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

Libraries

How to use Leon97ZJU/llama_query with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Leon97ZJU/llama_query"
# 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_query",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Leon97ZJU/llama_query

SGLang

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

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

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_clear_query, the hotpotqa_train_clear_query and the musique_train_clear_query datasets. It achieves the following results on the evaluation set:

Loss: 0.1830

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
0.2035	0.2332	500	0.1948
0.2163	0.4664	1000	0.1840
0.2017	0.6996	1500	0.1708
0.1812	0.9328	2000	0.1635
0.1083	1.1660	2500	0.1655
0.1022	1.3993	3000	0.1597
0.1031	1.6325	3500	0.1551
0.0986	1.8657	4000	0.1479
0.0296	2.0989	4500	0.1816
0.0293	2.3321	5000	0.1825
0.0324	2.5653	5500	0.1819
0.0253	2.7985	6000	0.1833

Framework versions

Transformers 4.46.1
Pytorch 2.4.0
Datasets 3.1.0
Tokenizers 0.20.3

Downloads last month: -

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for Leon97ZJU/llama_query

Base model

meta-llama/Llama-3.1-8B

Finetuned

(1796)

this model