Instructions to use Eeshan082004/quotedesk-student with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Eeshan082004/quotedesk-student with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-3-270m-it") model = PeftModel.from_pretrained(base_model, "Eeshan082004/quotedesk-student") - Notebooks
- Google Colab
- Kaggle
Download run_config.json from Eeshan082004/quotedesk-student: direct link, hf CLI and curl.
- Browser
- Download file 2.89 kB
-
https://huggingface.co/Eeshan082004/quotedesk-student/resolve/main/run_config.json
- Command line
-
hf download hf://Eeshan082004/quotedesk-student/run_config.json
-
curl -L -o run_config.json https://huggingface.co/Eeshan082004/quotedesk-student/resolve/main/run_config.json
2.89 kB
| { | |
| "version_tag": "v1", | |
| "base_model": "unsloth/gemma-3-270m-it", | |
| "epochs": 3, | |
| "learning_rate": 0.0002, | |
| "lora_r": 16, | |
| "lora_alpha": 32, | |
| "lora_dropout": 0.0, | |
| "batch_size": 8, | |
| "grad_accum": 2, | |
| "warmup_ratio": 0.05, | |
| "seed": 3407, | |
| "max_seq_len": 640, | |
| "max_new_tokens": 384, | |
| "train_examples": 1334, | |
| "corrections": 0, | |
| "train_seconds": 564.4, | |
| "gpu": "Tesla T4", | |
| "platform": "kaggle", | |
| "dataset_manifest": { | |
| "version_tag": "v1", | |
| "created": "2026-09-30T21:42:39", | |
| "git_sha": null, | |
| "counts": { | |
| "train.jsonl": 1334, | |
| "val.jsonl": 150, | |
| "exam.jsonl": 150, | |
| "exam_hard.jsonl": 30, | |
| "fewshot.jsonl": 3 | |
| }, | |
| "corrections": 0, | |
| "sha256": { | |
| "catalog.json": "b354cf4e5b81f6be80c5071c0961b0ee79c9c8f9cec86bd4e0102f819e22c021", | |
| "customers.json": "b650b859b37bd2c3c33c703aaea9e227cf186558789e7e88f5fd53b7352a1bef", | |
| "dataset-metadata.json": "b483500aa344633dc1614e132d976cee59073c4c91003f0977f3c71ecf19a875", | |
| "equipment.json": "91e341753fee46d62b9444747d177a670b308999743de1fddd6079a596c56e2c", | |
| "evallib/__init__.py": "ec38b6ac328f58d03f3425148201628598e7521691faf7d34a1c326e7d7aa8f8", | |
| "evallib/metrics.py": "7bef5b64f15ba6fc0ba877daa6d57294da373061543c984023af9c59edce5a37", | |
| "evallib/normalize.py": "0c663091219f7a14baa37307863c8ea9e066b87e46f045abc38e3176f13069a3", | |
| "evallib/pricing.json": "782918cf5cdbf2b69d85873028e0bfa9bbafc89cda6828cb9c933ca3bdf96f5f", | |
| "evallib/prompt.py": "521bdb70ba03d77436c32b82edf3a7a8f6f44b0ccadade37416ec3db786ffdad", | |
| "evallib/runner.py": "3992d9971ab4d21c707964650fb5d09e98123a4adabb329160a8d8eacebec744", | |
| "evallib/schema.py": "eec543089b10c394dec74de147b2b238337657c037f69e72caa48244a482e27e", | |
| "evallib/system_prompt.txt": "fc0d3583b99fc4dfe3ec3b42f5fe3bfbabbccc23f9aec7edc3771eea5adbbc03", | |
| "evallib/teacher_rules.txt": "20d48a4ea874fa5c3f0c5eb69e81f4796aaa6dae40ffc89bb30eeca3b232633d", | |
| "exam.jsonl": "dc1100cffd0bbdfd8089f0841f455f753eae50ad6fcd11ba2ad1dca830287584", | |
| "exam_hard.jsonl": "bea23f278853cf5b6803df77e2b87cd62175333f8dec3235c779d4c9d4852bd0", | |
| "fewshot.jsonl": "f4d148125fe418cd0735e20f326d3df4a2319e8084abebd4eee371c6bc34db91", | |
| "orders.json": "37dea4e9b8026315814840acf0cd4557d921c4e5b30bf856def22c01e99a285c", | |
| "train.jsonl": "63578645ec0a3d53de85255a888d8855710b218e31a4bf095e568e4473eda33f", | |
| "val.jsonl": "e723c6a8ac3fb82f1a6ce411274f8e0bb7c1be2f4fa3e2b52f8831bddf81c060" | |
| } | |
| }, | |
| "versions": { | |
| "torch": "2.10.0+cu128", | |
| "transformers": "4.56.2", | |
| "trl": "0.22.2", | |
| "peft": "0.19.1", | |
| "unsloth": "2026.9.12", | |
| "unsloth_zoo": "2026.9.8", | |
| "datasets": "4.3.0", | |
| "accelerate": "1.13.0" | |
| }, | |
| "final_train_loss": 0.12306493408386669, | |
| "eval_loss_per_epoch": [ | |
| 0.086934395134449, | |
| 0.03893920034170151, | |
| 0.030055643990635872 | |
| ] | |
| } |