more details.\n  warnings.warn(\n\n\rGenerating train split:   0%|          | 0/25000 [00:00<?, ? examples/s]\rGenerating train split: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25000/25000 [00:00<00:00, 252690.23 examples/s]\n\n\rGenerating test split:   0%|          | 0/25000 [00:00<?, ? examples/s]\rGenerating test split: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25000/25000 [00:00<00:00, 324852.77 examples/s]\n\n\rGenerating unsupervised split:   0%|          | 0/50000 [00:00<?, ? examples/s]\rGenerating unsupervised split: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50000/50000 [00:00<00:00, 305963.75 examples/s]\n\n\rMap:   0%|          | 0/1000 [00:00<?, ? examples/s]\rMap: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1000/1000 [00:00<00:00, 2402.92 examples/s]\n\n\rMap:   0%|          | 0/200 [00:00<?, ? examples/s]\rMap: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 200/200 [00:00<00:00, 2548.52 examples/s]\n[transformers] DistilBertForSequenceClassification LOAD REPORT from: distilbert-base-uncased\nKey                     | Status     | \n------------------------+------------+-\nvocab_layer_norm.bias   | UNEXPECTED | \nvocab_transform.bias    | UNEXPECTED | \nvocab_transform.weight  | UNEXPECTED | \nvocab_projector.bias    | UNEXPECTED | \nvocab_layer_norm.weight | UNEXPECTED | \nclassifier.bias         | MISSING    | \nclassifier.weight       | MISSING    | \npre_classifier.bias     | MISSING    | \npre_classifier.weight   | MISSING    | \n\nNotes:\n- UNEXPECTED:\tcan be ignored when loading from different task/architecture; not ok if you expect identical arch.\n- MISSING:\tthose params were newly initialized because missing from the checkpoint. Consider training on your downstream task.\nTraceback (most recent call last):\n  File \"<string>\", line 51, in <module>\nTypeError: Trainer.__init__() got an unexpected keyword argument 'tokenizer'\n```",
+        "status": "completed"
+      },
+      {
+        "type": "custom_tool_call",
+        "id": "hf_jobs:5",
+        "name": "hf_jobs",
+        "input": "{\"operation\": \"run\", \"script\": \"\\nimport os\\nimport json\\nimport numpy as np\\nfrom datasets import load_dataset\\nfrom huggingface_hub import HfApi, create_repo\\nfrom transformers import (\\n    AutoTokenizer,\\n    AutoModelForSequenceClassification,\\n    DataCollatorWithPadding,\\n    Trainer,\\n    TrainingArguments,\\n)\\n\\nrepo_id = os.environ[\\\"HUB_MODEL_ID\\\"]\\nmodel_id = \\\"distilbert-base-uncased\\\"\\n\\nraw = load_dataset(\\\"stanfordnlp/imdb\\\")\\ntrain_ds = raw[\\\"train\\\"].shuffle(seed=42).select(range(1000))\\neval_ds = raw[\\\"test\\\"].shuffle(seed=42).select(range(200))\\n\\ntok = AutoTokenizer.from_pretrained(model_id)\\n\\ndef tokenize(batch):\\n    return tok(batch[\\\"text\\\"], truncation=True, max_length=256)\\n\\ntrain_tok = train_ds.map(tokenize, batched=True, remove_columns=[\\\"text\\\"])\\neval_tok = eval_ds.map(tokenize, batched=True, remove_columns=[\\\"text\\\"])\\n\\nmodel = AutoModelForSequenceClassification.from_pretrained(model_id, num_labels=2)\\ncollator = DataCollatorWithPadding(tok)\\n\\ndef compute_metrics(eval_pred):\\n    logits, labels = eval_pred\\n    preds = np.argmax(logits, axis=-1)\\n    return {\\\"accuracy\\\": float((preds == labels).mean())}\\n\\nargs = TrainingArguments(\\n    output_dir=\\\"/tmp/imdb-distilbert\\\",\\n    max_steps=60,\\n    per_device_train_batch_size=16,\\n    per_device_eval_batch_size=32,\\n    learning_rate=5e-5,\\n    warmup_steps=5,\\n    logging_steps=10,\\n    save_strategy=\\\"no\\\",\\n    report_to=\\\"none\\\",\\n    seed=42,\\n)\\n\\ntrainer = Trainer(\\n    model=model,\\n    args=args,\\n    train_dataset=train_tok,\\n    eval_dataset=eval_tok,\\n    processing_class=tok,\\n    data_collator=collator,\\n    compute_metrics=compute_metrics,\\n)\\ntrainer.train()\\nmetrics = trainer.evaluate()\\n\\ncreate_repo(repo_id, repo_type=\\\"model\\\", exist_ok=True)\\nmodel.push_to_hub(repo_id)\\ntok.push_to_hub(repo_id)\\n\\nreadme = f\\\"\\\"\\\"---\\nlibrary_name: transformers\\ntags:\\n- text-classification\\n- imdb\\n- ml-intern-api-example\\nbase_model: {model_id}\\n---\\n\\n# ML Intern API IMDb DistilBERT Example\\n\\nThis model was produced by a public ML Intern API example run.\\n\\n- Base model: `{model_id}`\\n- Dataset: `imdb`\\n- Train subset: 1,000 examples\\n- Eval subset: 200 examples\\n- Training: 60 Trainer steps, batch size 16, learning rate 5e-5\\n- Eval metrics: `{json.dumps(metrics, sort_keys=True)}`\\n\\\"\\\"\\\"\\nHfApi().upload_file(\\n    path_or_fileobj=readme.encode(\\\"utf-8\\\"),\\n    path_in_repo=\\\"README.md\\\",\\n    repo_id=repo_id,\\n    repo_type=\\\"model\\\",\\n)\\nprint(json.dumps({\\\"repo_id\\\": repo_id, \\\"model_url\\\": f\\\"https://huggingface.co/{repo_id}\\\", \\\"metrics\\\": metrics}, indent=2))\\n\", \"dependencies\": [\"transformers\", \"datasets\", \"numpy\", \"huggingface_hub\", \"accelerate\"], \"hardware_flavor\": \"cpu-basic\", \"timeout\": \"30m\", \"env\": {\"HUB_MODEL_ID\": \"abidlabs/ml-intern-api-imdb-distilbert-20260613-020123\"}}",
+        "output": "Python job completed!\n\n**Job ID:** 6a2cba84871c005b5352ba24\n**Final Status:** RUNNING\n**View at:** https://huggingface.co/jobs/abidlabs/6a2cba84871c005b5352ba24\n\n**Logs:**\n```\n(no logs)\n```",
+        "status": "completed"
+      },
+      {
+        "type": "custom_tool_call",
+        "id": "hf_jobs:6",
+        "name": "hf_jobs",
+        "input": "{\"operation\": \"inspect\", \"job_id\": \"6a2cba84871c005b5352ba24\"}",
+        "output": "**Job Details** (1 job):\n\n```json\n[\n  {\n    \"id\": \"6a2cba84871c005b5352ba24\",\n    \"status\": {\n      \"stage\": \"RUNNING\",\n      \"message\": null\n    },\n    \"command\": [\n      \"/bin/sh\",\n      \"-lc\",\n      \"set -e; _ml_intern_artifacts_dir=\\\"$(mktemp -d)\\\"; printf %s 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\u2026 [truncated 16312 chars]",
+        "status": "completed"
+      },
+      {
+        "type": "custom_tool_call",
+        "id": "hf_jobs:7",
+        "name": "hf_jobs",
+        "input": "{\"operation\": \"logs\", \"job_id\": \"6a2cba84871c005b5352ba24\"}",
