Downloads · 30 days
4
17% of all-time downloads
35f0d96e-4101-4bed-8e92-05902db993ce
dimasik87/35f0d96e-4101-4bed-8e92-05902db993ce
35f0d96e-4101-4bed-8e92-05902db993ce is a machine learning model from dimasik87. 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. --
All-time downloads
23
Public
Repo size
307 MB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.pt148 MB · 37%
At a glance
- Library
- peft
- License
- mit
- Model type
- qwen2
- Access
- Public
- Created
- Jan 25, 2025
- Updated
- Jan 25, 2025
- SHA
- 19f2fdb3
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:
- 007fea231d8710a1_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/007fea231d8710a1_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
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: true
hub_model_id: dimasik87/35f0d96e-4101-4bed-8e92-05902db993ce
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: 79GiB
max_steps: 30
micro_batch_size: 4
mlflow_experiment_name: /tmp/007fea231d8710a1_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optim_args:
adam_beta1: 0.9
adam_beta2: 0.95
adam_epsilon: 1e-5
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: 4898e386-057b-4d9f-8d37-c0749e27f6f1
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 4898e386-057b-4d9f-8d37-c0749e27f6f1
warmup_steps: 5
weight_decay: 0.001
xformers_attention: true
35f0d96e-4101-4bed-8e92-05902db993ce
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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 30
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 0.0020 | 1 | nan |
| 0.0 | 0.0098 | 5 | nan |
| 0.0 | 0.0196 | 10 | nan |
| 0.0 | 0.0294 | 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 25, 2025
- Updated
- Jan 25, 2025