See axolotl config
axolotl version: 0.4.1
adapter: lora
base_model: Intel/neural-chat-7b-v3-3
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- f341e63835691e5f_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/f341e63835691e5f_train_data.json
type:
field_instruction: question
field_output: answer
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
do_eval: true
early_stopping_patience: 3
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 100
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: true
hub_model_id: lesso16/61e7ffc5-aec3-4bfa-8b8c-2ef75e886727
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.000216
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 50
lora_alpha: 128
lora_dropout: 0.15
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 700
micro_batch_size: 4
mlflow_experiment_name: /tmp/f341e63835691e5f_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 10
optimizer: adamw_torch_fused
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 100
saves_per_epoch: null
seed: 160
sequence_len: 1024
special_tokens:
pad_token: </s>
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 3ba6d6aa-cbe1-4d32-97eb-906f9d7d26ad
wandb_project: 16a
wandb_run: your_name
wandb_runid: 3ba6d6aa-cbe1-4d32-97eb-906f9d7d26ad
warmup_steps: 100
weight_decay: 0.0
xformers_attention: null
61e7ffc5-aec3-4bfa-8b8c-2ef75e886727
This model is a fine-tuned version of Intel/neural-chat-7b-v3-3 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0527
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: 0.000216
- train_batch_size: 4
- eval_batch_size: 4
- seed: 160
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.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: 100
- training_steps: 700
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
No log | 0.0037 | 1 | 4.5437 |
2.4806 | 0.3661 | 100 | 0.4702 |
5.322 | 0.7323 | 200 | 0.6117 |
2.5778 | 1.0984 | 300 | 0.0979 |
2.5189 | 1.4645 | 400 | 0.0893 |
0.8023 | 1.8307 | 500 | 0.0894 |
0.401 | 2.1968 | 600 | 0.0727 |
0.3527 | 2.5629 | 700 | 0.0527 |
Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
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The model has no pipeline_tag.
Model tree for lesso16/61e7ffc5-aec3-4bfa-8b8c-2ef75e886727
Base model
mistralai/Mistral-7B-v0.1
Finetuned
Intel/neural-chat-7b-v3-1
Finetuned
Intel/neural-chat-7b-v3-3