+        "output": "**Logs for 6a2cba84871c005b5352ba24:**\n\n```\nDownloading nvidia-cublas (403.5MiB)\nDownloading nvidia-cusparselt-cu13 (162.3MiB)\nDownloading pyarrow (46.6MiB)\nDownloading nvidia-cuda-cupti (10.2MiB)\nDownloading sympy (6.0MiB)\nDownloading pygments (1.2MiB)\nDownloading nvidia-cufile (1.2MiB)\nDownloading tokenizers (3.1MiB)\nDownloading nvidia-cusparse (139.2MiB)\nDownloading nvidia-cuda-runtime (2.1MiB)\nDownloading nvidia-cudnn-cu13 (349.2MiB)\nDownloading nvidia-cufft (204.2MiB)\nDownloading triton (192.1MiB)\nDownloading setuptools (1.0MiB)\nDownloading nvidia-cuda-nvrtc (86.0MiB)\nDownloading hf-xet (4.3MiB)\nDownloading nvidia-cusolver (191.6MiB)\nDownloading hf-transfer (3.4MiB)\nDownloading cuda-bindings (6.3MiB)\nDownloading pandas (10.4MiB)\nDownloading nvidia-nvshmem-cu13 (57.6MiB)\nDownloading nvidia-nccl-cu13 (196.4MiB)\nDownloading nvidia-nvjitlink (38.8MiB)\nDownloading networkx (2.0MiB)\nDownloading numpy (15.9MiB)\nDownloading aiohttp (1.7MiB)\nDownloading torch (507.6MiB)\nDownloading nvidia-curand (56.8MiB)\nDownloading transformers (10.6MiB)\n Downloaded nvidia-cufile\n Downloaded aiohttp\n Downloaded nvidia-cuda-runtime\n Downloaded pygments\n Downloaded tokenizers\n Downloaded setuptools\n Downloaded hf-transfer\n Downloaded networkx\n Downloaded hf-xet\n Downloaded cuda-bindings\n Downloaded nvidia-cuda-cupti\n Downloaded sympy\n Downloaded numpy\n Downloaded pandas\n Downloaded nvidia-nvjitlink\n Downloaded transformers\n Downloaded nvidia-curand\n Downloaded nvidia-nvshmem-cu13\n Downloaded nvidia-cuda-nvrtc\n Downloaded pyarrow\n Downloaded nvidia-cusparse\n Downloaded nvidia-cusparselt-cu13\n Downloaded nvidia-cusolver\n Downloaded nvidia-nccl-cu13\n Downloaded nvidia-cufft\n Downloaded triton\n Downloaded nvidia-cudnn-cu13\n Downloaded nvidia-cublas\n Downloaded torch\nInstalled 75 packages in 249ms\n/root/.cache/uv/archive-v0/rUmhh6q7EMihhAVTbstVN/lib/python3.12/site-packages/huggingface_hub/constants.py:298: FutureWarning: The `HF_HUB_ENABLE_HF_TRANSFER` environment variable is deprecated as 'hf_transfer' is not used anymore. Please use `HF_XET_HIGH_PERFORMANCE` instead to enable high performance transfer with Xet. Visit https://huggingface.co/docs/huggingface_hub/package_reference/environment_variables#hfxethighperformance for more details.\n  warnings.warn(\n\n\rGenerating train split:   0%|          | 0/25000 [00:00<?, ? examples/s]\rGenerating train split: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25000/25000 [00:00<00:00, 262536.49 examples/s]\n\n\rGenerating test split:   0%|          | 0/25000 [00:00<?, ? examples/s]\rGenerating test split: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25000/25000 [00:00<00:00, 299508.42 examples/s]\n\n\rGenerating unsupervised split:   0%|          | 0/50000 [00:00<?, ? examples/s]\rGenerating unsupervised split: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50000/50000 [00:00<00:00, 298079.75 examples/s]\n\n\rMap:   0%|          | 0/1000 [00:00<?, ? examples/s]\rMap: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1000/1000 [00:00<00:00, 2368.15 examples/s]\n\n\rMap:   0%|          | 0/200 [00:00<?, ? examples/s]\rMap: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 200/200 [00:00<00:00, 2555.81 examples/s]\n[transformers] DistilBertForSequenceClassification LOAD REPORT from: distilbert-base-uncased\nKey                     | Status     | \n------------------------+------------+-\nvocab_layer_norm.weight | UNEXPECTED | \nvocab_projector.bias    | UNEXPECTED | \nvocab_transform.bias    | UNEXPECTED | \nvocab_transform.weight  | UNEXPECTED | \nvocab_layer_norm.bias   | UNEXPECTED | \nclassifier.bias         | MISSING    | \npre_classifier.weight   | MISSING    | \nclassifier.weight       | MISSING    | \npre_classifier.bias     | MISSING    | \n\nNotes:\n- UNEXPECTED:\tcan be ignored when loading from different task/architecture; not ok if you expect identical arch.\n- MISSING:\tthose params were newly initialized because missing from the checkpoint. Consider training on your downstream task.\n/root/.cache/uv/archive-v0/rUmhh6q7EMihhAVTbstVN/lib/python3.12/site-packages/torch/utils/data/dataloader.py:752: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n  super().__init__(loader)\n```",
+        "status": "completed"
+      },
+      {
+        "type": "custom_tool_call",
+        "id": "hf_jobs:8",
+        "name": "hf_jobs",
+        "input": "{\"operation\": \"logs\", \"job_id\": \"6a2cba84871c005b5352ba24\"}",
+        "output": "**Logs for 6a2cba84871c005b5352ba24:**\n\n```\nDownloading nvidia-cublas (403.5MiB)\nDownloading nvidia-cusparselt-cu13 (162.3MiB)\nDownloading pyarrow (46.6MiB)\nDownloading nvidia-cuda-cupti (10.2MiB)\nDownloading sympy (6.0MiB)\nDownloading pygments (1.2MiB)\nDownloading nvidia-cufile (1.2MiB)\nDownloading tokenizers (3.1MiB)\nDownloading nvidia-cusparse (139.2MiB)\nDownloading nvidia-cuda-runtime (2.1MiB)\nDownloading nvidia-cudnn-cu13 (349.2MiB)\nDownloading nvidia-cufft (204.2MiB)\nDownloading triton (192.1MiB)\nDownloading setuptools (1.0MiB)\nDownloading nvidia-cuda-nvrtc (86.0MiB)\nDownloading hf-xet (4.3MiB)\nDownloading nvidia-cusolver (191.6MiB)\nDownloading hf-transfer (3.4MiB)\nDownloading cuda-bindings (6.3MiB)\nDownloading pandas (10.4MiB)\nDownloading nvidia-nvshmem-cu13 (57.6MiB)\nDownloading nvidia-nccl-cu13 (196.4MiB)\nDownloading nvidia-nvjitlink (38.8MiB)\nDownloading networkx (2.0MiB)\nDownloading numpy (15.9MiB)\nDownloading aiohttp (1.7MiB)\nDownloading torch (507.6MiB)\nDownloading nvidia-curand (56.8MiB)\nDownloading transformers (10.6MiB)\n Downloaded nvidia-cufile\n Downloaded aiohttp\n Downloaded nvidia-cuda-runtime\n Downloaded pygments\n Downloaded tokenizers\n Downloaded setuptools\n Downloaded hf-transfer\n Downloaded networkx\n Downloaded hf-xet\n Downloaded cuda-bindings\n Downloaded nvidia-cuda-cupti\n Downloaded sympy\n Downloaded numpy\n Downloaded pandas\n Downloaded nvidia-nvjitlink\n Downloaded transformers\n Downloaded nvidia-curand\n Downloaded nvidia-nvshmem-cu13\n Downloaded nvidia-cuda-nvrtc\n Downloaded pyarrow\n Downloaded nvidia-cusparse\n Downloaded nvidia-cusparselt-cu13\n Downloaded nvidia-cusolver\n Downloaded nvidia-nccl-cu13\n Downloaded nvidia-cufft\n Downloaded triton\n Downloaded nvidia-cudnn-cu13\n Downloaded nvidia-cublas\n Downloaded torch\nInstalled 75 packages in 249ms\n/root/.cache/uv/archive-v0/rUmhh6q7EMihhAVTbstVN/lib/python3.12/site-packages/huggingface_hub/constants.py:298: FutureWarning: The `HF_HUB_ENABLE_HF_TRANSFER` environment variable is deprecated as 'hf_transfer' is not used anymore. Please use `HF_XET_HIGH_PERFORMANCE` instead to enable high performance transfer with Xet. Visit https://huggingface.co/docs/huggingface_hub/package_reference/environment_variables#hfxethighperformance for more details.