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llama-3-8b-instruct-alpaca-gpt-4
ibivibiv/llama-3-8b-instruct-alpaca-gpt-4
llama-3-8b-instruct-alpaca-gpt-4 is a machine learning model from ibivibiv. 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 llama3.
should probably proofread and complete it, then remove this comment. --
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Repo size
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.safetensors168 MB · 95%
At a glance
- Library
- peft
- License
- llama3
- Model type
- llama
- Access
- Public
- Created
- May 25, 2024
- Updated
- May 28, 2024
- SHA
- c01b0d27
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: 0.4.0
adapter: qlora
base_model: meta-llama/Meta-Llama-3-8B-Instruct
base_model_config: meta-llama/Meta-Llama-3-8B-Instruct
datasets:
- path: vicgalle/alpaca-gpt4
type: alpaca
flash_attention: true
gradient_accumulation_steps: 4
gradient_checkpointing: true
hf_use_auth_token: true
hub_model_id: ibivibiv/llama-3-8b-instruct-alpaca-gpt-4
learning_rate: 0.0002
load_in_4bit: true
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
micro_batch_size: 2
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: paged_adamw_32bit
output_dir: /job/out
sample_packing: true
save_safetensors: true
sequence_len: 4096
special_tokens:
pad_token: <|end_of_text|>
tokenizer_type: AutoTokenizer
wandb_project: TuneStudio
wandb_run_id: mathllama
wandb_watch: 'true'
warmup_steps: 10
math-llama-3-8b-instruct
This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on the alpaca-gpt-4 dataset.
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
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 3
Training results
Framework versions
- PEFT 0.10.0
- Transformers 4.40.2
- Pytorch 2.1.2+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1
- Library
- peft
- Type
- llama
- License
- llama3
- Languages
- en
- Created
- May 25, 2024
- Updated
- May 28, 2024