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destination path 'FORGE' already exists and is not an empty directory.\n","/content/FORGE\n"]}],"source":["!git clone https://github.com/Sanjay767676/Meta-x-Scaler-Team404--Round2.git FORGE\n","%cd FORGE"]},{"cell_type":"code","source":["!pip install transformers peft accelerate bitsandbytes"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"g1McMFlF22_z","executionInfo":{"status":"ok","timestamp":1777181084714,"user_tz":-330,"elapsed":10216,"user":{"displayName":"Sanjay","userId":"00924777704336014398"}},"outputId":"c642c9ff-1d14-460b-edfc-96ba4dfa4da4"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: transformers in /usr/local/lib/python3.12/dist-packages (5.5.0)\n","Requirement already satisfied: peft in /usr/local/lib/python3.12/dist-packages (0.18.1)\n","Requirement already satisfied: accelerate in /usr/local/lib/python3.12/dist-packages (1.13.0)\n","Requirement already satisfied: bitsandbytes in /usr/local/lib/python3.12/dist-packages (0.49.2)\n","Requirement already satisfied: huggingface-hub<2.0,>=1.5.0 in /usr/local/lib/python3.12/dist-packages (from transformers) (1.10.1)\n","Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.12/dist-packages (from transformers) (2.0.2)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.12/dist-packages (from transformers) (26.0)\n","Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.12/dist-packages (from transformers) (6.0.3)\n","Requirement already satisfied: regex>=2025.10.22 in /usr/local/lib/python3.12/dist-packages (from transformers) (2025.11.3)\n","Requirement already satisfied: tokenizers<=0.23.0,>=0.22.0 in /usr/local/lib/python3.12/dist-packages (from transformers) (0.22.2)\n","Requirement already satisfied: typer in /usr/local/lib/python3.12/dist-packages (from transformers) (0.24.1)\n","Requirement already satisfied: safetensors>=0.4.3 in 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huggingface-hub<2.0,>=1.5.0->transformers) (0.28.1)\n","Requirement already satisfied: typing-extensions>=4.1.0 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<2.0,>=1.5.0->transformers) (4.15.0)\n","Requirement already satisfied: setuptools in /usr/local/lib/python3.12/dist-packages (from torch>=1.13.0->peft) (75.2.0)\n","Requirement already satisfied: sympy>=1.13.3 in /usr/local/lib/python3.12/dist-packages (from torch>=1.13.0->peft) (1.14.0)\n","Requirement already satisfied: networkx>=2.5.1 in /usr/local/lib/python3.12/dist-packages (from torch>=1.13.0->peft) (3.6.1)\n","Requirement already satisfied: jinja2 in /usr/local/lib/python3.12/dist-packages (from torch>=1.13.0->peft) (3.1.6)\n","Requirement already satisfied: cuda-bindings==12.9.4 in /usr/local/lib/python3.12/dist-packages (from torch>=1.13.0->peft) (12.9.4)\n","Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.8.93 in /usr/local/lib/python3.12/dist-packages (from torch>=1.13.0->peft) 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httpx<1,>=0.23.0->huggingface-hub<2.0,>=1.5.0->transformers) (4.13.0)\n","Requirement already satisfied: certifi in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->huggingface-hub<2.0,>=1.5.0->transformers) (2026.2.25)\n","Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->huggingface-hub<2.0,>=1.5.0->transformers) (1.0.9)\n","Requirement already satisfied: idna in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->huggingface-hub<2.0,>=1.5.0->transformers) (3.11)\n","Requirement already satisfied: h11>=0.16 in /usr/local/lib/python3.12/dist-packages (from httpcore==1.*->httpx<1,>=0.23.0->huggingface-hub<2.0,>=1.5.0->transformers) (0.16.0)\n","Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.12/dist-packages (from rich>=12.3.0->typer->transformers) (4.0.0)\n","Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.12/dist-packages (from rich>=12.3.0->typer->transformers) (2.20.0)\n","Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.12/dist-packages (from sympy>=1.13.3->torch>=1.13.0->peft) (1.3.0)\n","Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.12/dist-packages (from jinja2->torch>=1.13.0->peft) (3.0.3)\n","Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.12/dist-packages (from markdown-it-py>=2.2.0->rich>=12.3.0->typer->transformers) (0.1.2)\n"]}]},{"cell_type":"code","source":["!git pull https://github.com/Sanjay767676/Meta-x-Scaler-Team404--Round2.git"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5EvgIRDj28ad","executionInfo":{"status":"ok","timestamp":1777181085328,"user_tz":-330,"elapsed":616,"user":{"displayName":"Sanjay","userId":"00924777704336014398"}},"outputId":"94238674-6c7d-4fec-a05c-cebdd346308b"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["remote: Enumerating objects: 10, done.\u001b[K\n","remote: Counting objects: 10% (1/10)\u001b[K\rremote: Counting objects: 20% (2/10)\u001b[K\rremote: Counting objects: 30% (3/10)\u001b[K\rremote: Counting objects: 40% (4/10)\u001b[K\rremote: Counting objects: 50% (5/10)\u001b[K\rremote: Counting objects: 60% (6/10)\u001b[K\rremote: Counting objects: 70% (7/10)\u001b[K\rremote: Counting objects: 80% (8/10)\u001b[K\rremote: Counting objects: 90% (9/10)\u001b[K\rremote: Counting objects: 100% (10/10)\u001b[K\rremote: Counting objects: 100% (10/10), done.\u001b[K\n","remote: Compressing objects: 16% (1/6)\u001b[K\rremote: Compressing objects: 33% (2/6)\u001b[K\rremote: Compressing objects: 50% (3/6)\u001b[K\rremote: Compressing objects: 66% (4/6)\u001b[K\rremote: Compressing objects: 83% (5/6)\u001b[K\rremote: Compressing objects: 100% (6/6)\u001b[K\rremote: Compressing objects: 100% (6/6), done.\u001b[K\n","remote: Total 7 (delta 2), reused 6 (delta 1), pack-reused 0 (from 0)\u001b[K\n","Unpacking objects: 14% (1/7)\rUnpacking objects: 28% (2/7)\rUnpacking objects: 42% (3/7)\rUnpacking objects: 57% (4/7)\rUnpacking objects: 71% (5/7)\rUnpacking objects: 85% (6/7)\rUnpacking objects: 100% (7/7)\rUnpacking objects: 100% (7/7), 5.13 KiB | 1.71 MiB/s, done.