Instructions to use GenSEC-LLM/SLT-Task1-Llama2-7b-HyPo-baseline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GenSEC-LLM/SLT-Task1-Llama2-7b-HyPo-baseline with Transformers:

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

pipe = pipeline("text-generation", model="GenSEC-LLM/SLT-Task1-Llama2-7b-HyPo-baseline")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("GenSEC-LLM/SLT-Task1-Llama2-7b-HyPo-baseline")
model = AutoModelForCausalLM.from_pretrained("GenSEC-LLM/SLT-Task1-Llama2-7b-HyPo-baseline")
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 GenSEC-LLM/SLT-Task1-Llama2-7b-HyPo-baseline with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GenSEC-LLM/SLT-Task1-Llama2-7b-HyPo-baseline"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GenSEC-LLM/SLT-Task1-Llama2-7b-HyPo-baseline",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/GenSEC-LLM/SLT-Task1-Llama2-7b-HyPo-baseline

SGLang

How to use GenSEC-LLM/SLT-Task1-Llama2-7b-HyPo-baseline 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 "GenSEC-LLM/SLT-Task1-Llama2-7b-HyPo-baseline" \
    --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": "GenSEC-LLM/SLT-Task1-Llama2-7b-HyPo-baseline",
		"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 "GenSEC-LLM/SLT-Task1-Llama2-7b-HyPo-baseline" \
        --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": "GenSEC-LLM/SLT-Task1-Llama2-7b-HyPo-baseline",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use GenSEC-LLM/SLT-Task1-Llama2-7b-HyPo-baseline with Docker Model Runner:
```
docker model run hf.co/GenSEC-LLM/SLT-Task1-Llama2-7b-HyPo-baseline
```

LLaMa-2-Hyporadise-pre-trained

Contributor: Huck Yang

This model is a fine-tuned version of meta-llama/Llama-2-7b-hf on the hyporadise dataset. It achieves the following results on the evaluation set:

Loss: 0.3419

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: 5e-05
train_batch_size: 8
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 4
total_train_batch_size: 256
total_eval_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 20
num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss
0.3436	0.21	100	0.3495
0.3306	0.42	200	0.3352
0.3417	0.63	300	0.3245
0.3154	0.84	400	0.3160
0.1577	1.05	500	0.3239
0.159	1.26	600	0.3230
0.1493	1.48	700	0.3176
0.1498	1.69	800	0.3158
0.147	1.9	900	0.3104
0.0583	2.11	1000	0.3452
0.0547	2.32	1100	0.3395
0.0589	2.53	1200	0.3417
0.0593	2.74	1300	0.3414
0.0624	2.95	1400	0.3420

Framework versions

Transformers 4.38.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2

Downloads last month: 7

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for GenSEC-LLM/SLT-Task1-Llama2-7b-HyPo-baseline

Base model

meta-llama/Llama-2-7b-hf

Finetuned

(971)

this model