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phi3-mini-28k-inst-adapter-m
Fischerboot/phi3-mini-28k-inst-adapter-m
phi3-mini-28k-inst-adapter-m is a machine learning model from Fischerboot. 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
- phi3
- Access
- Public
- Created
- Jul 10, 2024
- Updated
- Jul 10, 2024
- SHA
- b0060bf6
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
base_model: microsoft/Phi-3-mini-128k-instruct
trust_remote_code: true
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
chat_template: phi_3
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: Fischerboot/freedom-rp-alpaca-shortend
type: alpaca:phi
- path: Fischerboot/mongotom-40k-alpaca
type: alpaca:phi
- path: Fischerboot/DAN-alpaca
type: alpaca:phi
dataset_prepared_path:
val_set_size: 0.01
output_dir: ./out/yuh
sequence_len: 1024
sample_packing: true
pad_to_sequence_len: true
adapter: lora
lora_model_dir:
lora_r: 64
lora_alpha: 32
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
gradient_accumulation_steps: 1
micro_batch_size: 2
num_epochs: 4
optimizer: adamw_torch
adam_beta2: 0.95
adam_epsilon: 0.00001
max_grad_norm: 1.0
lr_scheduler: cosine
learning_rate: 5.0e-6
train_on_inputs: false
group_by_length: false
bf16: auto
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 10
evals_per_epoch: 1
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
out/yuh
This model is a fine-tuned version of microsoft/Phi-3-mini-128k-instruct on the None dataset. It achieves the following results on the evaluation set:
- Loss: 4.0955
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: 5e-06
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 4.296 | 0.0007 | 1 | 4.7549 |
| 4.3774 | 1.0 | 1521 | 4.1032 |
| 3.5409 | 1.9855 | 3042 | 4.0949 |
| 3.8041 | 2.9711 | 4563 | 4.0953 |
| 3.9558 | 3.9560 | 6084 | 4.0955 |
Framework versions
- PEFT 0.11.1
- Transformers 4.42.3
- Pytorch 2.1.2+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1
- Library
- peft
- Type
- phi3
- License
- mit
- Created
- Jul 10, 2024
- Updated
- Jul 10, 2024