End of training

README.md

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+---
+base_model: HuggingFaceTB/SmolLM-135M
+datasets:
+- wikimedia/wikipedia
+library_name: Distily
+license: creativeml-openrail-m
+tags:
+- generated_from_trainer
+- Distily
+base_model_relation: finetune
+model-index:
+- name: distily_distsmollm_long
+  results: []
+---
+
+
+# Summary
+
+Distilled with [Distily](https://github.com/lapp0/distily) library
+using teacher model [HuggingFaceTB/SmolLM-135M](https://huggingface.co/HuggingFaceTB/SmolLM-135M)
+on dataset [wikimedia/wikipedia](https://huggingface.co/datasets/wikimedia/wikipedia).
+
+<!-- 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.
+
+# Model description
+
+More information needed
+
+# Intended uses & limitations
+
+More information needed
+-->
+
+# Model Architecture:
+- **Architecture**: `LlamaForCausalLM`
+- **Total Parameters**: 81,413,568
+- **Data Type (dtype)**: torch.float32
+- **Model Size**: 0.30 GB
+
+<details>
+<summary>Student Model Details</summary>
+
+```
+LlamaForCausalLM(
+  (model): LlamaModel(
+    (embed_tokens): Embedding(49152, 576)
+    (layers): ModuleList(
+      (0-14): 15 x LlamaDecoderLayer(
+        (self_attn): LlamaSdpaAttention(
+          (q_proj): Linear(in_features=576, out_features=576, bias=False)
+          (k_proj): Linear(in_features=576, out_features=192, bias=False)
+          (v_proj): Linear(in_features=576, out_features=192, bias=False)
+          (o_proj): Linear(in_features=576, out_features=576, bias=False)
+          (rotary_emb): LlamaRotaryEmbedding()
+        )
+        (mlp): LigerSwiGLUMLP(
+          (gate_proj): Linear(in_features=576, out_features=1536, bias=False)
+          (up_proj): Linear(in_features=576, out_features=1536, bias=False)
+          (down_proj): Linear(in_features=1536, out_features=576, bias=False)
+        )
+        (input_layernorm): LigerRMSNorm((576,), eps=1e-05, offset=0.0)
+        (post_attention_layernorm): LigerRMSNorm((576,), eps=1e-05, offset=0.0)
+      )
+    )
+    (norm): LigerRMSNorm((576,), eps=1e-05, offset=0.0)
+    (rotary_emb): LlamaRotaryEmbedding()
+  )
+  (lm_head): Linear(in_features=576, out_features=49152, bias=False)
+)
+```
+
+</details>
+<br/>
+
+
+
+# Resource Usage
+
+- Max Train VRAM Use: 13.4793 GB
+- Available VRAM: 23.6497 GB
+- GPUs: 
+  - 1x NVIDIA GeForce RTX 4090
+- CPUs: 48
+- CPU Memory: 251.5386 GB
+- CPU Memory Bandwidth: 1200 GB/s
+
+# Distillation (Teacher -> Student) Architecture Difference:
+
+- **Architecture**: `LlamaForCausalLM` -> `LlamaForCausalLM`
+- **Total Parameters**: 134,515,008 -> 81,413,568
+- **Data Type (dtype)**: torch.float32 -> torch.float32
+- **Model Size**: 0.25 GB -> 0.30 GB
+
+<details>
+<summary>Module Diff Details</summary>
+
+```diff
+--- teacher model modules
++++ student model modules
+@@ -2,7 +2,7 @@
+   (model): LlamaModel(
+     (embed_tokens): Embedding(49152, 576)
+     (layers): ModuleList(
+-      (0-29): 30 x LlamaDecoderLayer(
++      (0-14): 15 x LlamaDecoderLayer(
+         (self_attn): LlamaSdpaAttention(
+           (q_proj): Linear(in_features=576, out_features=576, bias=False)
+           (k_proj): Linear(in_features=576, out_features=192, bias=False)
+@@ -10,17 +10,16 @@
+           (o_proj): Linear(in_features=576, out_features=576, bias=False)
+           (rotary_emb): LlamaRotaryEmbedding()
+         )
+-        (mlp): LlamaMLP(
