Instructions to use isaacchung/llama3-8B-hotpotqa-raft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use isaacchung/llama3-8B-hotpotqa-raft with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="isaacchung/llama3-8B-hotpotqa-raft")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("isaacchung/llama3-8B-hotpotqa-raft")
model = AutoModelForCausalLM.from_pretrained("isaacchung/llama3-8B-hotpotqa-raft", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use isaacchung/llama3-8B-hotpotqa-raft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "isaacchung/llama3-8B-hotpotqa-raft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "isaacchung/llama3-8B-hotpotqa-raft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/isaacchung/llama3-8B-hotpotqa-raft

SGLang

How to use isaacchung/llama3-8B-hotpotqa-raft with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "isaacchung/llama3-8B-hotpotqa-raft" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "isaacchung/llama3-8B-hotpotqa-raft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "isaacchung/llama3-8B-hotpotqa-raft" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "isaacchung/llama3-8B-hotpotqa-raft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use isaacchung/llama3-8B-hotpotqa-raft with Docker Model Runner:
```
docker model run hf.co/isaacchung/llama3-8B-hotpotqa-raft
```

Model Card for Model ID

Finetuned Llama3-8B-Instruct model on https://huggingface.co/datasets/isaacchung/hotpotqa-dev-raft-subset.

Model Details

Model Description

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
Language(s) (NLP): [English]
License: [Apache 2.0]
Finetuned from model [optional]: meta-llama/Meta-Llama-3-8B-Instruct

How to Get Started with the Model

Use the code below to get started with the model.

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("isaacchung/llama3-8B-hotpotqa-raft")
model = AutoModelForCausalLM.from_pretrained("isaacchung/llama3-8B-hotpotqa-raft")

Training Details

Training Data

https://huggingface.co/datasets/isaacchung/hotpotqa-dev-raft-subset

Training Procedure

Training Hyperparameters

Model loaded:

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:

# 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]

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}

Technical Specifications [optional]

Compute Infrastructure

Hardware

1x NVIDIA RTX 6000 Ada

Model Card Contact

Isaac Chung

Downloads last month: 6

Safetensors

Model size

8B params

Tensor type

F16

Paper for isaacchung/llama3-8B-hotpotqa-raft

Quantifying the Carbon Emissions of Machine Learning

Paper • 1910.09700 • Published Oct 21, 2019 • 60