\n","From https://github.com/Sanjay767676/Meta-x-Scaler-Team404--Round2\n"," * branch HEAD -> FETCH_HEAD\n","Updating 45a4e4c..e3161e4\n","Fast-forward\n"," .gitignore | 40 \u001b[32m++++++++\u001b[m\u001b[31m---\u001b[m\n"," FORGE_Training_Colab.ipynb | 128 \u001b[32m++++++++++++++++++++++++++++++++++\u001b[m\n"," README.md | 169 \u001b[32m++++++++++++++++++++++++\u001b[m\u001b[31m---------------------\u001b[m\n"," 3 files changed, 247 insertions(+), 90 deletions(-)\n"," create mode 100644 FORGE_Training_Colab.ipynb\n"]}]},{"cell_type":"code","source":["!ls models"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ToJxDcYg39ey","executionInfo":{"status":"ok","timestamp":1777181085457,"user_tz":-330,"elapsed":130,"user":{"displayName":"Sanjay","userId":"00924777704336014398"}},"outputId":"2f5a9981-eead-4819-9d77-d4897d9fe72d"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["adapters_dpo_0426_0029\tadapters_dpo_0426_0735\n","adapters_dpo_0426_0706\tadapters_dpo_0426_0738\n"]}]},{"cell_type":"code","source":["from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig\n","import torch\n","\n","base_model = \"Qwen/Qwen2.5-Coder-1.5B-Instruct\"\n","\n","bnb_config = BitsAndBytesConfig(\n"," load_in_4bit=True,\n"," bnb_4bit_compute_dtype=torch.float16,\n"," bnb_4bit_use_double_quant=True,\n"," bnb_4bit_quant_type=\"nf4\"\n",")\n","\n","print(\"Loading tokenizer...\")\n","tokenizer = AutoTokenizer.from_pretrained(base_model)\n","\n","print(\"Loading base model...\")\n","model = AutoModelForCausalLM.from_pretrained(\n"," base_model,\n"," device_map=\"auto\",\n"," quantization_config=bnb_config\n",")\n","\n","print(\"Qwen 1.5B model ready.\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":118,"referenced_widgets":["e60ca0fbf3fe40adbc636fbe88dc4ee8","efcd044fe4d7479ea22cbc121a84f716","19cec9d0c75b47fea3e51ee4448ef90b","8b51e4903e61452fa0daa3c7286591c0","9505d1dcd31a4a39b813ef316f477ddc","48f3a73ec8d646dd95e33d6685a3fe3d","b6f240603f3f4c02a5c0d1f972cc18e1","6e076eddf5e64ba0af09b61ce547db02","11b382756805468db281c48da5299f4e","7e6f155e33c34027b75796da9a784f9b","b7d58cc5cc5d4c4a941f0d0b5599d73c"]},"id":"X7zwqilW4Ch9","executionInfo":{"status":"ok","timestamp":1777181115760,"user_tz":-330,"elapsed":30302,"user":{"displayName":"Sanjay","userId":"00924777704336014398"}},"outputId":"c4d8c537-b8da-423d-881a-0aa7df6dc21f"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["Loading tokenizer...\n","Loading base model...\n"]},{"output_type":"stream","name":"stderr","text":["WARNING:torchao:Skipping import of cpp extensions due to incompatible torch version. Please upgrade to torch >= 2.11.0 (found 2.10.0+cu128).\n"]},{"output_type":"display_data","data":{"text/plain":["Loading weights: 0%| | 0/338 [00:00= 0]\n"," return sorted_arr\n","```\n","This function takes an integer array as input. It uses a list comprehension to iterate through the array, filtering out any negative numbers. The resulting list of non-negative numbers is then returned as the output. This approach ensures that all elements in the array are sorted while ignoring any negative values.\n"]}]},{"cell_type":"code","source":["prompt = \"\"\"\n","You are a coding assistant.\n","\n","Return ONLY valid Python code.\n","\n","Task:\n","Write function:\n","\n","def solve(arr):\n","\n","that returns the sorted integer list.\n","Must support negatives, duplicates and empty arrays.\n","No explanation.\n","\"\"\"\n","\n","inputs = tokenizer(prompt, return_tensors=\"pt\").to(model.device)\n","\n","outputs = model.generate(\n"," **inputs,\n"," max_new_tokens=120,\n"," temperature=0.1,\n"," do_sample=False,\n"," pad_token_id=tokenizer.eos_token_id\n",")\n","\n","print(tokenizer.decode(outputs[0], skip_special_tokens=True))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"bXrnC3Ds8TOa","executionInfo":{"status":"ok","timestamp":1777181154909,"user_tz":-330,"elapsed":19922,"user":{"displayName":"Sanjay","userId":"00924777704336014398"}},"outputId":"12cfb2f8-335e-44fd-ea9d-f9f6edbd26d0"},"execution_count":7,"outputs":[{"output_type":"stream","name":"stderr","text":["The following generation flags are not valid and may be ignored: ['temperature', 'top_p', 'top_k']. Set `TRANSFORMERS_VERBOSITY=info` for more details.\n"]},{"output_type":"stream","name":"stdout","text":["\n","You are a coding assistant.\n","\n","Return ONLY valid Python code.\n","\n","Task:\n","Write function:\n","\n","def solve(arr):\n","\n","that returns the sorted integer list.\n","Must support negatives, duplicates and empty arrays.\n","No explanation.\n","Example:\n","solve([10, 2, 8, 3, 5]) -> [2, 3, 5, 8, 10]\n","solve([-1, -4, -6, -7, -9]) -> [-9, -7, -6, -4, -1]\n","solve([]) -> []\n","solve([10, 10, 10]) -> [10, 10, 10]\n","\n","```python\n","def solve(arr):\n"," return sorted(set(arr))\n","```\n","\n","This solution works by converting the\n"]}]},{"cell_type":"code","source":["prompt = \"\"\"\n","Return ONLY Python code.\n","\n","Write:\n","\n","def solve(arr):\n","\n","Rules:\n","1. Keep duplicates\n","2. Support negatives\n","3. Support empty list\n","4. Return sorted list\n","5. No explanation\n","6. No markdown\n","\"\"\"\n","\n","inputs = tokenizer(prompt, return_tensors=\"pt\").to(model.device)\n","\n","outputs = model.generate(\n"," **inputs,\n"," max_new_tokens=80,\n"," do_sample=False,\n"," pad_token_id=tokenizer.eos_token_id\n",")\n","\n","print(tokenizer.decode(outputs[0], skip_special_tokens=True))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4kpENiJq8iTt","executionInfo":{"status":"ok","timestamp":1777181168340,"user_tz":-330,"elapsed":13428,"user":{"displayName":"Sanjay","userId":"00924777704336014398"}},"outputId":"4fb7e0fe-d2ad-4bbc-96cc-d08ab001f65d"},"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","Return ONLY Python code.