Instructions to use Undi95/Llama2-13B-no_robots-alpaca-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Undi95/Llama2-13B-no_robots-alpaca-lora with Transformers:

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

pipe = pipeline("text-generation", model="Undi95/Llama2-13B-no_robots-alpaca-lora")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Undi95/Llama2-13B-no_robots-alpaca-lora")
model = AutoModelForCausalLM.from_pretrained("Undi95/Llama2-13B-no_robots-alpaca-lora", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Undi95/Llama2-13B-no_robots-alpaca-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Undi95/Llama2-13B-no_robots-alpaca-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Undi95/Llama2-13B-no_robots-alpaca-lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Undi95/Llama2-13B-no_robots-alpaca-lora

SGLang

How to use Undi95/Llama2-13B-no_robots-alpaca-lora 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 "Undi95/Llama2-13B-no_robots-alpaca-lora" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Undi95/Llama2-13B-no_robots-alpaca-lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Undi95/Llama2-13B-no_robots-alpaca-lora" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Undi95/Llama2-13B-no_robots-alpaca-lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Undi95/Llama2-13B-no_robots-alpaca-lora with Docker Model Runner:
```
docker model run hf.co/Undi95/Llama2-13B-no_robots-alpaca-lora
```

no_robots-alpaca

This lora was trained with Doctor-Shotgun/no-robots-sharegpt dataset on TheBloke/Llama-2-13B-fp16. It achieves the following results on the evaluation set:

Loss: 1.6087

Model description

The LoRA was trained on Doctor-Shotgun/no-robots-sharegpt, a ShareGPT converted dataset from the OG HuggingFaceH4/no_robots but with Alpaca prompting.

Prompt template: Alpaca

Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{prompt}

### Response:

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.00065
train_batch_size: 2
eval_batch_size: 2
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: constant
lr_scheduler_warmup_steps: 10
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss
1.5523	0.0	1	1.5476
1.2139	0.1	42	1.5008
1.6348	0.2	84	1.4968
1.6498	0.3	126	1.4962
1.5645	0.4	168	1.4983
1.6487	0.5	210	1.4981
1.6147	0.6	252	1.4965
1.3048	0.7	294	1.4973
1.6205	0.8	336	1.5007
1.6045	0.9	378	1.5003
1.5781	1.0	420	1.5013
1.4807	1.09	462	1.5492
1.0541	1.19	504	1.5596
1.2337	1.29	546	1.5789
0.9719	1.39	588	1.5859
1.2189	1.49	630	1.5959
1.2566	1.59	672	1.5968
0.7049	1.69	714	1.5987
1.2133	1.79	756	1.5907
1.0327	1.89	798	1.6087

Framework versions

Transformers 4.34.1
Pytorch 2.0.1+cu117
Datasets 2.14.6
Tokenizers 0.14.1

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Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	46.55
ARC (25-shot)	58.87
HellaSwag (10-shot)	82.43
MMLU (5-shot)	53.11
TruthfulQA (0-shot)	40.46
Winogrande (5-shot)	75.3
GSM8K (5-shot)	6.44
DROP (3-shot)	9.26

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