\n  warnings.warn(\n\n\rGenerating train split:   0%|          | 0/25000 [00:00<?, ? examples/s]\rGenerating train split: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25000/25000 [00:00<00:00, 262536.49 examples/s]\n\n\rGenerating test split:   0%|          | 0/25000 [00:00<?, ? examples/s]\rGenerating test split: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25000/25000 [00:00<00:00, 299508.42 examples/s]\n\n\rGenerating unsupervised split:   0%|          | 0/50000 [00:00<?, ? examples/s]\rGenerating unsupervised split: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50000/50000 [00:00<00:00, 298079.75 examples/s]\n\n\rMap:   0%|          | 0/1000 [00:00<?, ? examples/s]\rMap: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1000/1000 [00:00<00:00, 2368.15 examples/s]\n\n\rMap:   0%|          | 0/200 [00:00<?, ? examples/s]\rMap: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 200/200 [00:00<00:00, 2555.81 examples/s]\n[transformers] DistilBertForSequenceClassification LOAD REPORT from: distilbert-base-uncased\nKey                     | Status     | \n------------------------+------------+-\nvocab_layer_norm.weight | UNEXPECTED | \nvocab_projector.bias    | UNEXPECTED | \nvocab_transform.bias    | UNEXPECTED | \nvocab_transform.weight  | UNEXPECTED | \nvocab_layer_norm.bias   | UNEXPECTED | \nclassifier.bias         | MISSING    | \npre_classifier.weight   | MISSING    | \nclassifier.weight       | MISSING    | \npre_classifier.bias     | MISSING    | \n\nNotes:\n- UNEXPECTED:\tcan be ignored when loading from different task/architecture; not ok if you expect identical arch.\n- MISSING:\tthose params were newly initialized because missing from the checkpoint. Consider training on your downstream task.\n/root/.cache/uv/archive-v0/rUmhh6q7EMihhAVTbstVN/lib/python3.12/site-packages/torch/utils/data/dataloader.py:752: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n  super().__init__(loader)\n```",
+        "status": "completed"
+      },
+      {
+        "type": "custom_tool_call",
+        "id": "bash:9",
+        "name": "bash",
+        "input": "{\"command\": \"sleep 30\", \"description\": \"Wait for job to progress\"}",
+        "output": "(no output)",
+        "status": "completed"
+      },
+      {
+        "type": "custom_tool_call",
+        "id": "hf_jobs:10",
+        "name": "hf_jobs",
+        "input": "{\"operation\": \"logs\", \"job_id\": \"6a2cba84871c005b5352ba24\"}",
+        "output": "**Logs for 6a2cba84871c005b5352ba24:**\n\n```\nDownloading nvidia-cublas (403.5MiB)\nDownloading nvidia-cusparselt-cu13 (162.3MiB)\nDownloading pyarrow (46.6MiB)\nDownloading nvidia-cuda-cupti (10.2MiB)\nDownloading sympy (6.0MiB)\nDownloading pygments (1.2MiB)\nDownloading nvidia-cufile (1.2MiB)\nDownloading tokenizers (3.1MiB)\nDownloading nvidia-cusparse (139.2MiB)\nDownloading nvidia-cuda-runtime (2.1MiB)\nDownloading nvidia-cudnn-cu13 (349.2MiB)\nDownloading nvidia-cufft (204.2MiB)\nDownloading triton (192.1MiB)\nDownloading setuptools (1.0MiB)\nDownloading nvidia-cuda-nvrtc (86.0MiB)\nDownloading hf-xet (4.3MiB)\nDownloading nvidia-cusolver (191.6MiB)\nDownloading hf-transfer (3.4MiB)\nDownloading cuda-bindings (6.3MiB)\nDownloading pandas (10.4MiB)\nDownloading nvidia-nvshmem-cu13 (57.6MiB)\nDownloading nvidia-nccl-cu13 (196.4MiB)\nDownloading nvidia-nvjitlink (38.8MiB)\nDownloading networkx (2.0MiB)\nDownloading numpy (15.9MiB)\nDownloading aiohttp (1.7MiB)\nDownloading torch (507.6MiB)\nDownloading nvidia-curand (56.8MiB)\nDownloading transformers (10.6MiB)\n Downloaded nvidia-cufile\n Downloaded aiohttp\n Downloaded nvidia-cuda-runtime\n Downloaded pygments\n Downloaded tokenizers\n Downloaded setuptools\n Downloaded hf-transfer\n Downloaded networkx\n Downloaded hf-xet\n Downloaded cuda-bindings\n Downloaded nvidia-cuda-cupti\n Downloaded sympy\n Downloaded numpy\n Downloaded pandas\n Downloaded nvidia-nvjitlink\n Downloaded transformers\n Downloaded nvidia-curand\n Downloaded nvidia-nvshmem-cu13\n Downloaded nvidia-cuda-nvrtc\n Downloaded pyarrow\n Downloaded nvidia-cusparse\n Downloaded nvidia-cusparselt-cu13\n Downloaded nvidia-cusolver\n Downloaded nvidia-nccl-cu13\n Downloaded nvidia-cufft\n Downloaded triton\n Downloaded nvidia-cudnn-cu13\n Downloaded nvidia-cublas\n Downloaded torch\nInstalled 75 packages in 249ms\n/root/.cache/uv/archive-v0/rUmhh6q7EMihhAVTbstVN/lib/python3.12/site-packages/huggingface_hub/constants.py:298: FutureWarning: The `HF_HUB_ENABLE_HF_TRANSFER` environment variable is deprecated as 'hf_transfer' is not used anymore. Please use `HF_XET_HIGH_PERFORMANCE` instead to enable high performance transfer with Xet. Visit https://huggingface.co/docs/huggingface_hub/package_reference/environment_variables#hfxethighperformance for more details.\n  warnings.warn(\n\n\rGenerating train split:   0%|          | 0/25000 [00:00<?, ? examples/s]\rGenerating train split: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25000/25000 [00:00<00:00, 262536.49 examples/s]\n\n\rGenerating test split:   0%|          | 0/25000 [00:00<?, ? examples/s]\rGenerating test split: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25000/25000 [00:00<00:00, 299508.42 examples/s]\n\n\rGenerating unsupervised split:   0%|          | 0/50000 [00:00<?, ? examples/s]\rGenerating unsupervised split: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50000/50000 [00:00<00:00, 298079.75 examples/s]\n\n\rMap:   0%|          | 0/1000 [00:00<?, ? examples/s]\rMap: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1000/1000 [00:00<00:00, 2368.15 examples/s]\n\n\rMap:   0%|          | 0/200 [00:00<?, ? examples/s]\rMap: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 200/200 [00:00<00:00, 2555.81 examples/s]\n[transformers] DistilBertForSequenceClassification LOAD REPORT from: distilbert-base-uncased\nKey                     | Status     | \n------------------------+------------+-\nvocab_layer_norm.weight | UNEXPECTED | \nvocab_projector.bias    | UNEXPECTED | \nvocab_transform.bias    | UNEXPECTED | \nvocab_transform.weight  | UNEXPECTED | \nvocab_layer_norm.bias   | UNEXPECTED | \nclassifier.bias         | MISSING    | \npre_classifier.weight   | MISSING    | \nclassifier.weight       | MISSING    | \npre_classifier.bias     | MISSING    | \n\nNotes:\n- UNEXPECTED:\tcan be ignored when loading from different task/architecture; not ok if you expect identical arch.\n- MISSING:\tthose params were newly initialized because missing from the checkpoint. Consider training on your downstream task.\n/root/.cache/uv/archive-v0/rUmhh6q7EMihhAVTbstVN/lib/python3.12/site-packages/torch/utils/data/dataloader.py:752: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n  super().__init__(loader)\n```",
+        "status": "completed"
+      },
+      {
+        "type": "custom_tool_call",
+        "id": "hf_jobs:11",
+        "name": "hf_jobs",
+        "input": "{\"operation\": \"inspect\", \"job_id\": \"6a2cba84871c005b5352ba24\"}",