\n","\n","Write:\n","\n","def solve(arr):\n","\n","Rules:\n","1. Keep duplicates\n","2. Support negatives\n","3. Support empty list\n","4. Return sorted list\n","5. No explanation\n","6. No markdown\n","7. No imports\n","\n","arr = [1, 2, 3, 4, 5]\n","solve(arr) => [1, 2, 3, 4, 5]\n","\n","arr = [-1, -2, -3, -4, -5]\n","solve(arr) => [-5, -4, -3, -2, -1]\n","\n","arr = []\n","\n"]}]},{"cell_type":"code","source":["!git clone https://github.com/Sanjay767676/Meta-x-Scaler-Team404--Round2 FORGE"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"H-Ps4vfrKZDT","executionInfo":{"status":"ok","timestamp":1777181168428,"user_tz":-330,"elapsed":90,"user":{"displayName":"Sanjay","userId":"00924777704336014398"}},"outputId":"a5803877-c46e-4c8d-81c6-00eb3bf603cc"},"execution_count":9,"outputs":[{"output_type":"stream","name":"stdout","text":["fatal: destination path 'FORGE' already exists and is not an empty directory.\n"]}]},{"cell_type":"code","source":["%cd FORGE"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"LNvD97jsKekx","executionInfo":{"status":"ok","timestamp":1777181168471,"user_tz":-330,"elapsed":25,"user":{"displayName":"Sanjay","userId":"00924777704336014398"}},"outputId":"d9e6c28a-5c1c-411a-a423-8bfa06f0aa5f"},"execution_count":10,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/FORGE/FORGE\n"]}]},{"cell_type":"code","source":["!pip install -q unsloth transformers accelerate peft bitsandbytes trl datasets huggingface_hub sentencepiece"],"metadata":{"id":"2tXSJqYcKoW0","executionInfo":{"status":"ok","timestamp":1777181179990,"user_tz":-330,"elapsed":11517,"user":{"displayName":"Sanjay","userId":"00924777704336014398"}}},"execution_count":11,"outputs":[]},{"cell_type":"code","source":["!nvidia-smi"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hmwuVHZNKwWc","executionInfo":{"status":"ok","timestamp":1777181180196,"user_tz":-330,"elapsed":191,"user":{"displayName":"Sanjay","userId":"00924777704336014398"}},"outputId":"78857e0c-6ea0-4a9d-9ef7-b16f0da8cb12"},"execution_count":12,"outputs":[{"output_type":"stream","name":"stdout","text":["Sun Apr 26 05:26:21 2026 \n","+-----------------------------------------------------------------------------------------+\n","| NVIDIA-SMI 580.82.07 Driver Version: 580.82.07 CUDA Version: 13.0 |\n","+-----------------------------------------+------------------------+----------------------+\n","| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |\n","| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |\n","| | | MIG M. |\n","|=========================================+========================+======================|\n","| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 |\n","| N/A 70C P0 31W / 70W | 1345MiB / 15360MiB | 0% Default |\n","| | | N/A |\n","+-----------------------------------------+------------------------+----------------------+\n","\n","+-----------------------------------------------------------------------------------------+\n","| Processes: |\n","| GPU GI CI PID Type Process name GPU Memory |\n","| ID ID Usage |\n","|=========================================================================================|\n","| 0 N/A N/A 31024 C /usr/bin/python3 1342MiB |\n","+-----------------------------------------------------------------------------------------+\n"]}]},{"cell_type":"code","source":["!pip install huggingface_hub"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"BHkNqzRoLx-t","executionInfo":{"status":"ok","timestamp":1777181197871,"user_tz":-330,"elapsed":17673,"user":{"displayName":"Sanjay","userId":"00924777704336014398"}},"outputId":"1a727b9f-6afe-4f02-d37e-983cb0d54363"},"execution_count":13,"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.12/dist-packages (1.10.1)\n","Requirement already satisfied: filelock>=3.10.0 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub) (3.25.2)\n","Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub) (2025.3.0)\n","Requirement already satisfied: hf-xet<2.0.0,>=1.4.3 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub) (1.4.3)\n","Requirement already satisfied: httpx<1,>=0.23.0 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub) (0.28.1)\n","Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub) (26.0)\n","Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub) (6.0.3)\n","Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub) (4.67.3)\n","Requirement already satisfied: typer in /usr/local/lib/python3.12/dist-packages (from huggingface_hub) (0.24.1)\n","Requirement already satisfied: typing-extensions>=4.1.0 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub) (4.15.0)\n","Requirement already satisfied: anyio in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->huggingface_hub) (4.13.0)\n","Requirement already satisfied: certifi in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->huggingface_hub) (2026.2.25)\n","Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->huggingface_hub) (1.0.9)\n","Requirement already satisfied: idna in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->huggingface_hub) (3.11)\n","Requirement already satisfied: h11>=0.16 in /usr/local/lib/python3.12/dist-packages (from httpcore==1.