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Model: rbelanec/train_qnli_42_1779286680
Source: Original Platform

.gitattributes

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README.md

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+---
+library_name: transformers
+license: llama3.2
+base_model: meta-llama/Llama-3.2-1B-Instruct
+tags:
+- peft-factory
+- freeze
+- llama-factory
+- generated_from_trainer
+model-index:
+- name: train_qnli_42_1779286680
+  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. -->
+
+# train_qnli_42_1779286680
+
+This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the qnli dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0523
+- Num Input Tokens Seen: 11312256
+
+## 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-06
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 1
+
+### Training results
+
+| Training Loss | Epoch  | Step  | Validation Loss | Input Tokens Seen |
+|:-------------:|:------:|:-----:|:---------------:|:-----------------:|
+| 0.0929        | 0.0501 | 590   | 0.0807          | 571072            |
+| 0.1054        | 0.1001 | 1180  | 0.0708          | 1136384           |
+| 0.1201        | 0.1502 | 1770  | 0.0836          | 1703808           |
+| 0.1436        | 0.2003 | 2360  | 0.0888          | 2266496           |
+| 0.0749        | 0.2503 | 2950  | 0.0761          | 2827328           |
+| 0.0141        | 0.3004 | 3540  | 0.0862          | 3399808           |
+| 0.0051        | 0.3505 | 4130  | 0.0710          | 3963584           |
+| 0.0782        | 0.4005 | 4720  | 0.0551          | 4530304           |
+| 0.05          | 0.4506 | 5310  | 0.0634          | 5095424           |
+| 0.0293        | 0.5007 | 5900  | 0.0550          | 5660352           |
+| 0.0534        | 0.5507 | 6490  | 0.0558          | 6232896           |
+| 0.0467        | 0.6008 | 7080  | 0.0598          | 6801984           |
+| 0.0404        | 0.6509 | 7670  | 0.0556          | 7363968           |
+| 0.0633        | 0.7010 | 8260  | 0.0546          | 7924800           |
+| 0.0632        | 0.7510 | 8850  | 0.0540          | 8494720           |
+| 0.1023        | 0.8011 | 9440  | 0.0547          | 9066048           |
+| 0.0665        | 0.8512 | 10030 | 0.0526          | 9634624           |
+| 0.0855        | 0.9012 | 10620 | 0.0523          | 10199424          |
+| 0.004         | 0.9513 | 11210 | 0.0523          | 10764096          |
+
+
+### Framework versions
+
+- Transformers 4.51.3
+- Pytorch 2.10.0+cu128
+- Datasets 4.0.0
+- Tokenizers 0.21.4

all_results.json

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+{
+    "epoch": 1.0,
+    "eval_loss": 0.05228454992175102,
+    "eval_runtime": 15.7327,
+    "eval_samples_per_second": 665.81,
+    "eval_steps_per_second": 83.266,
+    "num_input_tokens_seen": 11312256,
+    "total_flos": 6.605086766609203e+16,
+    "train_loss": 0.0722960903008882,
+    "train_runtime": 1744.9763,
+    "train_samples_per_second": 54.023,
+    "train_steps_per_second": 6.753
+}

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 16,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 32.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.51.3",
+  "use_cache": false,
+  "vocab_size": 128256
+}

eval_results.json

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+{
+    "epoch": 1.0,
+    "eval_loss": 0.05228454992175102,
+    "eval_runtime": 15.7327,
+    "eval_samples_per_second": 665.81,
+    "eval_steps_per_second": 83.266,
+    "num_input_tokens_seen": 11312256
+}

generation_config.json

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+{
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.51.3"
+}

model.safetensors

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special_tokens_map.json

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+{
+  "additional_special_tokens": [
+    {
+      "content": "<|eom_id|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    }
+  ],
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|eot_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<|eot_id|>"
+}

train.yaml

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+seed: 42
+
+### model
+model_name_or_path: meta-llama/Llama-3.2-1B-Instruct
+trust_remote_code: true
+flash_attn: auto
+use_cache: false
+
+### method
+# Full fine-tune of every decoder block, but with the (tied) embeddings frozen.
+# `finetuning_type: freeze` only trains modules whose name matches a trainable layer;
+# embed_tokens / lm_head / final model.norm are "extra" modules and stay frozen unless
+# listed in freeze_extra_modules. Setting freeze_trainable_layers = num_hidden_layers (16
+# for Llama-3.2-1B) makes ALL decoder blocks trainable, so this == "full FT minus
+# embeddings". Because tie_word_embeddings=true, freezing embed_tokens also freezes lm_head.
+# This is lever B of the embedding-amplification fix (see figures/amplification/README.md).
+stage: sft
+do_train: true
+finetuning_type: freeze
+freeze_trainable_layers: 16
+freeze_trainable_modules: all
+# freeze_extra_modules: left unset -> embed_tokens, lm_head (tied), final norm stay frozen
+
+### dataset
+dataset: qnli
+template: llama3
+cutoff_len: 2048
+overwrite_cache: true
+preprocessing_num_workers: 4
+dataloader_num_workers: 4
+packing: false
+
+### output
+output_dir: saves_bts_preliminary/freeze/llama-3.2-1b-instruct/train_qnli_42_1779286680
+logging_steps: 5
+save_steps: 0.05
+overwrite_output_dir: true
+save_only_model: false
+plot_loss: true
+include_num_input_tokens_seen: true
+push_to_hub: true
+push_to_hub_organization: rbelanec
+load_best_model_at_end: true
+save_total_limit: 1
+
+### train
+per_device_train_batch_size: 8
+learning_rate: 2.0e-6
+num_train_epochs: 1
+weight_decay: 1.0e-2
+lr_scheduler_type: cosine
+bf16: true
+ddp_timeout: 180000000
+resume_from_checkpoint: null
+warmup_ratio: 0.1
+optim: adamw_torch
+report_to:
+- wandb
+run_name: freeze_llama-3.2-1b-instruct_train_qnli_42_1779286680
+
+### eval
+per_device_eval_batch_size: 8
+eval_strategy: steps
+eval_steps: 0.05
+val_size: 0.1

train_results.json

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+{
+    "epoch": 1.0,
+    "num_input_tokens_seen": 11312256,
+    "total_flos": 6.605086766609203e+16,
+    "train_loss": 0.0722960903008882,
+    "train_runtime": 1744.9763,
+    "train_samples_per_second": 54.023,
+    "train_steps_per_second": 6.753
+}

training_args.bin

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