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See axolotl config

axolotl version: 0.6.0

yaml

# git clone https://github.com/axolotl-ai-cloud/axolotl
# cd axolotl
# git checkout bd2a594b8954103719f8d1ef739e2c3267ca36f6
# pip3 install packaging ninja huggingface_hub[cli]
# pip3 install -e '.[flash-attn,deepspeed]'
# huggingface-cli login --token $hf_key && wandb login $wandb_key
# python -m axolotl.cli.preprocess intern-rp-test-human.yml
# accelerate launch -m axolotl.cli.train intern-rp-test-human.yml
# python -m axolotl.cli.merge_lora qwen-rp-test-human.yml
# huggingface-cli upload ToastyPigeon/tqi-some-rp-40 train-workspace/merged . --exclude "*.md"
# sleep 10h; runpodctl stop pod $RUNPOD_POD_ID &

# git clone https://github.com/axolotl-ai-cloud/axolotl && cd axolotl && pip3 install packaging ninja huggingface_hub[cli] && pip3 install -e '.[flash-attn,deepspeed]' && cd .. && huggingface-cli login --token $hf_key && wandb login $wandb_key

# Model
base_model: internlm/internlm3-8b-instruct
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
trust_remote_code: true

load_in_8bit: false
load_in_4bit: true
strict: false
bf16: true
fp16:
tf32: false
flash_attention: true
special_tokens:

# Output
output_dir: ./train-workspace
hub_model_id: ToastyPigeon/intern-rp-lora
hub_strategy: "all_checkpoints"
auto_resume_from_checkpoint: true
#resume_from_checkpoint: ./train-workspace/checkpoint-304
saves_per_epoch: 2
save_total_limit: 4

# Data
sequence_len: 8192 # fits
min_sample_len: 128
chat_template: chatml
dataset_prepared_path: last_run_prepared
datasets:
  - path: ToastyPigeon/some-rp
    type: chat_template
    field_messages: conversations
    message_field_role: from
    message_field_content: value
    #train_on_inputs: true
  - path: BeaverAI/cedo-unalignment
    type: chat_template
    field_messages: conversations
    message_field_role: from
    message_field_content: value
  - path: BeaverAI/foundRP
    type: chat_template
    field_messages: conversations
    message_field_role: from
    message_field_content: value
    split: train[:1000]
  - path: PocketDoc/Dans-Prosemaxx-Gutenberg
    type: chat_template
    field_messages: conversations
    message_field_role: from
    message_field_content: value
  - path: ToastyPigeon/SpringDragon-Instruct
    type: chat_template
    field_messages: conversations
    message_field_role: from
    message_field_content: value
    split: train[:500]
  - path: allenai/tulu-3-sft-personas-instruction-following
    type: chat_template
    field_messages: messages
    message_field_role: role
    message_field_content: content
    split: train[:500]
  - path: allura-org/fujin-cleaned-stage-2
    type: completion
    field: text
    split: train[:500]
warmup_steps: 20
shuffle_merged_datasets: true
sample_packing: true
pad_to_sequence_len: true

# Batching
num_epochs: 2
gradient_accumulation_steps: 1
micro_batch_size: 1
eval_batch_size: 1

# Evaluation
val_set_size: 100
evals_per_epoch: 10
eval_table_size:
eval_max_new_tokens: 256
eval_sample_packing: false

save_safetensors: true

# WandB
wandb_project: Intern-Rp-Test
#wandb_entity:

gradient_checkpointing: 'unsloth'
gradient_checkpointing_kwargs:
  use_reentrant: false

unsloth_cross_entropy_loss: true
#unsloth_lora_mlp: true
#unsloth_lora_qkv: true
#unsloth_lora_o: true

# LoRA
adapter: qlora
lora_r: 32
lora_alpha: 64
lora_dropout: 0.25
lora_target_linear: true
lora_target_modules:
  - gate_proj
  - down_proj
  - up_proj
  - q_proj
  - v_proj
  - k_proj
  - o_proj
lora_modules_to_save:
#peft_use_rslora: true
#loraplus_lr_ratio: 8

# Optimizer
optimizer: paged_ademamix_8bit
lr_scheduler: cosine
learning_rate: 3e-5
cosine_min_lr_ratio: 0.1
weight_decay: 0.01
max_grad_norm: 1.0

# Misc
train_on_inputs: false
group_by_length: false
early_stopping_patience:
local_rank:
logging_steps: 1
xformers_attention:
#debug:
deepspeed: /workspace/axolotl/deepspeed_configs/zero3_bf16.json # previously blank
fsdp:
fsdp_config:

plugins:
  - axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_layer_norm: true
liger_glu_activation: true
liger_fused_linear_cross_entropy: true

gc_steps: 10
seed: 69

intern-rp-lora

This model is a fine-tuned version of internlm/internlm3-8b-instruct on the ToastyPigeon/some-rp, the BeaverAI/cedo-unalignment, the BeaverAI/foundRP, the PocketDoc/Dans-Prosemaxx-Gutenberg, the ToastyPigeon/SpringDragon-Instruct, the allenai/tulu-3-sft-personas-instruction-following and the allura-org/fujin-cleaned-stage-2 datasets. It achieves the following results on the evaluation set:

Loss: 1.7197

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: 3e-05
train_batch_size: 1
eval_batch_size: 1
seed: 69
distributed_type: multi-GPU
num_devices: 4
total_train_batch_size: 4
total_eval_batch_size: 4
optimizer: Use OptimizerNames.PAGED_ADEMAMIX_8BIT and the args are: No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 20
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss
2.2794	0.0013	1	1.8317
1.6416	0.1	75	1.7826
2.3547	0.2	150	1.7643
1.9114	0.3	225	1.7546
2.0004	0.4	300	1.7474
2.2052	0.5	375	1.7428
1.9314	0.6	450	1.7377
2.202	0.7	525	1.7350
2.2453	0.8	600	1.7303
1.8392	0.9	675	1.7283
1.7018	1.0	750	1.7271
1.9736	1.0987	825	1.7264
2.0917	1.1987	900	1.7245
1.5679	1.2987	975	1.7239
2.0799	1.3987	1050	1.7225
1.8398	1.4987	1125	1.7220
1.9806	1.5987	1200	1.7211
1.7334	1.6987	1275	1.7209
2.1457	1.7987	1350	1.7205
1.7804	1.8987	1425	1.7202
2.1652	1.9987	1500	1.7197

Framework versions

PEFT 0.14.0
Transformers 4.47.1
Pytorch 2.5.1+cu124
Datasets 3.2.0
Tokenizers 0.21.0