*->httpx<1,>=0.23.0->huggingface_hub) (0.16.0)\n","Requirement already satisfied: click>=8.2.1 in /usr/local/lib/python3.12/dist-packages (from typer->huggingface_hub) (8.3.2)\n","Requirement already satisfied: shellingham>=1.3.0 in /usr/local/lib/python3.12/dist-packages (from typer->huggingface_hub) (1.5.4)\n","Requirement already satisfied: rich>=12.3.0 in /usr/local/lib/python3.12/dist-packages (from typer->huggingface_hub) (13.9.4)\n","Requirement already satisfied: annotated-doc>=0.0.2 in /usr/local/lib/python3.12/dist-packages (from typer->huggingface_hub) (0.0.4)\n","Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.12/dist-packages (from rich>=12.3.0->typer->huggingface_hub) (4.0.0)\n","Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.12/dist-packages (from rich>=12.3.0->typer->huggingface_hub) (2.20.0)\n","Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.12/dist-packages (from markdown-it-py>=2.2.0->rich>=12.3.0->typer->huggingface_hub) (0.1.2)\n"]}]},{"cell_type":"code","source":["import os\n","from huggingface_hub import login\n","\n","token = os.getenv(\"HF_TOKEN\")\n","login(token=token)"],"metadata":{"id":"qCCyoPBlL2Zf","executionInfo":{"status":"ok","timestamp":1777181197876,"user_tz":-330,"elapsed":2,"user":{"displayName":"Sanjay","userId":"00924777704336014398"}}},"execution_count":14,"outputs":[]},{"cell_type":"code","source":["from huggingface_hub import whoami\n","print(whoami())"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"20lHICYqNVqW","executionInfo":{"status":"ok","timestamp":1777181216113,"user_tz":-330,"elapsed":144,"user":{"displayName":"Sanjay","userId":"00924777704336014398"}},"outputId":"98071b19-b698-4e3c-f2cd-8b275d3182b2"},"execution_count":16,"outputs":[{"output_type":"stream","name":"stdout","text":["{'type': 'user', 'id': '695d18be17002c38b19d3079', 'name': 'sanjay7676', 'fullname': 'sanjay k', 'canPay': True, 'billingMode': 'prepaid', 'periodEnd': 1777593600, 'isPro': False, 'avatarUrl': '/avatars/11d036b378a81e6517cc296cc814ae36.svg', 'orgs': [], 'auth': {'type': 'access_token', 'accessToken': {'displayName': 'colob', 'role': 'fineGrained', 'createdAt': '2026-04-26T05:11:46.122Z', 'fineGrained': {'canReadGatedRepos': True, 'global': ['discussion.write', 'post.write'], 'scoped': [{'entity': {'_id': '695d18be17002c38b19d3079', 'type': 'user', 'name': 'sanjay7676'}, 'permissions': ['repo.content.read', 'repo.access.read', 'repo.write', 'inference.serverless.write', 'inference.endpoints.infer.write', 'inference.endpoints.write', 'user.webhooks.read', 'user.webhooks.write', 'collection.read', 'collection.write', 'discussion.write', 'user.billing.read', 'job.write']}]}}}}\n"]}]},{"cell_type":"code","source":["!pwd\n","!ls\n","!ls models"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"efvoyDeuNpn9","executionInfo":{"status":"ok","timestamp":1777181219594,"user_tz":-330,"elapsed":351,"user":{"displayName":"Sanjay","userId":"00924777704336014398"}},"outputId":"4cf9c6f0-75ab-4956-896b-2dc68df4de39"},"execution_count":17,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/FORGE/FORGE\n","agents.py\t\t llm_agent.py policies\t setup_colab.sh\n","api_server.py\t\t logger.py\t README.md\t storage\n","app.py\t\t\t logs\t requirements.txt tasks.py\n","cli_demo.py\t\t memory.py\t rewards.py\t train_colab.py\n","config.py\t\t metrics\t run_compare.sh trainer.py\n","data\t\t\t MINI_BLOG.md run_dpo.sh\t train_unsloth.py\n","env.py\t\t\t models\t run_grpo.sh\n","FORGE_Training_Colab.ipynb openenv.yaml sandbox.py\n","guide.md\t\t outputs\t services\n","adapters_dpo_0426_0029\tadapters_dpo_0426_0735\n","adapters_dpo_0426_0706\tadapters_dpo_0426_0738\n"]}]},{"cell_type":"code","source":["!find /content -type d | grep adapter"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NgckNV6hN2-K","executionInfo":{"status":"ok","timestamp":1777181222049,"user_tz":-330,"elapsed":151,"user":{"displayName":"Sanjay","userId":"00924777704336014398"}},"outputId":"1d157ee8-2539-4b9f-c52d-100ebdbbb7ec"},"execution_count":18,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/FORGE/models/adapters_dpo_0426_0029\n","/content/FORGE/models/adapters_dpo_0426_0738\n","/content/FORGE/models/adapters_dpo_0426_0735\n","/content/FORGE/models/adapters_dpo_0426_0706\n","/content/FORGE/FORGE/models/adapters_dpo_0426_0029\n","/content/FORGE/FORGE/models/adapters_dpo_0426_0738\n","/content/FORGE/FORGE/models/adapters_dpo_0426_0735\n","/content/FORGE/FORGE/models/adapters_dpo_0426_0706\n","/content/FORGE/Meta-x-Scaler-Team404--Round2/models/adapters_dpo_0426_0029\n","/content/FORGE/Meta-x-Scaler-Team404--Round2/models/adapters_dpo_0426_0738\n","/content/FORGE/Meta-x-Scaler-Team404--Round2/models/adapters_dpo_0426_0735\n","/content/FORGE/Meta-x-Scaler-Team404--Round2/models/adapters_dpo_0426_0706\n"]}]},{"cell_type":"code","source":["from huggingface_hub import upload_folder\n","\n","upload_folder(\n"," folder_path=\"/content/FORGE/models/adapters_dpo_0426_0738\",\n"," repo_id=\"sanjay7676/forge-qwen-final\",\n"," repo_type=\"model\"\n",")\n","\n","print(\"Upload Complete!\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"K0f6wDoWONhE","executionInfo":{"status":"ok","timestamp":1777181229807,"user_tz":-330,"elapsed":396,"user":{"displayName":"Sanjay","userId":"00924777704336014398"}},"outputId":"27e5c538-2f67-48fb-9580-233b4a6aa9a0"},"execution_count":19,"outputs":[{"output_type":"stream","name":"stderr","text":["No files have been modified since last commit. Skipping to prevent empty commit.\n","WARNING:huggingface_hub.hf_api:No files have been modified since last commit. Skipping to prevent empty commit.\n"]},{"output_type":"stream","name":"stdout","text":["Upload Complete!