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Model: adityabanerjee13/qwen2.5-0.5b-sft-IT
Source: Original Platform

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: adityabanerjee13/qwen2.5-0.5b-cpt-mix-1to2
+tags:
+- axolotl
+- generated_from_trainer
+datasets:
+- adityabanerjee13/indic-sft-mini-train
+- adityabanerjee13/tulu-sft-mini-train
+model-index:
+- name: qwen2.5-0.5b-sft-IT
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
+<details><summary>See axolotl config</summary>
+
+axolotl version: `0.19.0.dev0`
+```yaml
+# ==============================================================================
+# Axolotl CPT config — Qwen2.5-0.5B, full-parameter, single GPU.
+# Data mix RATIO EXPERIMENT (character-level exact):
+#
+#   RUN 2 of 3 — fineweb : indic = 1 : 2   (FineWeb is HALF the Indic size)
+#   FineWeb web-crawl chars == Indic train chars / 2.
+#
+# Indic train  : adityabanerjee13/indic-cpt-mini-train   (7,907,882 chars)
+# FineWeb train: adityabanerjee13/fineweb-cpt-half       (3,953,941 chars)
+# Validation   : adityabanerjee13/indic-cpt-mini-val     (held-out 1% Indic)
+#
+# The datasets are pre-sized to exact character counts on the Hub, so loading
+# each one whole gives the exact 1:2 ratio — no slicing needed.
+#
+# Usage:
+#   python train.py --config qwen2.5_0.5b_cpt_mix_1to2.yml
+# ==============================================================================
+
+base_model: adityabanerjee13/qwen2.5-0.5b-cpt-mix-1to2
+model_type: AutoModelForCausalLM
+tokenizer_type: AutoTokenizer
+trust_remote_code: false
+
+adapter:
+load_in_8bit: false
+load_in_4bit: false
+
+# multi_eval_plugin splits test_datasets back into per-source eval sets so
+# this run logs eval_indic_cpt_mini_val_loss and eval_fineweb_cpt_val_loss
+# separately (instead of one merged eval_loss) at every eval step, incl. to
+# wandb. Requires this folder on PYTHONPATH — launch via
+# `python train.py --config <this file>`.
+plugins:
+  - multi_eval_plugin.MultiEvalPlugin
+
+datasets:
+  - path: adityabanerjee13/indic-sft-mini-train
+    type: chat_template
+    field_messages: messages
+    split: train
+  - path: adityabanerjee13/tulu-sft-mini-train
+    type: chat_template
+    field_messages: messages
+    split: train
+
+test_datasets:
+  - path: adityabanerjee13/indic-sft-mini-val
+    type: chat_template
+    field_messages: messages
+    split: validation
+  - path: adityabanerjee13/tulu-sft-mini-val
+    type: chat_template
+    field_messages: messages
+    split: validation
+
+train_on_inputs: false   
+
+chat_template: tokenizer_default
+
+dataset_prepared_path: ./last_run_prepared_1to2
+dataset_num_proc: 1          # single-process tokenize: avoids fork deadlock
+val_set_size: 0
+output_dir: ./outputs/qwen2.5-0.5b-sft-IT
+
+# --- Sequence packing -----------------------------------------------------
+sequence_len: 4096
+sample_packing: true
+pad_to_sequence_len: true
+eval_sample_packing: false
+
+# --- Optimization ----------------------------------------------------------
+gradient_accumulation_steps: 8
+micro_batch_size: 4
+num_epochs: 3
+optimizer: adamw_torch_fused
+lr_scheduler: cosine
+learning_rate: 2e-5
+warmup_ratio: 0.03
+weight_decay: 0.01
+max_grad_norm: 1.0
+
+train_on_inputs: true
+group_by_length: false
+
+# --- Precision / memory ---------------------------------------------------
+bf16: auto
+fp16:
+tf32: true
+gradient_checkpointing: true
+flash_attention: true
+
+# --- Logging / checkpoints ------------------------------------------------
+logging_steps: 10
+save_strategy: steps
+save_steps: 500
+save_total_limit: 30
+save_only_model: true   # save weights only — no optimizer/scheduler state
+                        # (checkpoints ~1/3 the size; can't resume training)
+evals_per_epoch: 4
+
+wandb_project: indic-sft
+wandb_entity: models-na9841
+wandb_name: qwen2.5-0.5b-sft-IT
+wandb_log_model: "false"
+
+hub_model_id: adityabanerjee13/qwen2.5-0.5b-sft-IT
+hub_strategy: all_checkpoints
+
+special_tokens:
+
+```
+
+</details><br>
+
+# qwen2.5-0.5b-sft-IT
+
+This model is a fine-tuned version of [adityabanerjee13/qwen2.5-0.5b-cpt-mix-1to2](https://huggingface.co/adityabanerjee13/qwen2.5-0.5b-cpt-mix-1to2) on the adityabanerjee13/indic-sft-mini-train and the adityabanerjee13/tulu-sft-mini-train datasets.
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 4
+- eval_batch_size: 4
+- seed: 42
+- gradient_accumulation_steps: 8
+- total_train_batch_size: 32
+- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 17
+- training_steps: 591
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 5.14.1
+- Pytorch 2.12.0+cu130
+- Datasets 4.8.4
+- Tokenizers 0.22.2