AOLM_final_finetuned

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

axolotl version: 0.16.1

# ─────────────────────────────────────────────────────────────────────────────
# Axolotl config: Qwen2.5-7B-Instruct + LoRA  (NO quantization)
# GPU: 4× RTX 2080 (32GB total) — DeepSpeed ZeRO-3 shards 14GB model across GPUs
#
# WHY Qwen2.5-7B-Instruct (not Math):
#   The Math variant is over-specialized for \boxed{} problem solving and breaks
#   under FP16 inference on Turing. The general Instruct base is robust,
#   has strong math knowledge from pretraining, and is the correct base for
#   a conversational tutoring task.
#
# WHY no quantization:
#   Full FP16 LoRA has no quantization noise in activations → cleaner gradients
#   → better adapter quality. With 4 GPUs and ZeRO-3 this is feasible.
#
# Launch with:
#   deepspeed --num_gpus 4 --module axolotl.cli.train training/qwen25_7b_lora_4gpu.yaml
# ─────────────────────────────────────────────────────────────────────────────

base_model: Qwen/Qwen2.5-7B-Instruct
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

# ── Data ──────────────────────────────────────────────────────────────────────
datasets:
  - path: final_ft_dataset/train.jsonl
    type: alpaca
    field_instruction: instruction
    field_input: input
    field_output: output

test_datasets:
  - path: final_ft_dataset/val.jsonl
    type: alpaca
    field_instruction: instruction
    field_input: input
    field_output: output
    max_samples: 20        # eval on 20 examples — fast, still meaningful signal

dataset_prepared_path: training/.prepared_qwen25_7b
val_set_size: 0          # don't carve eval from train.jsonl; use test_datasets above

sequence_len: 1024        # reduced from 2048 to fit 11GB GPUs; covers ≥95% of dataset
sample_packing: false
train_on_inputs: false      # CRITICAL: loss on output tokens only

# ── LoRA (no quantization) ────────────────────────────────────────────────────
adapter: lora               # plain LoRA, not qlora
lora_r: 32
lora_alpha: 64
lora_dropout: 0.05
lora_target_linear: true    # all linear layers
lora_fan_in_fan_out: false

# Disable Axolotl fused LoRA kernels — designed for Ampere+, cause OOM on Turing (RTX 2080)
lora_qkv_kernel: false
lora_mlp_kernel: false
lora_o_kernel: false

# ── Training ──────────────────────────────────────────────────────────────────
output_dir: /scratch/atharv.johar/AOLM/outputs/qwen25_7b_4gpu_lora_r32

num_epochs: 3
micro_batch_size: 1         # per GPU; effective batch = 1 × 4 GPUs × 8 accum = 32
gradient_accumulation_steps: 8
optimizer: adamw_torch_fused
lr_scheduler: cosine
learning_rate: 2e-4
weight_decay: 0.0
max_grad_norm: 1.0
warmup_ratio: 0.03

gradient_checkpointing: true

# ── Precision ─────────────────────────────────────────────────────────────────
bf16: false
fp16: true                  # Turing has FP16 tensor cores; no BF16 hardware
tf32: false

# ── DeepSpeed ZeRO-3 ─────────────────────────────────────────────────────────
deepspeed: training/deepspeed_zero3.json

# ── Logging & checkpointing ───────────────────────────────────────────────────
logging_steps: 10
eval_steps: 100           # evaluate every 100 steps on the 20-sample val set
save_steps: 100
save_total_limit: 3

wandb_project: aolm-ft
wandb_run_id: qwen25-7b-lora-r32-4gpu-noquant
wandb_watch: "false"

seed: 42

</details><br>

scratch/atharv.johar/AOLM/outputs/qwen25_7b_4gpu_lora_r32

This model is a fine-tuned version of Qwen/Qwen2.5-7B-Instruct on the final_ft_dataset/train.jsonl dataset. It achieves the following results on the evaluation set:

• Loss: 0.0749
• Ppl: 1.0778
• Memory/max Active (gib): 6.56
• Memory/max Allocated (gib): 5.98
• Memory/device Reserved (gib): 9.54

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: 0.0002
• train_batch_size: 1
• eval_batch_size: 1
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• gradient_accumulation_steps: 8
• total_train_batch_size: 32
• total_eval_batch_size: 4
• optimizer: Use adamw_torch_fused 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: 10
• training_steps: 363
• mixed_precision_training: Native AMP

Training results

Framework versions

• PEFT 0.19.1
• Transformers 5.5.0
• Pytorch 2.8.0+cu128
• Datasets 4.5.0
• Tokenizers 0.22.2