README.md

---
base_model: HuggingFaceTB/SmolLM-135M
datasets:
- HuggingFaceFW/fineweb
library_name: Distily
license: creativeml-openrail-m
tags:
- generated_from_trainer
- Distily
base_model_relation: finetune
model-index:
- name: distily_smollm_dataset_sweep
  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 [HuggingFaceFW/fineweb](https://huggingface.co/datasets/HuggingFaceFW/fineweb).

<!-- 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/>

# Benchmark Metrics Comparison

- student 0: `dataset_max_seq_length=1024, dataset_sample_size=1000000, dataset_subset=20231101.en, dataset_uri=wikimedia_wikipedia, per_device_train_batch_size=8`
- student 1: `dataset_max_seq_length=1024, dataset_sample_size=1000000, dataset_subset=None, dataset_uri=distily_filtered_redpajama_en, per_device_train_batch_size=8`
- student 2: `dataset_max_seq_length=1024, dataset_sample_size=1000000, dataset_subset=sample-10BT, dataset_uri=HuggingFaceFW_fineweb-edu, per_device_train_batch_size=8`
- student 3: `dataset_max_seq_length=1024, dataset_sample_size=1000000, dataset_subset=sample-10BT, dataset_uri=HuggingFaceFW_fineweb, per_device_train_batch_size=8`
- student 4: `dataset_max_seq_length=1024, dataset_sample_size=1000000, dataset_subset=sample-10BT, dataset_uri=HuggingFaceFW_fineweb, learning_rate=6e-05, per_device_train_batch_size=8`

| Metric | teacher | student 0 | student 1 | student 2 | student 3 | student 4 |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| tinyArc.acc_norm,none | 0.37 | **0.303** | 0.295 | 0.302 | 0.26 | 0.269 |
| tinyGSM8k.exact_match,flexible-extract | 0.006 | 0.029 | **0.03** | 0.025 | 0.006 | 0.006 |
| tinyGSM8k.exact_match,strict-match | 0.006 | **0.006** | **0.006** | **0.006** | **0.006** | **0.006** |
| tinyHellaswag.acc_norm,none | 0.452 | **0.341** | 0.281 | 0.327 | 0.3 | 0.303 |
| tinyMMLU.acc_norm,none | 0.341 | 0.276 | 0.281 | **0.31** | 0.286 | 0.279 |
| tinyTruthfulQA.acc,none | 0.38 | **0.463** | 0.447 | 0.423 | 0.419 | 0.421 |
| tinyWinogrande.acc_norm,none | 0.509 | 0.466 | 0.436 | 0.46 | **0.492** | 0.473 |

# Resource Usage

- Max Train VRAM Use: 13.1269 GB
- Available VRAM: 23.4329 GB
- GPUs: 
  - 1x NVIDIA GeForce RTX 4090
- CPUs: 64
- CPU Memory: 251.7299 GB
- CPU Memory Bandwidth: 1600 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 501,158,307 tokens from the [HuggingFaceFW/fineweb](https://huggingface.co/datasets/HuggingFaceFW/fineweb) dataset.

- Num Samples: `998,000`
- Subset: `sample-10BT`
- 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: `6e-05`
- train_batch_size: `8`
- eval_batch_size: `4`
- seed: `42`
- optimizer: `Adam with betas=(0.9,0.999) and epsilon=1e-08`
- lr_scheduler_type: `polynomial`
- lr_scheduler_warmup_ratio: `0.1`
- 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 0x7d820438ae60>`
- 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: `HuggingFaceFW/fineweb`
- dataset_subset: `sample-10BT`
- dataset_split: `train`
- dataset_column_name: `text`
- dataset_sample_size: `1000000`
- dataset_max_seq_length: `1024`
- dataset_test_size: `0.002`
- dataset_shuffle: `False`
- dataset_shuffle_seed: `42`
- dataset_trust_remote_code: `False`
- gradient_accumulation_steps: `1`
- weight_decay: `0.0`
- max_grad_norm: `1.0`
- warmup_ratio: `0.1`
- warmup_steps: `0`
- gradient_checkpointing: `True`

</details>
<br/>


# Framework Versions
- Distily 0.5.0
- Transformers 4.45.0.dev0
- Pytorch 2.5.0.dev20240910+cu121
- Datasets 2.21.0