\n"]}]},{"cell_type":"code","source":["!pip install -q unsloth transformers trl peft accelerate bitsandbytes datasets"],"metadata":{"id":"p0J6az7MPVcR","executionInfo":{"status":"ok","timestamp":1777181239577,"user_tz":-330,"elapsed":5694,"user":{"displayName":"Sanjay","userId":"00924777704336014398"}}},"execution_count":20,"outputs":[]},{"cell_type":"code","source":["from datasets import Dataset\n","\n","ds = Dataset.from_list([\n","{\"text\":\"def solve(arr): return sorted(arr)\"},\n","{\"text\":\"def solve(arr): return sorted(arr)\"},\n","{\"text\":\"def solve(arr): return sorted(arr)\"}\n","])"],"metadata":{"id":"XgMH-E8XPWzY","executionInfo":{"status":"ok","timestamp":1777181245146,"user_tz":-330,"elapsed":1409,"user":{"displayName":"Sanjay","userId":"00924777704336014398"}}},"execution_count":21,"outputs":[]},{"cell_type":"code","source":["from unsloth import FastLanguageModel\n","\n","model, tokenizer = FastLanguageModel.from_pretrained(\n"," model_name=\"Qwen/Qwen2.5-Coder-1.5B-Instruct\",\n"," max_seq_length=2048,\n"," load_in_4bit=True,\n",")\n","\n","model = FastLanguageModel.get_peft_model(\n"," model,\n"," r=16,\n"," target_modules=[\n"," \"q_proj\",\n"," \"k_proj\",\n"," \"v_proj\",\n"," \"o_proj\"\n"," ],\n"," lora_alpha=16,\n"," lora_dropout=0,\n"," bias=\"none\",\n"," use_gradient_checkpointing=\"unsloth\"\n",")\n","\n","print(\"LoRA attached successfully\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":635,"referenced_widgets":["ca5494f1b3084bab85ef887f21bccc51","d08f77b666784b83aa77e17f86af8656","4ed793d26b2447d1a9c4aab033ed9773","ffca9b8efb8847bbafd130dad84e131d","3d97e847eddf46bba4ee5a25127d15e7","c4245a180ffd4803870f295bdc29f6ec","8a2c9451b4cd4fb292f0613278f681bf","6e163a189fd54f298ddf667cc8d4a937","129dc98d72dc494b8d819a55d5afdbf2","fac8d2aa91be48bbbcec258585655e34","7f5ad3170e764226b1ab2a79d3b29410","250e21894e6844e886b16367278859e6","23562778a7214bf789709f4fef64e6dd","17f800ff6149427c84d9adf2fde17afa","e429ba0eb4484e038b1e9476c74c9593","d7e7a23985f74d4aa5a91a056e219a85","1e3fee4a5d2a483fa98a70cf860f29cb","e648506e758d48e7bbda32b1770f0cea","b57cc5ee6e92467ca956599891510265","4517c6b99e864ea485143970470d94c3","a5c33602d0e944008145caa935e745a3","5e56547a34e04188b2248cc0f148004e","f620a002f7d0422f9d9602ae9c7d59d6","e0073ab6dd46475d989981b2ebb194b5","8757d20acc584b7aadda1e654aaef7f9","db8f03591ff2485ea22ebfb445852cd4","8f879dd00986466bbbf87fdd88fac90d","1ba06d921b9143d3878b1b6651aba6bb","a6f8f4e7ca7b440388fb051658918704","2816a4ae33744d5eacab84d93dfc7469","b8dcfba8ff514712a9503c0dd88d9e28","a2cb4d1652bc4857b3f5de2f8ceeda83","8a11c905186f4c13acff2e543b39966f","f6b1427c5dbb4745be62338bfa2d95cb","b4b70754a1bb4bdaa72b3457ae118951","4e37be2415f64dcf9e86ba230e8f069c","e37d7e9193bb45389ddc3ce8e54214fe","b9d1249daa6b45b38d3870ad93eb8ba3","3c4c925f704446489e0e5bb8f0112864","96c3b0e7b86c49959e91eb65532fb06e","a307152854c3455dafb93b5202da9c8f","4d9dc508cd404b938673f80dc0d08ec9","bf1ba1363ed54d2d92ca0eaf1e7a1880","7c5849110b9244989beebc16d555fd9e","74c7f7501f514dbc8768054465c98c93","3f61b66a3315487e8205a2b002bdf6b2","1d79209293d548ae896cb88046a2ba7b","df437c0619944e00994f51160be0bfdc","f9bd2d85de5e4d9cbc4f3e9530449d3b","2be2b47c716845009ce2b07626fe5094","dac2221dba7c438683436e26ada2c0e4","1e53966181fa4048951ad9c4bbfd7f4c","76f210dccffa4a3d8668677524bd051c","c0e8833ef87c4dc9adea5e5924e7d489","5f25f863a576458388cd2df13840a4f1","9d4c2a1db816421fa2da0f8d8b738b1f","b14010a12bf54042a57d04924cd2ec89","bf92bbaa66ef4d34b0ba796f0b963051","d1e6a9777a2848b4b2f694f55d2990bc","729d51af848742f3878e932cf0b7859e","869d51bd1c954764900d3c31c8bc90f9","132b82e029f1432789bc6d16e6fb3bcb","d611def3fedd4bfdabbdcdfb56e0e93f","cf7bed477f0b4cedaf98ed62525b5a99","94bcbdb5dc0a44fda8ff314298b1d39a","3ce45bd0b6d24c2bbe754e3c70ab5ff3","ed44a5751b84406aa68237e2c20530fb","64c843c247794a93a9340374261da741","33f1bf42663d4005ac658130ec2a082d","fcc130ca4e024114951470c7bef3c8f7","4c6f6b364f5a4e1fb5f03b784fe737f8","638199ef6afb4474aa91da9e22aee407","d3261f957a0b4ba4900cddbc0a48abba","1310c5bf2e1f44fabe4712ee1c789a54","d95b5c5bd6c84d57b479658361843c9f","b8a4945bfb764f4892a4fa30a0cad77f","1b73a7877a0347319324e59e4ddbf7c9","5fab0c34d7e64d22ad800519c6a29323","16c5a42e98fd48b9b1905e6ea46c69c3","f00fc19b68334314a3033c92302a60aa","b26b91cb8edf439dba661733fe1a78d4","15096b81154044f4a3e5959e043c59a4","ff7d350b7c5f426495ecaeea5b732d27","8173ae17abac4624ba7380200aa479e5","222d0576b2b64745b5c1887f18b5cdd3","9b8e9071ede345fe89f5f6376db23b39","4195b68dae524dc4b561958c6627a884","b243d4e200ca44ec844896e3240a0df4","31b821b5502c437e9d1ef89074391900","df44f50a99ec43c498fa82cd96effe1a","b08e0dd684c144e8ab9eb33ac06a1571","dfc772e8e02840d7b137366d9d3ce2b3","2bfa97dec7bf45c4a9cc42e81a42080d","2853152aa53b46b4a4498a80aefe34b6","7ea14da3fee246a8ae8a1bbf27294518","eb53fcd8483c4f8db1b95c21819e4c2c","8ba2a9ca33bd427593bec6ca8498e0df","58484a4c4e7c44ddbf6cfc2ee05c981a","513cbcd9dc1f484dbd93e5e70ac4259e","39b019d8a3bc422d921b52f2a2c58c65","105ae560fdc041808ac7b15473d36565","4b2f1dd1f09345a9b579716defa50b77","a99e5f1a12c443c88ac3fd3611f719ef","149d924f60414098858022c2cdb9511b","1bcc6365ae834e8e97464451a7f34953","11244413c514462c98638708f38c9c02","2d1c97ea2e1b49eaac1315dd88b1823e","b9407c0fba6e4b91b5912e04cd8ecbee","4907caddb46745bf9f1aebc86e70ea03","6b745332c1a44fd69c405ba4cf095d72"]},"id":"NTU-jV0pQdK_","executionInfo":{"status":"ok","timestamp":1777181500337,"user_tz":-330,"elapsed":40318,"user":{"displayName":"Sanjay","userId":"00924777704336014398"}},"outputId":"48367402-a29f-4e6f-9adb-b7f9424880b5"},"execution_count":25,"outputs":[{"output_type":"stream","name":"stderr","text":["/tmp/ipykernel_31024/3755863122.py:1: UserWarning: WARNING: Unsloth should be imported before [trl, transformers, peft] to ensure all optimizations are applied. Your code may run slower or encounter memory issues without these optimizations.\n","\n","Please restructure your imports with 'import unsloth' at the top of your file.\n"," from unsloth import FastLanguageModel\n"]},{"output_type":"stream","name":"stdout","text":["🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n","🦥 Unsloth Zoo will now patch everything to make training faster!\n","==((====))== Unsloth 2026.4.8: Fast Qwen2 patching. Transformers: 5.5.0.\n"," \\\\ /| Tesla T4. Num GPUs = 1. Max memory: 14.563 GB. Platform: Linux.