{ "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 \"\", line 18, in \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\", line 51, in \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