+        "output": "**Job Details** (1 job):\n\n```json\n[\n  {\n    \"id\": \"6a2cba84871c005b5352ba24\",\n    \"status\": {\n      \"stage\": \"RUNNING\",\n      \"message\": null\n    },\n    \"command\": [\n      \"/bin/sh\",\n      \"-lc\",\n      \"set -e; _ml_intern_artifacts_dir=\\\"$(mktemp -d)\\\"; printf %s 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\u2026 [truncated 16312 chars]",
+        "status": "completed"
+      },
+      {
+        "type": "custom_tool_call",
+        "id": "bash:12",
+        "name": "bash",
+        "input": "{\"command\": \"sleep 60\", \"description\": \"Wait for training to finish\"}",
+        "output": "(no output)",
+        "status": "completed"
+      },
+      {
+        "type": "custom_tool_call",
+        "id": "hf_jobs:13",
+        "name": "hf_jobs",
+        "input": "{\"operation\": \"logs\", \"job_id\": \"6a2cba84871c005b5352ba24\"}",
+        "output": "**Logs for 6a2cba84871c005b5352ba24:**\n\n```\nDownloading nvidia-cublas (403.5MiB)\nDownloading nvidia-cusparselt-cu13 (162.3MiB)\nDownloading pyarrow (46.6MiB)\nDownloading nvidia-cuda-cupti (10.2MiB)\nDownloading sympy (6.0MiB)\nDownloading pygments (1.2MiB)\nDownloading nvidia-cufile (1.2MiB)\nDownloading tokenizers (3.1MiB)\nDownloading nvidia-cusparse (139.2MiB)\nDownloading nvidia-cuda-runtime (2.1MiB)\nDownloading nvidia-cudnn-cu13 (349.2MiB)\nDownloading nvidia-cufft (204.2MiB)\nDownloading triton (192.1MiB)\nDownloading setuptools (1.0MiB)\nDownloading nvidia-cuda-nvrtc (86.0MiB)\nDownloading hf-xet (4.3MiB)\nDownloading nvidia-cusolver (191.6MiB)\nDownloading hf-transfer (3.4MiB)\nDownloading cuda-bindings (6.3MiB)\nDownloading pandas (10.4MiB)\nDownloading nvidia-nvshmem-cu13 (57.6MiB)\nDownloading nvidia-nccl-cu13 (196.4MiB)\nDownloading nvidia-nvjitlink (38.8MiB)\nDownloading networkx (2.0MiB)\nDownloading numpy (15.9MiB)\nDownloading aiohttp (1.7MiB)\nDownloading torch (507.6MiB)\nDownloading nvidia-curand (56.8MiB)\nDownloading transformers (10.6MiB)\n Downloaded nvidia-cufile\n Downloaded aiohttp\n Downloaded nvidia-cuda-runtime\n Downloaded pygments\n Downloaded tokenizers\n Downloaded setuptools\n Downloaded hf-transfer\n Downloaded networkx\n Downloaded hf-xet\n Downloaded cuda-bindings\n Downloaded nvidia-cuda-cupti\n Downloaded sympy\n Downloaded numpy\n Downloaded pandas\n Downloaded nvidia-nvjitlink\n Downloaded transformers\n Downloaded nvidia-curand\n Downloaded nvidia-nvshmem-cu13\n Downloaded nvidia-cuda-nvrtc\n Downloaded pyarrow\n Downloaded nvidia-cusparse\n Downloaded nvidia-cusparselt-cu13\n Downloaded nvidia-cusolver\n Downloaded nvidia-nccl-cu13\n Downloaded nvidia-cufft\n Downloaded triton\n Downloaded nvidia-cudnn-cu13\n Downloaded nvidia-cublas\n Downloaded torch\nInstalled 75 packages in 249ms\n/root/.cache/uv/archive-v0/rUmhh6q7EMihhAVTbstVN/lib/python3.12/site-packages/huggingface_hub/constants.py:298: FutureWarning: The `HF_HUB_ENABLE_HF_TRANSFER` environment variable is deprecated as 'hf_transfer' is not used anymore. Please use `HF_XET_HIGH_PERFORMANCE` instead to enable high performance transfer with Xet. Visit https://huggingface.co/docs/huggingface_hub/package_reference/environment_variables#hfxethighperformance for more details.\n  warnings.warn(\n\n\rGenerating train split:   0%|          | 0/25000 [00:00<?, ? examples/s]\rGenerating train split: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25000/25000 [00:00<00:00, 262536.49 examples/s]\n\n\rGenerating test split:   0%|          | 0/25000 [00:00<?, ? examples/s]\rGenerating test split: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25000/25000 [00:00<00:00, 299508.42 examples/s]\n\n\rGenerating unsupervised split:   0%|          | 0/50000 [00:00<?, ? examples/s]\rGenerating unsupervised split: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50000/50000 [00:00<00:00, 298079.75 examples/s]\n\n\rMap:   0%|          | 0/1000 [00:00<?, ? examples/s]\rMap: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1000/1000 [00:00<00:00, 2368.15 examples/s]\n\n\rMap:   0%|          | 0/200 [00:00<?, ? examples/s]\rMap: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 200/200 [00:00<00:00, 2555.81 examples/s]\n[transformers] DistilBertForSequenceClassification LOAD REPORT from: distilbert-base-uncased\nKey                     | Status     | \n------------------------+------------+-\nvocab_layer_norm.weight | UNEXPECTED | \nvocab_projector.bias    | UNEXPECTED | \nvocab_transform.bias    | UNEXPECTED | \nvocab_transform.weight  | UNEXPECTED | \nvocab_layer_norm.bias   | UNEXPECTED | \nclassifier.bias         | MISSING    | \npre_classifier.weight   | MISSING    | \nclassifier.weight       | MISSING    | \npre_classifier.bias     | MISSING    | \n\nNotes:\n- UNEXPECTED:\tcan be ignored when loading from different task/architecture; not ok if you expect identical arch.\n- MISSING:\tthose params were newly initialized because missing from the checkpoint. Consider training on your downstream task.\n/root/.cache/uv/archive-v0/rUmhh6q7EMihhAVTbstVN/lib/python3.12/site-packages/torch/utils/data/dataloader.py:752: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n  super().__init__(loader)\n{'loss': '0.6979', 'grad_norm': '0.8113\u2026 [truncated 55 chars]",
+        "status": "completed"
+      },
+      {
+        "type": "custom_tool_call",
+        "id": "bash:14",
+        "name": "bash",
+        "input": "{\"command\": \"sleep 90\", \"description\": \"Wait for training to complete\"}",
+        "output": "(no output)",
+        "status": "completed"
+      },
+      {
+        "type": "custom_tool_call",
+        "id": "hf_jobs:15",
+        "name": "hf_jobs",
+        "input": "{\"operation\": \"logs\", \"job_id\": \"6a2cba84871c005b5352ba24\"}",
+        "output": "**Logs for 6a2cba84871c005b5352ba24:**\n\n```\nDownloading nvidia-cublas (403.5MiB)\nDownloading nvidia-cusparselt-cu13 (162.3MiB)\nDownloading pyarrow (46.6MiB)\nDownloading nvidia-cuda-cupti (10.2MiB)\nDownloading sympy (6.0MiB)\nDownloading pygments (1.2MiB)\nDownloading nvidia-cufile (1.2MiB)\nDownloading tokenizers (3.1MiB)\nDownloading nvidia-cusparse (139.2MiB)\nDownloading nvidia-cuda-runtime (2.1MiB)\nDownloading nvidia-cudnn-cu13 (349.2MiB)\nDownloading nvidia-cufft (204.2MiB)\nDownloading triton (192.1MiB)\nDownloading setuptools (1.0MiB)\nDownloading nvidia-cuda-nvrtc (86.0MiB)\nDownloading hf-xet (4.3MiB)\nDownloading nvidia-cusolver (191.6MiB)\nDownloading hf-transfer (3.4MiB)\nDownloading cuda-bindings (6.3MiB)\nDownloading pandas (10.4MiB)\nDownloading nvidia-nvshmem-cu13 (57.6MiB)\nDownloading nvidia-nccl-cu13 (196.4MiB)\nDownloading nvidia-nvjitlink (38.8MiB)\nDownloading networkx (2.0MiB)\nDownloading numpy (15.9MiB)\nDownloading aiohttp (1.7MiB)\nDownloading torch (507.6MiB)\nDownloading nvidia-curand (56.8MiB)\nDownloading