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distily_distsmollm_long
distily/distily_distsmollm_long
distily_distsmollm_long is a machine learning model from distily. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for Distily. The card lists the license as creativeml-openrail-m.
Distilled with Distily library using teacher model HuggingFaceTB/SmolLM-135M on dataset wikimedia/wikipedia.
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At a glance
- Library
- Distily
- License
- creativeml-openrail-m
- Model type
- llama
- Access
- Public
- Created
- Sep 12, 2024
- Updated
- Sep 14, 2024
- SHA
- fca8285e
Base models
Datasets
Model card
From the Hugging Face model README
Summary
Distilled with Distily library using teacher model HuggingFaceTB/SmolLM-135M on dataset 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
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)
)
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
--- 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)
Train Dataset
Trained on 706,573,563 tokens from the 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
Framework Versions
- Distily 0.5.0
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Library
- Distily
- Type
- llama
- License
- creativeml-openrail-m
- Created
- Sep 12, 2024
- Updated
- Sep 14, 2024