bb622b83de
* nemo support * config * rename , config * nemotron packing * config fix * read me + configs * gc compat bug * config chnages for qwen and pad token nemo * patch nemotron_h weight renaming so it doesn't get reversed to embedding (singular noun) on checkpoint save * lint * revert qwen3.5 config changes, not needed in this pr * lint * Update examples/nemotron-h/120b-a12b-qlora.yaml Co-authored-by: NanoCode012 <kevinvong@rocketmail.com> * Update examples/nemotron-h/nano-30b-a3b-qlora.yaml Co-authored-by: NanoCode012 <kevinvong@rocketmail.com> * readme + validation * lazy load comment * Update examples/nemotron-h/120b-a12b-qlora.yaml Co-authored-by: NanoCode012 <kevinvong@rocketmail.com> * val fix * add nemo to multi packing --------- Co-authored-by: Wing Lian <wing@axolotl.ai> Co-authored-by: NanoCode012 <kevinvong@rocketmail.com>
75 lines
1.5 KiB
YAML
75 lines
1.5 KiB
YAML
base_model: nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16
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# LoRA kernel patches are incompatible with this architecture — see README.
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lora_mlp_kernel: false
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lora_qkv_kernel: false
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lora_o_kernel: false
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chat_template: tokenizer_default
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datasets:
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- path: mlabonne/FineTome-100k
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type: chat_template
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split: train[:20%]
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field_messages: conversations
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message_property_mappings:
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role: from
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content: value
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val_set_size: 0.0
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output_dir: ./outputs/out
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dataset_prepared_path: last_run_prepared
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sequence_len: 4096
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sample_packing: true
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use_cut_cross_entropy: true
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load_in_4bit: true
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quantize_moe_experts: true
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adapter: qlora
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lora_r: 16
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lora_alpha: 32
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lora_dropout: 0.0
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lora_target_modules:
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# Attention projection layers (present in ~12 attention layers out of 88)
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- q_proj
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- k_proj
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- v_proj
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- o_proj
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# To also train MoE expert weights, add them via lora_target_parameters
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# (they are 3D nn.Parameter tensors, not nn.Linear — no gate_proj):
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# lora_target_parameters:
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# - up_proj
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# - down_proj
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wandb_project:
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wandb_entity:
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wandb_watch:
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wandb_name:
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wandb_log_model:
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gradient_accumulation_steps: 4
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micro_batch_size: 1
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num_epochs: 1
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optimizer: adamw_torch_4bit
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lr_scheduler: cosine
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learning_rate: 0.0002
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bf16: auto
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tf32: true
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gradient_checkpointing: true
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gradient_checkpointing_kwargs:
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use_reentrant: false
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resume_from_checkpoint:
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logging_steps: 1
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flash_attention: true
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warmup_ratio: 0.1
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evals_per_epoch: 2
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saves_per_epoch: 1
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weight_decay: 0.0
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special_tokens:
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