++        (mlp): LigerSwiGLUMLP(
+           (gate_proj): Linear(in_features=576, out_features=1536, bias=False)
+           (up_proj): Linear(in_features=576, out_features=1536, bias=False)
+           (down_proj): Linear(in_features=1536, out_features=576, bias=False)
+-          (act_fn): SiLU()
+         )
+-        (input_layernorm): LlamaRMSNorm((576,), eps=1e-05)
+-        (post_attention_layernorm): LlamaRMSNorm((576,), eps=1e-05)
++        (input_layernorm): LigerRMSNorm((576,), eps=1e-05, offset=0.0)
++        (post_attention_layernorm): LigerRMSNorm((576,), eps=1e-05, offset=0.0)
+       )
+     )
+-    (norm): LlamaRMSNorm((576,), eps=1e-05)
++    (norm): LigerRMSNorm((576,), eps=1e-05, offset=0.0)
+     (rotary_emb): LlamaRotaryEmbedding()
+   )
+   (lm_head): Linear(in_features=576, out_features=49152, bias=False)
+
+```
+
+</details>
+<br/>
+
+# Train Dataset
+Trained on 706,573,563 tokens from the [wikimedia/wikipedia](https://huggingface.co/datasets/wikimedia/wikipedia) dataset.
+
+- Num Samples: `998,000`
+- Subset: `20231101.en`
+- Split: `train`
+
+
+# Training Objective
+
+```
+DistillationObjective(
+    logits_loss_component=LossComponent(
+        weight=1,
+        loss_fn='kl'
+    ),
+    hs_loss_component=LossComponent(
+        weight=0
+    ),
+    attn_loss_component=LossComponent(
+        weight=0
+    )
+)
+```
+
+# Hyperparameters
+The following hyperparameters were used during training:
+
+<details>
+<summary>Expand</summary>
+
+- learning_rate: `0.0002`
+- train_batch_size: `4`
+- eval_batch_size: `2`
+- seed: `42`
+- gradient_accumulation_steps: `2`
+- total_train_batch_size: `8`
+- optimizer: `Adam with betas=(0.9,0.999) and epsilon=1e-08`
+- lr_scheduler_type: `polynomial`
+- num_epochs: `1.0`
+- distillation_objective: `DistillationObjective(
+    logits_loss_component=LossComponent(
+        weight=1,
+        loss_fn='kl'
+    ),
+    hs_loss_component=LossComponent(
+        weight=0
+    ),
+    attn_loss_component=LossComponent(
+        weight=0
+    )
+)`
+- lr_scheduler: `<torch.optim.lr_scheduler.LambdaLR object at 0x718c02862f80>`
+- student_model_name_or_path: `None`
+- student_config_name_or_path: `None`
+- student_model_config: `{'num_hidden_layers': 15}`
+- reinitialize_weights: `None`
+- copy_teacher_modules: `[('lm_head', False)]`
+- student_model_as_bitnet: `False`
+- student_use_liger_kernel: `True`
+- teacher_model_name_or_path: `HuggingFaceTB/SmolLM-135M`
+- teacher_load_in_8bit: `False`
+- teacher_load_in_4bit: `False`
+- dataset_uri: `wikimedia/wikipedia`
+- dataset_subset: `20231101.en`
+- dataset_split: `train`
+- dataset_column_name: `text`
+- dataset_sample_size: `1000000`
+- dataset_test_size: `0.002`
+- dataset_shuffle: `False`
+- dataset_shuffle_seed: `42`
+- dataset_trust_remote_code: `False`
+- weight_decay: `0.0`
+- max_grad_norm: `1.0`
+- warmup_ratio: `0.0`
+- warmup_steps: `0`
+- gradient_checkpointing: `True`
+
+</details>
+<br/>
+
+
+# Framework Versions
+- Distily 0.5.0
+- Transformers 4.44.2
+- Pytorch 2.4.1+cu121
+- Datasets 3.0.0

generation_config.json

@@ -0,0 +1,7 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "eos_token_id": 0,
+  "transformers_version": "4.44.2",
+  "use_cache": false
+}

logs/gradient_accumulation_steps=2, per_device_train_batch_size=4, run_name=baseline/events.out.tfevents.1726274303.7eb020c3c65f

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