transformers (10.6MiB)\n Downloaded nvidia-cufile\n Downloaded aiohttp\n Downloaded nvidia-cuda-runtime\n Downloaded pygments\n Downloaded tokenizers\n Downloaded setuptools\n Downloaded hf-transfer\n Downloaded networkx\n Downloaded hf-xet\n Downloaded cuda-bindings\n Downloaded nvidia-cuda-cupti\n Downloaded sympy\n Downloaded numpy\n Downloaded pandas\n Downloaded nvidia-nvjitlink\n Downloaded transformers\n Downloaded nvidia-curand\n Downloaded nvidia-nvshmem-cu13\n Downloaded nvidia-cuda-nvrtc\n Downloaded pyarrow\n Downloaded nvidia-cusparse\n Downloaded nvidia-cusparselt-cu13\n Downloaded nvidia-cusolver\n Downloaded nvidia-nccl-cu13\n Downloaded nvidia-cufft\n Downloaded triton\n Downloaded nvidia-cudnn-cu13\n Downloaded nvidia-cublas\n Downloaded torch\nInstalled 75 packages in 249ms\n/root/.cache/uv/archive-v0/rUmhh6q7EMihhAVTbstVN/lib/python3.12/site-packages/huggingface_hub/constants.py:298: FutureWarning: The `HF_HUB_ENABLE_HF_TRANSFER` environment variable is deprecated as 'hf_transfer' is not used anymore. Please use `HF_XET_HIGH_PERFORMANCE` instead to enable high performance transfer with Xet. Visit https://huggingface.co/docs/huggingface_hub/package_reference/environment_variables#hfxethighperformance for more details.\n  warnings.warn(\n\n\rGenerating train split:   0%|          | 0/25000 [00:00<?, ? examples/s]\rGenerating train split: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25000/25000 [00:00<00:00, 262536.49 examples/s]\n\n\rGenerating test split:   0%|          | 0/25000 [00:00<?, ? examples/s]\rGenerating test split: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25000/25000 [00:00<00:00, 299508.42 examples/s]\n\n\rGenerating unsupervised split:   0%|          | 0/50000 [00:00<?, ? examples/s]\rGenerating unsupervised split: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50000/50000 [00:00<00:00, 298079.75 examples/s]\n\n\rMap:   0%|          | 0/1000 [00:00<?, ? examples/s]\rMap: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1000/1000 [00:00<00:00, 2368.15 examples/s]\n\n\rMap:   0%|          | 0/200 [00:00<?, ? examples/s]\rMap: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 200/200 [00:00<00:00, 2555.81 examples/s]\n[transformers] DistilBertForSequenceClassification LOAD REPORT from: distilbert-base-uncased\nKey                     | Status     | \n------------------------+------------+-\nvocab_layer_norm.weight | UNEXPECTED | \nvocab_projector.bias    | UNEXPECTED | \nvocab_transform.bias    | UNEXPECTED | \nvocab_transform.weight  | UNEXPECTED | \nvocab_layer_norm.bias   | UNEXPECTED | \nclassifier.bias         | MISSING    | \npre_classifier.weight   | MISSING    | \nclassifier.weight       | MISSING    | \npre_classifier.bias     | MISSING    | \n\nNotes:\n- UNEXPECTED:\tcan be ignored when loading from different task/architecture; not ok if you expect identical arch.\n- MISSING:\tthose params were newly initialized because missing from the checkpoint. Consider training on your downstream task.\n/root/.cache/uv/archive-v0/rUmhh6q7EMihhAVTbstVN/lib/python3.12/site-packages/torch/utils/data/dataloader.py:752: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n  super().__init__(loader)\n{'loss': '0.6979', 'grad_norm': '0.8113\u2026 [truncated 146 chars]",
+        "status": "completed"
+      },
+      {
+        "type": "custom_tool_call",
+        "id": "bash:16",
+        "name": "bash",
+        "input": "{\"command\": \"sleep 120\", \"description\": \"Wait for training to finish\"}",
+        "output": "(no output)",
+        "status": "completed"
+      },
+      {
+        "type": "custom_tool_call",
+        "id": "hf_jobs:17",
+        "name": "hf_jobs",
+        "input": "{\"operation\": \"logs\", \"job_id\": \"6a2cba84871c005b5352ba24\"}",
+        "output": "**Logs for 6a2cba84871c005b5352ba24:**\n\n```\nDownloading nvidia-cublas (403.5MiB)\nDownloading nvidia-cusparselt-cu13 (162.3MiB)\nDownloading pyarrow (46.6MiB)\nDownloading nvidia-cuda-cupti (10.2MiB)\nDownloading sympy (6.0MiB)\nDownloading pygments (1.2MiB)\nDownloading nvidia-cufile (1.2MiB)\nDownloading tokenizers (3.1MiB)\nDownloading nvidia-cusparse (139.2MiB)\nDownloading nvidia-cuda-runtime (2.1MiB)\nDownloading nvidia-cudnn-cu13 (349.2MiB)\nDownloading nvidia-cufft (204.2MiB)\nDownloading triton (192.1MiB)\nDownloading setuptools (1.0MiB)\nDownloading nvidia-cuda-nvrtc (86.0MiB)\nDownloading hf-xet (4.3MiB)\nDownloading nvidia-cusolver (191.6MiB)\nDownloading hf-transfer (3.4MiB)\nDownloading cuda-bindings (6.3MiB)\nDownloading pandas (10.4MiB)\nDownloading nvidia-nvshmem-cu13 (57.6MiB)\nDownloading nvidia-nccl-cu13 (196.4MiB)\nDownloading nvidia-nvjitlink (38.8MiB)\nDownloading networkx (2.0MiB)\nDownloading numpy (15.9MiB)\nDownloading aiohttp (1.7MiB)\nDownloading torch (507.6MiB)\nDownloading nvidia-curand (56.8MiB)\nDownloading transformers (10.6MiB)\n Downloaded nvidia-cufile\n Downloaded aiohttp\n Downloaded nvidia-cuda-runtime\n Downloaded pygments\n Downloaded tokenizers\n Downloaded setuptools\n Downloaded hf-transfer\n Downloaded networkx\n Downloaded hf-xet\n Downloaded cuda-bindings\n Downloaded nvidia-cuda-cupti\n Downloaded sympy\n Downloaded numpy\n Downloaded pandas\n Downloaded nvidia-nvjitlink\n Downloaded transformers\n Downloaded nvidia-curand\n Downloaded nvidia-nvshmem-cu13\n Downloaded nvidia-cuda-nvrtc\n Downloaded pyarrow\n Downloaded nvidia-cusparse\n Downloaded nvidia-cusparselt-cu13\n Downloaded nvidia-cusolver\n Downloaded nvidia-nccl-cu13\n Downloaded nvidia-cufft\n Downloaded triton\n Downloaded nvidia-cudnn-cu13\n Downloaded nvidia-cublas\n Downloaded torch\nInstalled 75 packages in 249ms\n/root/.cache/uv/archive-v0/rUmhh6q7EMihhAVTbstVN/lib/python3.12/site-packages/huggingface_hub/constants.py:298: FutureWarning: The `HF_HUB_ENABLE_HF_TRANSFER` environment variable is deprecated as 'hf_transfer' is not used anymore. Please use `HF_XET_HIGH_PERFORMANCE` instead to enable high performance transfer with Xet. Visit https://huggingface.co/docs/huggingface_hub/package_reference/environment_variables#hfxethighperformance for more details.