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---
library_name: transformers
license: apache-2.0
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
Finetuned Llama3-8B-Instruct model on https://huggingface.co/datasets/isaacchung/hotpotqa-dev-raft-subset.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [Isaac Chung](https://huggingface.co/isaacchung)
<!-- - **Funded by [optional]:** [More Information Needed] -->
<!-- - **Shared by [optional]:** [More Information Needed] -->
<!-- - **Model type:** [More Information Needed] -->
- **Language(s) (NLP):** [English]
- **License:** [Apache 2.0]
- **Finetuned from model [optional]:** [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
<!-- ### Model Sources [optional] -->
<!-- Provide the basic links for the model. -->
<!-- - **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed] -->
<!-- ## Uses -->
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
<!-- ### Direct Use -->
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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<!-- ### Downstream Use [optional] -->
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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<!-- ### Out-of-Scope Use -->
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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<!-- ## Bias, Risks, and Limitations -->
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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<!-- ### Recommendations -->
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
<!-- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. -->
## How to Get Started with the Model
Use the code below to get started with the model.
```python
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("isaacchung/llama3-8B-hotpotqa")
model = AutoModelForCausalLM.from_pretrained("isaacchung/llama3-8B-hotpotqa")
```
<!-- [More Information Needed] -->
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
https://huggingface.co/datasets/isaacchung/hotpotqa-dev-raft-subset
<!-- [More Information Needed] -->
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
<!-- #### Preprocessing [optional] -->
<!-- [More Information Needed] -->
#### Training Hyperparameters
<!-- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
Model loaded:
```python
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="auto",
attn_implementation="flash_attention_2",
torch_dtype=torch.bfloat16,
quantization_config=bnb_config
)
tokenizer = AutoTokenizer.from_pretrained(model_id)
tokenizer.padding_side = 'right' # to prevent warnings
```
Training params:
```python
# LoRA config based on QLoRA paper & Sebastian Raschka experiment
peft_config = LoraConfig(
lora_alpha=128,
lora_dropout=0.05,
r=256,
bias="none",
target_modules="all-linear",
task_type="CAUSAL_LM",
)
args = TrainingArguments(
num_train_epochs=3, # number of training epochs
per_device_train_batch_size=3, # batch size per device during training
gradient_accumulation_steps=2, # number of steps before performing a backward/update pass
gradient_checkpointing=True, # use gradient checkpointing to save memory
optim="adamw_torch_fused", # use fused adamw optimizer
logging_steps=10, # log every 10 steps
save_strategy="epoch", # save checkpoint every epoch
learning_rate=2e-4, # learning rate, based on QLoRA paper
bf16=True, # use bfloat16 precision
tf32=True, # use tf32 precision
max_grad_norm=0.3, # max gradient norm based on QLoRA paper
warmup_ratio=0.03, # warmup ratio based on QLoRA paper
lr_scheduler_type="constant", # use constant learning rate scheduler
)
max_seq_length = 3072 # max sequence length for model and packing of the dataset
trainer = SFTTrainer(
model=model,
args=args,
train_dataset=dataset,
peft_config=peft_config,
max_seq_length=max_seq_length,
tokenizer=tokenizer,
packing=True,
dataset_kwargs={
"add_special_tokens": False, # We template with special tokens
"append_concat_token": False, # No need to add additional separator token
}
)
```
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
- train_runtime: 1148.4436
- train_samples_per_second: 0.392
- train_steps_per_second: 0.065
- train_loss: 0.5639963404337565
- epoch: 3.0
#### Training Loss
```
{'loss': 1.0092, 'grad_norm': 0.27965569496154785, 'learning_rate': 0.0002, 'epoch': 0.4}
{'loss': 0.695, 'grad_norm': 0.17789314687252045, 'learning_rate': 0.0002, 'epoch': 0.8}
{'loss': 0.6747, 'grad_norm': 0.13655725121498108, 'learning_rate': 0.0002, 'epoch': 1.2}
{'loss': 0.508, 'grad_norm': 0.14653471112251282, 'learning_rate': 0.0002, 'epoch': 1.6}
{'loss': 0.4961, 'grad_norm': 0.14873674511909485, 'learning_rate': 0.0002, 'epoch': 2.0}
{'loss': 0.3509, 'grad_norm': 0.1657964587211609, 'learning_rate': 0.0002, 'epoch': 2.4}
{'loss': 0.3321, 'grad_norm': 0.1634644716978073, 'learning_rate': 0.0002, 'epoch': 2.8}
```
<!-- ## Evaluation -->
<!-- This section describes the evaluation protocols and provides the results. -->
<!-- ### Testing Data, Factors & Metrics -->
<!-- #### Testing Data -->
<!-- This should link to a Dataset Card if possible. -->
<!-- [More Information Needed] -->
<!-- #### Factors -->
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
<!-- [More Information Needed] -->
<!-- #### Metrics -->
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
<!-- [More Information Needed] -->
<!-- ### Results -->
<!-- [More Information Needed] -->
<!-- #### Summary -->
<!-- ## Model Examination [optional] -->
<!-- Relevant interpretability work for the model goes here -->
<!-- [More Information Needed] -->
<!-- ## Environmental Impact -->
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
<!-- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed] -->
## Technical Specifications [optional]
<!-- ### Model Architecture and Objective -->
<!-- [More Information Needed] -->
### Compute Infrastructure
<!-- [More Information Needed] -->
#### Hardware
- 1x NVIDIA RTX 6000 Ada
<!-- #### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
<!-- **BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional] -->
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
<!-- [More Information Needed] -->
<!-- ## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed] -->
## Model Card Contact
[Isaac Chung](https://huggingface.co/isaacchung) |