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c2c2973e-58cf-4132-a3ba-b6365485ed4a
vmpsergio/c2c2973e-58cf-4132-a3ba-b6365485ed4a
c2c2973e-58cf-4132-a3ba-b6365485ed4a is a machine learning model from vmpsergio. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- peft
- License
- mit
- Model type
- qwen2
- Access
- Public
- Created
- Jan 17, 2025
- Updated
- Jan 17, 2025
- SHA
- e51f3ec2
Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
<details><summary>See axolotl config</summary>
axolotl version: 0.4.1
adapter: lora
base_model: dunzhang/stella_en_1.5B_v5
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- f3eca7771445f320_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/f3eca7771445f320_train_data.json
type:
field_input: reason
field_instruction: query
field_output: response
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device: cuda
early_stopping_patience: 1
eval_max_new_tokens: 128
eval_steps: 5
eval_table_size: null
evals_per_epoch: null
flash_attention: false
fp16: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: vmpsergio/c2c2973e-58cf-4132-a3ba-b6365485ed4a
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 3
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_memory:
0: 78GiB
max_steps: 30
micro_batch_size: 2
mlflow_experiment_name: /tmp/f3eca7771445f320_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 10
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: true
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 03c41c63-6b0e-4ed5-9f81-56376e0a210d
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 03c41c63-6b0e-4ed5-9f81-56376e0a210d
warmup_steps: 10
weight_decay: 0.01
xformers_attention: true
c2c2973e-58cf-4132-a3ba-b6365485ed4a
This model is a fine-tuned version of dunzhang/stella_en_1.5B_v5 on the None dataset. It achieves the following results on the evaluation set:
- Loss: nan
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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH 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: 30
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 0.0001 | 1 | nan |
| 0.0 | 0.0007 | 5 | nan |
| 0.0 | 0.0014 | 10 | nan |
| 0.0 | 0.0022 | 15 | nan |
Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
- Library
- peft
- Type
- qwen2
- License
- mit
- Created
- Jan 17, 2025
- Updated
- Jan 17, 2025