\n  warnings.warn(\n\n\rGenerating train split:   0%|          | 0/25000 [00:00<?, ? examples/s]\rGenerating train split: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25000/25000 [00:00<00:00, 262536.49 examples/s]\n\n\rGenerating test split:   0%|          | 0/25000 [00:00<?, ? examples/s]\rGenerating test split: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25000/25000 [00:00<00:00, 299508.42 examples/s]\n\n\rGenerating unsupervised split:   0%|          | 0/50000 [00:00<?, ? examples/s]\rGenerating unsupervised split: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50000/50000 [00:00<00:00, 298079.75 examples/s]\n\n\rMap:   0%|          | 0/1000 [00:00<?, ? examples/s]\rMap: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1000/1000 [00:00<00:00, 2368.15 examples/s]\n\n\rMap:   0%|          | 0/200 [00:00<?, ? examples/s]\rMap: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 200/200 [00:00<00:00, 2555.81 examples/s]\n[transformers] DistilBertForSequenceClassification LOAD REPORT from: distilbert-base-uncased\nKey                     | Status     | \n------------------------+------------+-\nvocab_layer_norm.weight | UNEXPECTED | \nvocab_projector.bias    | UNEXPECTED | \nvocab_transform.bias    | UNEXPECTED | \nvocab_transform.weight  | UNEXPECTED | \nvocab_layer_norm.bias   | UNEXPECTED | \nclassifier.bias         | MISSING    | \npre_classifier.weight   | MISSING    | \nclassifier.weight       | MISSING    | \npre_classifier.bias     | MISSING    | \n\nNotes:\n- UNEXPECTED:\tcan be ignored when loading from different task/architecture; not ok if you expect identical arch.\n- MISSING:\tthose params were newly initialized because missing from the checkpoint. Consider training on your downstream task.\n/root/.cache/uv/archive-v0/rUmhh6q7EMihhAVTbstVN/lib/python3.12/site-packages/torch/utils/data/dataloader.py:752: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n  super().__init__(loader)\n{'loss': '0.6979', 'grad_norm': '0.8113\u2026 [truncated 236 chars]",
+        "status": "completed"
+      },
+      {
+        "type": "custom_tool_call",
+        "id": "bash:18",
+        "name": "bash",
+        "input": "{\"command\": \"sleep 120\", \"description\": \"Wait for training to finish\"}",
+        "output": "(no output)",
+        "status": "completed"
+      },
+      {
+        "type": "custom_tool_call",
+        "id": "hf_jobs:19",
+        "name": "hf_jobs",
+        "input": "{\"operation\": \"logs\", \"job_id\": \"6a2cba84871c005b5352ba24\"}",
+        "output": "**Logs for 6a2cba84871c005b5352ba24:**\n\n```\nDownloading nvidia-cublas (403.5MiB)\nDownloading nvidia-cusparselt-cu13 (162.3MiB)\nDownloading pyarrow (46.6MiB)\nDownloading nvidia-cuda-cupti (10.2MiB)\nDownloading sympy (6.0MiB)\nDownloading pygments (1.2MiB)\nDownloading nvidia-cufile (1.2MiB)\nDownloading tokenizers (3.1MiB)\nDownloading nvidia-cusparse (139.2MiB)\nDownloading nvidia-cuda-runtime (2.1MiB)\nDownloading nvidia-cudnn-cu13 (349.2MiB)\nDownloading nvidia-cufft (204.2MiB)\nDownloading triton (192.1MiB)\nDownloading setuptools (1.0MiB)\nDownloading nvidia-cuda-nvrtc (86.0MiB)\nDownloading hf-xet (4.3MiB)\nDownloading nvidia-cusolver (191.6MiB)\nDownloading hf-transfer (3.4MiB)\nDownloading cuda-bindings (6.3MiB)\nDownloading pandas (10.4MiB)\nDownloading nvidia-nvshmem-cu13 (57.6MiB)\nDownloading nvidia-nccl-cu13 (196.4MiB)\nDownloading nvidia-nvjitlink (38.8MiB)\nDownloading networkx (2.0MiB)\nDownloading numpy (15.9MiB)\nDownloading aiohttp (1.7MiB)\nDownloading torch (507.6MiB)\nDownloading nvidia-curand (56.8MiB)\nDownloading transformers (10.6MiB)\n Downloaded nvidia-cufile\n Downloaded aiohttp\n Downloaded nvidia-cuda-runtime\n Downloaded pygments\n Downloaded tokenizers\n Downloaded setuptools\n Downloaded hf-transfer\n Downloaded networkx\n Downloaded hf-xet\n Downloaded cuda-bindings\n Downloaded nvidia-cuda-cupti\n Downloaded sympy\n Downloaded numpy\n Downloaded pandas\n Downloaded nvidia-nvjitlink\n Downloaded transformers\n Downloaded nvidia-curand\n Downloaded nvidia-nvshmem-cu13\n Downloaded nvidia-cuda-nvrtc\n Downloaded pyarrow\n Downloaded nvidia-cusparse\n Downloaded nvidia-cusparselt-cu13\n Downloaded nvidia-cusolver\n Downloaded nvidia-nccl-cu13\n Downloaded nvidia-cufft\n Downloaded triton\n Downloaded nvidia-cudnn-cu13\n Downloaded nvidia-cublas\n Downloaded torch\nInstalled 75 packages in 249ms\n/root/.cache/uv/archive-v0/rUmhh6q7EMihhAVTbstVN/lib/python3.12/site-packages/huggingface_hub/constants.py:298: FutureWarning: The `HF_HUB_ENABLE_HF_TRANSFER` environment variable is deprecated as 'hf_transfer' is not used anymore. Please use `HF_XET_HIGH_PERFORMANCE` instead to enable high performance transfer with Xet. Visit https://huggingface.co/docs/huggingface_hub/package_reference/environment_variables#hfxethighperformance for more details.\n  warnings.warn(\n\n\rGenerating train split:   0%|          | 0/25000 [00:00<?, ? examples/s]\rGenerating train split: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25000/25000 [00:00<00:00, 262536.49 examples/s]\n\n\rGenerating test split:   0%|          | 0/25000 [00:00<?, ? examples/s]\rGenerating test split: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25000/25000 [00:00<00:00, 299508.42 examples/s]\n\n\rGenerating unsupervised split:   0%|          | 0/50000 [00:00<?, ? examples/s]\rGenerating unsupervised split: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50000/50000 [00:00<00:00, 298079.75 examples/s]\n\n\rMap:   0%|          | 0/1000 [00:00<?, ? examples/s]\rMap: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1000/1000 [00:00<00:00, 2368.15 examples/s]\n\n\rMap:   0%|          | 0/200 [00:00<?, ? examples/s]\rMap: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 200/200 [00:00<00:00, 2555.81 examples/s]\n[transformers] DistilBertForSequenceClassification LOAD REPORT from: distilbert-base-uncased\nKey                     | Status     | \n------------------------+------------+-\nvocab_layer_norm.weight | UNEXPECTED | \nvocab_projector.bias    | UNEXPECTED | \nvocab_transform.bias    | UNEXPECTED | \nvocab_transform.weight  | UNEXPECTED | \nvocab_layer_norm.bias   | UNEXPECTED | \nclassifier.bias         | MISSING    | \npre_classifier.weight   | MISSING    | \nclassifier.weight       | MISSING    | \npre_classifier.bias     | MISSING    | \n\nNotes:\n- UNEXPECTED:\tcan be ignored when loading from different task/architecture; not ok if you expect identical arch.\n- MISSING:\tthose params were newly initialized because missing from the checkpoint. Consider training on your downstream task.\n/root/.cache/uv/archive-v0/rUmhh6q7EMihhAVTbstVN/lib/python3.12/site-packages/torch/utils/data/dataloader.py:752: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n  super().__init__(loader)\n{'loss': '0.6979', 'grad_norm': '0.8113\u2026 [truncated 411 chars]",
+        "status": "completed"
+      },
+      {
+        "type": "custom_tool_call",
+        "id": "bash:20",
+        "name": "bash",
+        "input": "{\"command\": \"sleep 90\", \"description\": \"Wait for job to finish\"}",
+        "output": "(no output)",
+        "status": "completed"
+      },
+      {
+        "type": "custom_tool_call",
+        "id": "hf_jobs:21",
+        "name": "hf_jobs",
+        "input": "{\"operation\": \"logs\", \"job_id\": \"6a2cba84871c005b5352ba24\"}",