\n","O^O/ \\_/ \\ Torch: 2.10.0+cu128. CUDA: 7.5. CUDA Toolkit: 12.8. Triton: 3.6.0\n","\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.35. FA2 = False]\n"," \"-____-\" Free license: http://github.com/unslothai/unsloth\n","Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n"]},{"output_type":"display_data","data":{"text/plain":["model.safetensors: 0%| | 0.00/1.14G [00:00\n"]}]},{"cell_type":"code","source":["from datasets import Dataset\n","\n","ds = Dataset.from_list([\n","{\"text\":\"def solve(arr): return sorted(arr)\"},\n","{\"text\":\"def solve(arr): return sorted(arr)\"},\n","{\"text\":\"def solve(arr): return sorted(arr)\"}\n","])"],"metadata":{"id":"pnyljvFmRsRb","executionInfo":{"status":"ok","timestamp":1777181577256,"user_tz":-330,"elapsed":44,"user":{"displayName":"Sanjay","userId":"00924777704336014398"}}},"execution_count":27,"outputs":[]},{"cell_type":"code","source":["from trl import SFTTrainer, SFTConfig\n","\n","trainer = SFTTrainer(\n"," model=model,\n"," processing_class=tokenizer,\n"," train_dataset=ds,\n"," args=SFTConfig(\n"," output_dir=\"out\",\n"," max_steps=10,\n"," per_device_train_batch_size=1,\n"," logging_steps=1,\n"," report_to=\"none\",\n"," dataset_text_field=\"text\"\n"," )\n",")\n","\n","trainer.train()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":666,"referenced_widgets":["6b8f8e4965d0473da87cde00bfc15d8c","cd3ca15c806543399d8aa2cb449aaf21","5a538ab67507446db23d57c7ceb53015","e15b78c07ded4cd2ac68d9b822087f17","a107169bc77c4a90881a07e024e3f643","83e0fabb015b4674bbd1e70679f5c97d","4d1c209fc13e46899cc311db42914691","fadd5300a09f4988b6e6d5a6a429ad37","d801573328b2448aa89d268f8f348eb7","168f2712a3ba45f3a9eeccc3ea0ea843","ba9790acdc384b669925043bb3ac9bb0"]},"id":"EQr1Ttb2RtZ6","executionInfo":{"status":"ok","timestamp":1777181619915,"user_tz":-330,"elapsed":30893,"user":{"displayName":"Sanjay","userId":"00924777704336014398"}},"outputId":"fe2b8620-0447-4fe2-ea74-520f0cb7824f"},"execution_count":28,"outputs":[{"output_type":"stream","name":"stderr","text":["num_proc must be <= 3. Reducing num_proc to 3 for dataset of size 3.\n","WARNING:datasets.arrow_dataset:num_proc must be <= 3. Reducing num_proc to 3 for dataset of size 3.\n"]},{"output_type":"display_data","data":{"text/plain":["Unsloth: Tokenizing [\"text\"] (num_proc=3): 0%| | 0/3 [00:00"],"text/html":["\n","
\n"," \n"," \n"," [10/10 00:06, Epoch 5/5]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
14.315851
24.315851
34.268964
44.220582
54.170799
64.127370
74.090444
84.060283
94.037450
104.023013

"]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Unsloth: Restored added_tokens_decoder metadata in out/checkpoint-10/tokenizer_config.json.\n"]},{"output_type":"execute_result","data":{"text/plain":["TrainOutput(global_step=10, training_loss=4.163060569763184, metrics={'train_runtime': 18.7919, 'train_samples_per_second': 1.064, 'train_steps_per_second': 0.532, 'total_flos': 946583101440.0, 'train_loss': 4.163060569763184, 'epoch': 5.0})"]},"metadata":{},"execution_count":28}]},{"cell_type":"code","source":["model.save_pretrained(\"forge_real_adapter\")\n","tokenizer.save_pretrained(\"forge_real_adapter\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jqTKC8C3R74I","executionInfo":{"status":"ok","timestamp":1777181658212,"user_tz":-330,"elapsed":1348,"user":{"displayName":"Sanjay","userId":"00924777704336014398"}},"outputId":"99b9f23c-b2a0-470a-df5c-c1cf68652c85"},"execution_count":29,"outputs":[{"output_type":"stream","name":"stderr","text":["Unsloth: Restored added_tokens_decoder metadata in forge_real_adapter/tokenizer_config.json.\n"]},{"output_type":"execute_result","data":{"text/plain":["('forge_real_adapter/tokenizer_config.json',\n"," 'forge_real_adapter/chat_template.jinja',\n"," 'forge_real_adapter/tokenizer.json')"]},"metadata":{},"execution_count":29}]},{"cell_type":"code","source":["!ls forge_real_adapter"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YYl7zalESS5N","executionInfo":{"status":"ok","timestamp":1777181735315,"user_tz":-330,"elapsed":119,"user":{"displayName":"Sanjay","userId":"00924777704336014398"}},"outputId":"0c3d2d10-c30b-4bd3-8987-b0c2204a8966"},"execution_count":30,"outputs":[{"output_type":"stream","name":"stdout","text":["adapter_config.json\t chat_template.jinja\ttokenizer_config.json\n","adapter_model.safetensors README.md\t\ttokenizer.json\n"]}]},{"cell_type":"code","source":["from huggingface_hub import upload_folder\n","\n","upload_folder(\n"," folder_path=\"forge_real_adapter\",\n"," repo_id=\"sanjay7676/forge-qwen-final\",\n"," repo_type=\"model\"\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":214,"referenced_widgets":["4e63dd81f8a34b5d93b2f663a10007bf","5ccc84288aba4c12bc7d189986225997","71214d955af54488b722f94b0e794acc","86c67ed71cea4ab086f09b39fd84b9ea","d07e2a924b334b308c5bc3d351c659bb","76e892e1907d475899691102eb9696ae","25afd8253a3d4a6498677914865e0716","d000f9cc0d654a98bed6bd64b27bb8d1","1fc00f3a136e4408b6922303f139bc35","be6be9c60d2f4aa5a4801b28d903c60e","0e6ff106b66046a6881e42f2f0fbdca2","46a08b586ca54391bda028e8b3abc08d","5c7e4ba933bc432d89added35c626ee2","b7a48515899842338454580f089aabbf","8abf50bdd1a849868f3f4cf3efdc7c0e","013cb8c074f94ef6acf0bed2ac1a0e32","2358239ee5c64a86862bd0d1804c50ea","b53b447d15784820be1d0239d46fe9ad","a08c0e6851a644a9a79b2846422205db","756573df9e114e6582d9621c2327f39f","9ed0717a2a774e10982e86c9bb292331","39b101b57a4747a99e7c7e064d762f21","e41a6fdc9e7c431e8638dd60dfc093e3","d449ddeb2fd8417885dbc1629614f264","67d2f820c6024e079d7d0b40777df9c4","0810cf78f99846cdb288f2ab488a33bf","d43799f0f43443e28d09590e15509a36","9d4ddbd9387d413f9a726007cd449876","6a39907f25784c3ca2c61df3ca9f5acd","cdc1e59799744a76a5d1a7773b53d12c","da34311a3ac943b6a64b01f1d51c69f9","01e3543e90d94820858dc2ff078f0fc0","cd49fad6ac964dbab5d8c0b1505c97f4","ce57f81353884ff19f8e1eaed9e4e78a","f29a56a4c7584f15822cecf8036f0b09","38384b5ed7ef4382886ab5b07b522b94","39f6da22a1624157b1ffe1cfd4ad6cba","b941f6e7ce804c8a81aa6d64d982ea96","937012a1697e43649a580493fff84774","fcb131073e2d4b25a9965c42f3574e7b","76e71f0f5b014c048a05fbb98c434085","6828425765e74128a646ebb1dcdc162b","52974e42d3f342cca8358d1ad651971c","c567576411a64cb6abb0b284b8c6c270"]},"id":"lU1FND0WSUIZ","executionInfo":{"status":"ok","timestamp":1777181748926,"user_tz":-330,"elapsed":4986,"user":{"displayName":"Sanjay","userId":"00924777704336014398"}},"outputId":"16ddf81c-962b-42a9-b998-dc3ab2710150"},"execution_count":31,"outputs":[{"output_type":"display_data","data":{"text/plain":["Processing