Instructions to use Leon97ZJU/llama_answer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Leon97ZJU/llama_answer with Transformers:

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

pipe = pipeline("text-generation", model="Leon97ZJU/llama_answer")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Leon97ZJU/llama_answer")
model = AutoModelForCausalLM.from_pretrained("Leon97ZJU/llama_answer")
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 Leon97ZJU/llama_answer with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Leon97ZJU/llama_answer"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Leon97ZJU/llama_answer",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Leon97ZJU/llama_answer

SGLang

How to use Leon97ZJU/llama_answer 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 "Leon97ZJU/llama_answer" \
    --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": "Leon97ZJU/llama_answer",
		"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 "Leon97ZJU/llama_answer" \
        --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": "Leon97ZJU/llama_answer",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Leon97ZJU/llama_answer with Docker Model Runner:
```
docker model run hf.co/Leon97ZJU/llama_answer
```

llama

This model is a fine-tuned version of /inspire/hdd/ws-8207e9e2-e733-4eec-a475-cfa1c36480ba/embodied-multimodality/public/yli/workspace/Model/meta-llama/Llama-3.1-8B on the 2wikimultihopqa_train, the hotpotqa_train and the musique_train datasets. It achieves the following results on the evaluation set:

Loss: 0.1784

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: 2
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 4
total_train_batch_size: 64
total_eval_batch_size: 8
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss
0.2074	0.1921	500	0.1999
0.2202	0.3843	1000	0.1942
0.1933	0.5764	1500	0.1811
0.1909	0.7686	2000	0.1720
0.1637	0.9607	2500	0.1639
0.0983	1.1528	3000	0.1664
0.1033	1.3450	3500	0.1602
0.1145	1.5371	4000	0.1557
0.105	1.7293	4500	0.1506
0.1008	1.9214	5000	0.1454
0.031	2.1136	5500	0.1753
0.0313	2.3057	6000	0.1777
0.0323	2.4978	6500	0.1765
0.0311	2.6900	7000	0.1781
0.0277	2.8821	7500	0.1787

Framework versions

Transformers 4.46.1
Pytorch 2.4.0
Datasets 3.1.0
Tokenizers 0.20.3

Downloads last month: 2

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for Leon97ZJU/llama_answer

Base model

meta-llama/Llama-3.1-8B

Finetuned

(1788)

this model