+        "output": "**Logs for 6a2cba84871c005b5352ba24:**\n\n```\nDownloading nvidia-cublas (403.5MiB)\nDownloading nvidia-cusparselt-cu13 (162.3MiB)\nDownloading pyarrow (46.6MiB)\nDownloading nvidia-cuda-cupti (10.2MiB)\nDownloading sympy (6.0MiB)\nDownloading pygments (1.2MiB)\nDownloading nvidia-cufile (1.2MiB)\nDownloading tokenizers (3.1MiB)\nDownloading nvidia-cusparse (139.2MiB)\nDownloading nvidia-cuda-runtime (2.1MiB)\nDownloading nvidia-cudnn-cu13 (349.2MiB)\nDownloading nvidia-cufft (204.2MiB)\nDownloading triton (192.1MiB)\nDownloading setuptools (1.0MiB)\nDownloading nvidia-cuda-nvrtc (86.0MiB)\nDownloading hf-xet (4.3MiB)\nDownloading nvidia-cusolver (191.6MiB)\nDownloading hf-transfer (3.4MiB)\nDownloading cuda-bindings (6.3MiB)\nDownloading pandas (10.4MiB)\nDownloading nvidia-nvshmem-cu13 (57.6MiB)\nDownloading nvidia-nccl-cu13 (196.4MiB)\nDownloading nvidia-nvjitlink (38.8MiB)\nDownloading networkx (2.0MiB)\nDownloading numpy (15.9MiB)\nDownloading aiohttp (1.7MiB)\nDownloading torch (507.6MiB)\nDownloading nvidia-curand (56.8MiB)\nDownloading transformers (10.6MiB)\n Downloaded nvidia-cufile\n Downloaded aiohttp\n Downloaded nvidia-cuda-runtime\n Downloaded pygments\n Downloaded tokenizers\n Downloaded setuptools\n Downloaded hf-transfer\n Downloaded networkx\n Downloaded hf-xet\n Downloaded cuda-bindings\n Downloaded nvidia-cuda-cupti\n Downloaded sympy\n Downloaded numpy\n Downloaded pandas\n Downloaded nvidia-nvjitlink\n Downloaded transformers\n Downloaded nvidia-curand\n Downloaded nvidia-nvshmem-cu13\n Downloaded nvidia-cuda-nvrtc\n Downloaded pyarrow\n Downloaded nvidia-cusparse\n Downloaded nvidia-cusparselt-cu13\n Downloaded nvidia-cusolver\n Downloaded nvidia-nccl-cu13\n Downloaded nvidia-cufft\n Downloaded triton\n Downloaded nvidia-cudnn-cu13\n Downloaded nvidia-cublas\n Downloaded torch\nInstalled 75 packages in 249ms\n/root/.cache/uv/archive-v0/rUmhh6q7EMihhAVTbstVN/lib/python3.12/site-packages/huggingface_hub/constants.py:298: FutureWarning: The `HF_HUB_ENABLE_HF_TRANSFER` environment variable is deprecated as 'hf_transfer' is not used anymore. Please use `HF_XET_HIGH_PERFORMANCE` instead to enable high performance transfer with Xet. Visit https://huggingface.co/docs/huggingface_hub/package_reference/environment_variables#hfxethighperformance for more details.\n  warnings.warn(\n\n\rGenerating train split:   0%|          | 0/25000 [00:00<?, ? examples/s]\rGenerating train split: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25000/25000 [00:00<00:00, 262536.49 examples/s]\n\n\rGenerating test split:   0%|          | 0/25000 [00:00<?, ? examples/s]\rGenerating test split: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25000/25000 [00:00<00:00, 299508.42 examples/s]\n\n\rGenerating unsupervised split:   0%|          | 0/50000 [00:00<?, ? examples/s]\rGenerating unsupervised split: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 50000/50000 [00:00<00:00, 298079.75 examples/s]\n\n\rMap:   0%|          | 0/1000 [00:00<?, ? examples/s]\rMap: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1000/1000 [00:00<00:00, 2368.15 examples/s]\n\n\rMap:   0%|          | 0/200 [00:00<?, ? examples/s]\rMap: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 200/200 [00:00<00:00, 2555.81 examples/s]\n[transformers] DistilBertForSequenceClassification LOAD REPORT from: distilbert-base-uncased\nKey                     | Status     | \n------------------------+------------+-\nvocab_layer_norm.weight | UNEXPECTED | \nvocab_projector.bias    | UNEXPECTED | \nvocab_transform.bias    | UNEXPECTED | \nvocab_transform.weight  | UNEXPECTED | \nvocab_layer_norm.bias   | UNEXPECTED | \nclassifier.bias         | MISSING    | \npre_classifier.weight   | MISSING    | \nclassifier.weight       | MISSING    | \npre_classifier.bias     | MISSING    | \n\nNotes:\n- UNEXPECTED:\tcan be ignored when loading from different task/architecture; not ok if you expect identical arch.\n- MISSING:\tthose params were newly initialized because missing from the checkpoint. Consider training on your downstream task.\n/root/.cache/uv/archive-v0/rUmhh6q7EMihhAVTbstVN/lib/python3.12/site-packages/torch/utils/data/dataloader.py:752: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n  super().__init__(loader)\n{'loss': '0.6979', 'grad_norm': '0.8113\u2026 [truncated 1032 chars]",
+        "status": "completed"
+      },
+      {
+        "type": "custom_tool_call",
+        "id": "hf_jobs:22",
+        "name": "hf_jobs",
+        "input": "{\"operation\": \"inspect\", \"job_id\": \"6a2cba84871c005b5352ba24\"}",
+        "output": "**Job Details** (1 job):\n\n```json\n[\n  {\n    \"id\": \"6a2cba84871c005b5352ba24\",\n    \"status\": {\n      \"stage\": \"COMPLETED\",\n      \"message\": null\n    },\n    \"command\": [\n      \"/bin/sh\",\n      \"-lc\",\n      \"set -e; _ml_intern_artifacts_dir=\\\"$(mktemp -d)\\\"; printf %s IyBBdXRvLWdlbmVyYXRlZCBieSBNTCBJbnRlcm4uIEJlc3QtZWZmb3J0IEh1YiBhcnRpZmFjdCBtZXRhZGF0YSBvbmx5LgpkZWYgX2luc3RhbGxfbWxfaW50ZXJuX2FydGlmYWN0X2hvb2tzKCk6CiAgICBpbXBvcnQgb3MKICAgIGltcG9ydCByZQogICAgaW1wb3J0IHRlbXBmaWxlCiAgICBmcm9tIHBhdGhsaWIgaW1wb3J0IFBhdGgKCiAgICB0cnk6CiAgICAgICAgaW1wb3J0IGh1Z2dpbmdmYWNlX2h1YiBhcyBfaHViCiAgICAgICAgZnJvbSBodWdnaW5nZmFjZV9odWIgaW1wb3J0IEhmQXBpLCBoZl9odWJfZG93bmxvYWQKICAgICAgICBmcm9tIGh1Z2dpbmdmYWNlX2h1Yi5yZXBvY2FyZCBpbXBvcnQgbWV0YWRhdGFfbG9hZCwgbWV0YWRhdGFfc2F2ZQogICAgICAgIGZyb20gaHVnZ2luZ2ZhY2VfaHViLnV0aWxzIGltcG9ydCBFbnRyeU5vdEZvdW5kRXJyb3IsIFJlcG9zaXRvcnlOb3RGb3VuZEVycm9yCiAgICBleGNlcHQgRXhjZXB0aW9uOgogICAgICAgIHJldHVybgoKICAgIHNlc3Npb25faWQgPSAnNTEyZjZiYWQtNmUyNC00MmNhLTlhNzItYzAyZjQ3NzE2MjgxJwogICAgc2Vzc2lvbl9kYXRlID0gJzIwMjYtMDYtMTMnCiAgICBjb2xsZWN0aW9uX3RpdGxlID0gJ21sLWludGVybi1hcnRpZmFjdHMtMjAyNi0wNi0xMy01MTJmNmJhZCcKICAgIHRhZyA9ICdtbC1pbnRlcm4nCiAgICBtYXJrZXIgPSAnPCEtLSBtbC1pbnRlcm4tcHJvdmVuYW5jZSAtLT4nCiAgICBzdXBwb3J0ZWQgPSBbJ2RhdGFzZXQnLCAnbW9kZWwnLCAnc3BhY2UnXQogICAgc2FuZGJveF9zcGFjZV9yZSA9IHJlLmNvbXBpbGUoJ15zYW5kYm94LVthLWYwLTldezh9JCcpCiAgICByZWdpc3RlcmluZyA9IEZhbHNlCiAgICBjb2xsZWN0aW9uX3NsdWcgPSBOb25lCiAgICByZWdpc3RlcmVkID0gc2V0KCkKICAgIHVzYWdlX3JlID0gcmUuY29tcGlsZSgKICAgICAgICByIl4jezIsNn1ccysodXNhZ2V8aG93IHRvIHVzZXx1c2luZyB0aGlzIChtb2RlbHxkYXRhc2V0KXx1c2UgdGhpcyAobW9kZWx8ZGF0YXNldCkpXGIiLAogICAgICAgIHJlLklHTk9SRUNBU0UgfCByZS5NVUxUSUxJTkUsCiAgICApCiAgICBmcm9udF9tYXR0ZXJfcmUgPSByZS5jb21waWxlKHIiXEEtLS1ccypcbi4qP1xuLS0tXHMqXG4/IiwgcmUuRE9UQUxMKQogICAgY29sbGVjdGlvbl9jYWNoZV9wYXRoID0gKAogICAgICAgIG9zLmVudmlyb24uZ2V0KCJNTF9JTlRFUk5fQVJUSUZBQ1RfQ09MTEVDVElPTl9DQUNIRSIpCiAgICAgICAgb3Igc3RyKAogICAgICAgICAgICBQYXRoKHRlbXBmaWxlLmdldHRlbXBkaXIoKSkKICAgICAgICAgICAgLyBmIm1sLWludGVybi1hcnRpZmFjdHMte3Nlc3Npb25faWR9LmNvbGxlY3Rpb24iCiAgICAgICAgKQogICAgKQoKICAgIGRlZiBfdG9rZW4odmFsdWU9Tm9uZSwgYXBpPU5vbmUpOgogICAgICAgIGlmIGlzaW5zdGFuY2UodmFsdWUsIHN0cikgYW5kIHZhbHVlOgogICAgICAgICAgICByZXR1cm4gdmFsdWUKICAgICAgICBhcGlfdG9rZW4gPSBnZXRhdHRyKGFwaSwgInRva2VuIiwgTm9uZSkKICAgICAgICBpZiBpc2luc3RhbmNlKGFwaV90b2tlbiwgc3RyKSBhbmQgYXBpX3Rva2VuOgogICAgICAgICAgICByZXR1cm4gYXBpX3Rva2VuCiAgICAgICAgcmV0dXJuICgKICAgICAgICAgICAgb3MuZW52aXJvbi5nZXQoIkhGX1RPS0VOIikKICAgICAgICAgICAgb3Igb3MuZW52aXJvbi5nZXQoIkhVR0dJTkdGQUNFX0hVQl9UT0tFTiIpCiAgICAgICAgICAgIG9yIE5vbmUKICAgICAgICApCgogICAgZGVmIF9tZXJnZV90YWdzKG1ldGFkYXRhKToKICAgICAgICBtZXRhZGF0YSA9IGRpY3QobWV0YWRhdGEgb3Ige30pCiAgICAgICAgcmF3X3RhZ3MgPSBtZXRhZGF0YS5nZXQoInRhZ3MiKQogICAgICAgIGlmIHJhd190YWdzIGlzIE5vbmU6CiAgICAgICAgICAgIHRhZ3MgPSBbXQogICAgICAgIGVsaWYgaXNpbnN0YW5jZShyYXdfdGFncywgc3RyKToKICAgICAgICAgICAgdGFncyA9IFtyYXdfdGFnc10KICAgICAgICBlbGlmIGlzaW5zdGFuY2UocmF3X3RhZ3MsIGxpc3QpOgogICAgICAgICAgICB0YWdzID0gW3N0cihpdGVtKSBmb3IgaXRlbSBpbiByYXdfdGFnc10KICAgICAgICBlbHNlOgogICAgICAgICAgICB0YWdzID0gW3N0cihyYXdfdGFncyldCiAgICAgICAgaWYgdGFnIG5vdCBpbiB0YWdzOgogICAgICAgICAgICB0YWdzLmFwcGVuZCh0YWcpCiAgICAgICAgbWV0YWRhdGFbInRhZ3MiXSA9IHRhZ3MKICAgICAgICByZXR1cm4gbWV0YWRhdGEKCiAgICBkZWYgX21ldGFkYXRhX2Zyb21fY29udGVudChjb250ZW50KToKICAgICAgICB3aXRoIHRlbXBmaWxlLlRlbXBvcmFyeURpcmVjdG9yeSgpIGFzIHRtcF9kaXI6CiAgICAgICAgICAgIHBhdGggPSBQYXRoKHRtcF9kaXIpIC8gIlJFQURNRS5tZCIKICAgICAgICAgICAgcGF0aC53cml0ZV90ZXh0KGNvbnRlbnQgb3IgIiIsIGVuY29kaW5nPSJ1dGYtOCIpCiAgICAgICAgICAgIHJldHVybiBtZXRhZGF0YV9sb2FkKHBhdGgpIG9yIHt9CgogICAgZGVmIF9jb250ZW50X3dpdGhfbWV0YWRhdGEoY29udGVudCwgbWV0YWRhdGEpOgogICAgICAgIHdpdGggdGVtcGZpbGUuVGVtcG9yYXJ5RGlyZWN0b3J5KCkgYXMgdG1wX2RpcjoKICAgICAgICAgICAgcGF0aCA9IFBhdGgodG1wX2RpcikgLyAiUkVBRE1FLm1kIgogICAgICAgICAgICBwYXRoLndyaXRlX3RleHQoY29udGVudCBvciAiIiwgZW5jb2Rpbmc9InV0Zi04IikKICAgICAgICAgICAgbWV0YWRhdGFfc2F2ZShwYXRoLCBtZXRhZGF0YSkKICAgICAgICAgICAgcmV0dXJuIHBhdGgucmVhZF90ZXh0KGVuY29kaW5nPSJ1dGYtOCIpCgogICAgZGVmIF9ib2R5X3dpdGhvdXRfbWV0YWRhdGEoY29udGVudCk6CiAgICAgICAgcmV0dXJuIGZyb250X21hdHRlcl9yZS5zdWIoIiIsIGNvbnRlbn\u2026 [truncated 16314 chars]",
+        "status": "completed"
+      },
+      {
+        "type": "message",
+        "id": "msg_23",
+        "role": "assistant",
+        "status": "completed",
+        "content": [
+          {
+            "type": "output_text",
+            "text": "Done. The HF Job finished successfully.\n\n**Job ID:** `6a2cba84871c005b5352ba24`  \n**Job URL:** https://huggingface.co/jobs/abidlabs/6a2cba84871c005b5352ba24\n\n**Model published to:**  \nhttps://huggingface.co/abidlabs/ml-intern-api-imdb-distilbert-20260613-020123\n\n**Eval metrics:**\n- `eval_loss`: 0.4811\n- `eval_accuracy`: 0.815\n- `eval_runtime`: 24.20 s\n- `epoch`: 0.9524\n\n**Minimal fixes applied:**\n1. Dataset ID: `\"imdb\"` \u2192 `\"stanfordnlp/imdb\"` (resolved `HfUriError`)\n2. `Trainer` argument: `tokenizer=tok` \u2192 `processing_class=tok` (resolved `TypeError` in current Transformers)"
+          }
+        ]
+      }
+    ],
+    "error": null,
+    "incomplete_details": null,
+    "usage": {
+      "session_id": "512f6bad-6e24-42ca-9a72-c02f47716281",
+      "total_usd": 0.0012,
+      "inference_usd": 0.0,
+      "hf_jobs_estimated_usd": 0.0012,
+      "sandbox_estimated_usd": 0.0,
+      "llm_calls": 24,
+      "hf_jobs_count": 3,
+      "sandbox_count": 0,
+      "prompt_tokens": 1188761,
+      "completion_tokens": 4229,
+      "cache_read_tokens": 1083392,
+      "cache_creation_tokens": 0,
+      "total_tokens": 1192990,
+      "hf_jobs_billable_seconds_estimate": 84,
+      "sandbox_billable_seconds_estimate": 0
+    },
+    "artifacts": [
+      {
+        "type": "hf_job",
+        "id": "6a2cba0a871c005b5352ba1e",
+        "url": "https://huggingface.co/jobs/abidlabs/6a2cba0a871c005b5352ba1e"
+      },
+      {
+        "type": "hf_job",
+        "id": "6a2cba40234ca64b6012104f",
+        "url": "https://huggingface.co/jobs/abidlabs/6a2cba40234ca64b6012104f"
+      },
+      {
+        "type": "hf_job",
+        "id": "6a2cba84871c005b5352ba24",
+        "url": "https://huggingface.co/jobs/abidlabs/6a2cba84871c005b5352ba24"
+      }
+    ],
+    "metadata": {
+      "example_slug": "finetune-imdb-artifact",
+      "repo_id": "abidlabs/ml-intern-api-imdb-distilbert-20260613-020123"
+    }
+  }
+}

api-docs/index.html

@@ -484,6 +484,31 @@ td:first-child code { white-space: nowrap; }
         <p class="result-link"><a href="example-results/dataset-audit-sft-plan.json" target="_blank" rel="noopener">Open captured request + response JSON</a></p>
       </details>
     </article>
+
+    <article class="example">
+      <h3>Fine-tune and publish a model artifact</h3>
+      <div class="meta">
+        <code>completed</code><code>14 min</code><code>hf_jobs</code><code>model artifact</code><code>resp_518beb5c9e7c4aeb94b67d763183fdae</code>
+      </div>
+      <p><b>Task:</b> Launch a CPU HF Job that fine-tunes <code>distilbert-base-uncased</code> on a small IMDb subset, evaluates it, and pushes a model repo.</p>
+      <div class="codebox"><span class="lang">request excerpt</span><button class="copy" type="button">copy</button>
+<pre>{
+  "model": "moonshotai/Kimi-K2.6:novita",
+  "instructions": "Launch exactly one CPU-only HF Job using the provided script as inline Python source. Use hardware=cpu-basic and timeout about 30 minutes. Set HUB_MODEL_ID to the requested repo id. Wait for the job to finish, then report the model URL, job URL, and eval metrics.",
+  "input": "Run this exact CPU-only fine-tuning script as one HF Job and publish the artifact to abidlabs/ml-intern-api-imdb-distilbert-20260613-020123. The script fine-tunes distilbert-base-uncased on a small IMDb subset and pushes the model.",
+  "background": true,
+  "max_cost_usd": 15.0
+}</pre></div>
+      <p><b>Result:</b> The job published
+      <a href="https://huggingface.co/abidlabs/ml-intern-api-imdb-distilbert-20260613-020123" target="_blank" rel="noopener">abidlabs/ml-intern-api-imdb-distilbert-20260613-020123</a>
+      from <a href="https://huggingface.co/jobs/abidlabs/6a2cba84871c005b5352ba24" target="_blank" rel="noopener">HF Job 6a2cba84871c005b5352ba24</a>.
+      The final eval accuracy on the 200-example subset was <code>0.815</code>.</p>
+      <details class="result-details" data-result="example-results/finetune-imdb-artifact.json">
+        <summary>Full API response</summary>
+        <div class="codebox"><span class="lang">json</span><button class="copy" type="button">copy</button><pre>Loading…</pre></div>
+        <p class="result-link"><a href="example-results/finetune-imdb-artifact.json" target="_blank" rel="noopener">Open captured request + response JSON</a></p>
+      </details>
+    </article>
   </div>
 </section>