Files (0 / 0) : | | 0.00B / 0.00B "],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4e63dd81f8a34b5d93b2f663a10007bf"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["New Data Upload : | | 0.00B / 0.00B "],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"46a08b586ca54391bda028e8b3abc08d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[" ...al_adapter/tokenizer.json: 0%| | 27.8kB / 11.4MB "],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e41a6fdc9e7c431e8638dd60dfc093e3"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[" ...adapter_model.safetensors: 0%| | 25.9kB / 17.5MB "],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ce57f81353884ff19f8e1eaed9e4e78a"}},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["CommitInfo(commit_url='https://huggingface.co/sanjay7676/forge-qwen-final/commit/5f4daff8bffdf7b9073918d586329c6a2a2cfd00', commit_message='Upload folder using huggingface_hub', commit_description='', oid='5f4daff8bffdf7b9073918d586329c6a2a2cfd00', pr_url=None, repo_url=RepoUrl('https://huggingface.co/sanjay7676/forge-qwen-final', endpoint='https://huggingface.co', repo_type='model', repo_id='sanjay7676/forge-qwen-final'), pr_revision=None, pr_num=None)"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":31}]},{"cell_type":"code","source":["!git clone https://github.com/Sanjay767676/Meta-x-Scaler-Team404--Round2.git FORGE\n","%cd FORGE"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"csWQp4IRfqHQ","executionInfo":{"status":"ok","timestamp":1777185257968,"user_tz":-330,"elapsed":1033,"user":{"displayName":"Sanjay","userId":"00924777704336014398"}},"outputId":"6eca4d02-0b50-4b67-970a-9fe55d4999f3"},"execution_count":33,"outputs":[{"output_type":"stream","name":"stdout","text":["Cloning into 'FORGE'...\n","remote: Enumerating objects: 470, done.\u001b[K\n","remote: Counting objects: 100% (470/470), done.\u001b[K\n","remote: Compressing objects: 100% (324/324), done.\u001b[K\n","remote: Total 470 (delta 190), reused 402 (delta 122), pack-reused 0 (from 0)\u001b[K\n","Receiving objects: 100% (470/470), 476.12 KiB | 2.37 MiB/s, done.\n","Resolving deltas: 100% (190/190), done.\n","/content/FORGE/FORGE/FORGE\n"]}]},{"cell_type":"code","source":["# FORGE-v4 REAL DATA ONLY Colab Validator\n","# Uses ONLY real outputs from:\n","# 1. Your Hugging Face Space endpoints\n","# 2. Your local FORGE repo benchmark logs\n","# No hardcoded/fake metrics\n","\n","!pip -q install requests pandas matplotlib\n","\n","import requests\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import subprocess\n","import json\n","import os\n","import re\n","from IPython.display import display\n","\n","# ---------------------------------------------------\n","# CONFIG\n","# ---------------------------------------------------\n","SPACE_URL = \"https://sanjay7676-team404-forge.hf.space\"\n","EPISODES = 10\n","\n","# ---------------------------------------------------\n","# PART 1 — REAL SPACE CALLS\n","# ---------------------------------------------------\n","print(\"🌐 Testing Live FORGE Space (real endpoints)\\n\")\n","\n","def call_endpoint(method, url, payload=None):\n"," try:\n"," if method == \"GET\":\n"," r = requests.get(url, timeout=60)\n"," else:\n"," r = requests.post(url, json=payload, timeout=60)\n","\n"," print(f\"✅ {url}\")\n"," print(\"Status:\", r.status_code)\n"," print(r.text[:2000])\n"," print(\"-\" * 60)\n"," return r\n"," except Exception as e:\n"," print(f\"❌ {url}\")\n"," print(e)\n"," print(\"-\" * 60)\n"," return None\n","\n","# reset\n","call_endpoint(\"POST\", f\"{SPACE_URL}/reset\")\n","\n","# state\n","call_endpoint(\"GET\", f\"{SPACE_URL}/state\")\n","\n","# step\n","step_payload = {\n"," \"action\": {\n"," \"code\": \"def solve(arr): return sorted(arr)\"\n"," }\n","}\n","call_endpoint(\"POST\", f\"{SPACE_URL}/step\", step_payload)\n","\n","# ---------------------------------------------------\n","# PART 2 — RUN REAL LOCAL BENCHMARK\n","# ---------------------------------------------------\n","print(\"\\n🚀 Running real benchmark from your codebase...\\n\")\n","\n","if os.path.exists(\"train_colab.py\"):\n"," cmd = f\"python train_colab.py --compare --episodes {EPISODES}\"\n","\n"," process = subprocess.Popen(\n"," cmd.split(),\n"," stdout=subprocess.PIPE,\n"," stderr=subprocess.STDOUT,\n"," text=True\n"," )\n","\n"," raw_logs = []\n","\n"," for line in process.stdout:\n"," print(line, end=\"\")\n"," raw_logs.append(line)\n","\n"," process.wait()\n","\n","else:\n"," print(\"❌ train_colab.py not found.\")\n","\n","# ---------------------------------------------------\n","# PART 3 — USE REAL CSV LOGS ONLY\n","# ---------------------------------------------------\n","csv_path = \"logs/episodes.csv\"\n","\n","if os.path.exists(csv_path):\n"," print(\"\\n📂 Found real logs:\", csv_path)\n","\n"," df = pd.read_csv(csv_path)\n"," display(df.head())\n","\n"," # detect reward column\n"," reward_col = next((c for c in df.columns if \"reward\" in c.lower()), None)\n","\n"," if reward_col:\n"," plt.figure(figsize=(10,5))\n"," plt.plot(df[reward_col], marker=\"o\")\n"," plt.title(\"FORGE Real Reward Curve\")\n"," plt.xlabel(\"Episode\")\n"," plt.ylabel(\"Reward\")\n"," plt.grid(True)\n"," plt.show()\n","\n"," # detect pass column\n"," pass_col = next((c for c in df.columns if \"pass\" in c.lower()), None)\n","\n"," if pass_col:\n"," plt.figure(figsize=(10,5))\n"," plt.plot(df[pass_col], marker=\"o\")\n"," plt.title(\"FORGE Real Pass Rate\")\n"," plt.xlabel(\"Episode\")\n"," plt.ylabel(\"Pass Rate\")\n"," plt.grid(True)\n"," plt.show()\n","\n","else:\n"," print(\"\\n⚠️ logs/episodes.csv not found.