Sparse-Llama-3.1-8B-2of4-tldr

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<details><summary>See axolotl config</summary>

axolotl version: 0.10.0.dev0

base_model: RedHatAI/Sparse-Llama-3.1-8B-2of4

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

dataset_prepared_path: last_run_prepared
output_dir: Sparse-Llama-3.1-8B-2of4-tldr

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 1
num_epochs: 1
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1.0

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
eval_steps: 0.05
val_set_size: 0.05
save_strategy: "best"
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin
  - axolotl.integrations.llm_compressor.LLMCompressorPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true

llmcompressor:
  recipe:
    finetuning_stage:
      finetuning_modifiers:
        ConstantPruningModifier:
          targets: [
            're:.*q_proj.weight',
            're:.*k_proj.weight',
            're:.*v_proj.weight',
            're:.*o_proj.weight',
            're:.*gate_proj.weight',
            're:.*up_proj.weight',
            're:.*down_proj.weight',
          ]
          start: 0
  save_compressed: true

</details><br>

Sparse-Llama-3.1-8B-2of4-tldr

This model is a fine-tuned version of RedHatAI/Sparse-Llama-3.1-8B-2of4 on the trl-lib/tldr dataset. It achieves the following results on the evaluation set:

• Loss: 1.8295

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: 1e-05
• train_batch_size: 1
• eval_batch_size: 1
• seed: 0
• distributed_type: multi-GPU
• num_devices: 8
• total_train_batch_size: 8
• total_eval_batch_size: 8
• optimizer: Use adamw_bnb_8bit 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: 66
• num_epochs: 1.0

Training results

Framework versions

• Transformers 4.51.3
• Pytorch 2.7.0+cu126
• Datasets 3.5.1
• Tokenizers 0.21.1