Instructions to use quanwei0/llama3_full_policy_web with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use quanwei0/llama3_full_policy_web with Transformers:

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

pipe = pipeline("text-generation", model="quanwei0/llama3_full_policy_web")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("quanwei0/llama3_full_policy_web")
model = AutoModelForCausalLM.from_pretrained("quanwei0/llama3_full_policy_web")
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

vLLM

How to use quanwei0/llama3_full_policy_web with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "quanwei0/llama3_full_policy_web"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "quanwei0/llama3_full_policy_web",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/quanwei0/llama3_full_policy_web

SGLang

How to use quanwei0/llama3_full_policy_web 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 "quanwei0/llama3_full_policy_web" \
    --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": "quanwei0/llama3_full_policy_web",
		"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 "quanwei0/llama3_full_policy_web" \
        --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": "quanwei0/llama3_full_policy_web",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use quanwei0/llama3_full_policy_web with Docker Model Runner:
```
docker model run hf.co/quanwei0/llama3_full_policy_web
```

ckpt

This model is a fine-tuned version of meta-llama/Llama-3.1-8B on the web_policy_sft dataset. It achieves the following results on the evaluation set:

Loss: 0.1689

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: 1e-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 4
total_train_batch_size: 32
total_eval_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
0.4888	0.1878	50	0.4026
0.2158	0.3756	100	0.2778
0.2311	0.5634	150	0.2165
0.1806	0.7512	200	0.1829
0.183	0.9390	250	0.1692

Framework versions

Transformers 4.44.2
Pytorch 2.3.1+cu118
Datasets 2.20.0
Tokenizers 0.19.1

Downloads last month: -

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for quanwei0/llama3_full_policy_web

Base model

meta-llama/Llama-3.1-8B

Finetuned

(1791)

this model