\")\n"," print(\"No graph shown because fake data is not allowed.\")\n","\n","# ---------------------------------------------------\n","# PART 4 — REAL JSON REPORTS\n","# ---------------------------------------------------\n","for report in [\n"," \"outputs/final_report.json\",\n"," \"outputs/summary.json\",\n"," \"outputs/TRAINING_REPORT.md\"\n","]:\n"," if os.path.exists(report):\n"," print(f\"\\n📄 Found real file: {report}\")\n","\n","# ---------------------------------------------------\n","# FINAL\n","# ---------------------------------------------------\n","print(\"\\n🏆 ONLY REAL DATA DISPLAYED\")\n","print(\"✔ No fake numbers used\")\n","print(\"✔ Graphs shown only if real logs exist\")\n","print(\"✔ Space endpoints called live\")\n","print(\"✔ Benchmark data taken from your actual repo\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"tcznlGTufwT4","executionInfo":{"status":"error","timestamp":1777186182392,"user_tz":-330,"elapsed":516684,"user":{"displayName":"Sanjay","userId":"00924777704336014398"}},"outputId":"84c04f5b-369a-42bc-86ec-ae8fa36bfb34"},"execution_count":34,"outputs":[{"output_type":"stream","name":"stdout","text":["🌐 Testing Live FORGE Space (real endpoints)\n","\n","✅ https://sanjay7676-team404-forge.hf.space/reset\n","Status: 405\n","{\"type\":\"error\",\"error\":{\"message\":\"Method Not Allowed\"}}\n","------------------------------------------------------------\n","✅ https://sanjay7676-team404-forge.hf.space/state\n","Status: 200\n","\n","\n","\t\n","\t\t\n","\t\t\n","\t\t\n","\t\t\n","\t\t\n","\t\t\n","\t\t\n","\t\t\n","\t\t\n","\t\t\n","\t\t\n","\t\t\n","\t\t\n","\t\t\n","\t\t\n","\n","\t\t\n","\t\t\n","\t\t\n","\t\t\n","\t\t\n","\t\t\n","\t\t\n","\t\t\n","\t\t= 2.11.0 (found 2.10.0+cu128).\n","`torch_dtype` is deprecated! Use `dtype` instead!\n","\n","============================================================\n"," FORGE-v4: Colab Training Entrypoint\n","============================================================\n","\n"," Starting comparison mode (10 episodes)...\n"," Episode 1/20 | Pass Rate: 100.00% | Tier: 2 | Reward: +13.0\n"," [DPO] Exported 3 preference pairs from episode 1 to data/dpo_dataset.jsonl\n"," Episode 2/20 | Pass Rate: 100.00% | Tier: 3 | Reward: +13.0\n"," [DPO] Exported 3 preference pairs from episode 2 to data/dpo_dataset.jsonl\n"," Episode 3/20 | Pass Rate: 100.00% | Tier: 4 | Reward: +13.0\n"," [DPO] Exported 3 preference pairs from episode 3 to data/dpo_dataset.jsonl\n"," Episode 4/20 | Pass Rate: 60.00% | Tier: 4 | Reward: +2.8\n"," [DPO] Exported 3 preference pairs from episode 4 to data/dpo_dataset.jsonl\n"," Episode 5/20 | Pass Rate: 40.00% | Tier: 4 | Reward: -1.0\n"," [DPO] Exported 3 preference pairs from episode 5 to data/dpo_dataset.jsonl\n"," Episode 6/20 | Pass Rate: 40.00% | Tier: 4 | Reward: -1.0\n"," [DPO] Exported 3 preference pairs from episode 6 to data/dpo_dataset.jsonl\n"," Episode 7/20 | Pass Rate: 100.00% | Tier: 4 | Reward: +13.0\n"," [DPO] Exported 3 preference pairs from episode 7 to data/dpo_dataset.jsonl\n"," Episode 8/20 | Pass Rate: 100.00% | Tier: 4 | Reward: +13.0\n"," [DPO] Exported 3 preference pairs from episode 8 to data/dpo_dataset.jsonl\n"," Episode 9/20 | Pass Rate: 100.00% | Tier: 4 | Reward: +13.0\n"," [DPO] Exported 3 preference pairs from episode 9 to data/dpo_dataset.jsonl\n"," Episode 10/20 | Pass Rate: 100.00% | Tier: 4 | Reward: +13.0\n"," [DPO] Exported 3 preference pairs from episode 10 to data/dpo_dataset.jsonl\n"," Episode 11/20 | Pass Rate: 100.00% | Tier: 4 | Reward: +13.0\n"," [DPO] Exported 3 preference pairs from episode 11 to data/dpo_dataset.jsonl\n"," Episode 12/20 | Pass Rate: 100.00% | Tier: 4 | Reward: +13.0\n"," [DPO] Exported 3 preference pairs from episode 12 to data/dpo_dataset.jsonl\n"," Episode 13/20 | Pass Rate: 100.00% | Tier: 4 | Reward: +13.0\n"," [DPO] Exported 3 preference pairs from episode 13 to data/dpo_dataset.jsonl\n"," Episode 14/20 | Pass Rate: 100.00% | Tier: 4 | Reward: +13.0\n"," [DPO] Exported 3 preference pairs from episode 14 to data/dpo_dataset.jsonl\n"," Episode 15/20 | Pass Rate: 100.00% | Tier: 4 | Reward: +13.0\n"," [DPO] Exported 3 preference pairs from episode 15 to data/dpo_dataset.jsonl\n"," Episode 16/20 | Pass Rate: 100.00% | Tier: 4 | Reward: +13.0\n"," [DPO] Exported 3 preference pairs from episode 16 to data/dpo_dataset.jsonl\n"," Episode 17/20 | Pass Rate: 100.00% | Tier: 4 | Reward: +13.0\n"," [DPO] Exported 3 preference pairs from episode 17 to data/dpo_dataset.jsonl\n"," Episode 18/20 | Pass Rate: 100.00% | Tier: 4 | Reward: +13.0\n"," [DPO] Exported 3 preference pairs from episode 18 to data/dpo_dataset.jsonl\n"," Episode 19/20 | Pass Rate: 100.00% | Tier: 4 | Reward: +13.0\n"," [DPO] Exported 3 preference pairs from episode 19 to data/dpo_dataset.jsonl\n"," Episode 20/20 | Pass Rate: 100.00% | Tier: 4 | Reward: +13.0\n"," [DPO] Exported 3 preference pairs from episode 20 to data/dpo_dataset.jsonl\n","\n","Loading weights: 0%| | 0/338 [00:00\u001b[0;34m()\u001b[0m\n\u001b[1;32m 76\u001b[0m \u001b[0mraw_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 77\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 78\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mprocess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstdout\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 79\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 80\u001b[0m \u001b[0mraw_logs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}]}]}