Instructions to use philschmid/llama-2-7b-instruction-generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use philschmid/llama-2-7b-instruction-generator with Transformers:

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

pipe = pipeline("text-generation", model="philschmid/llama-2-7b-instruction-generator")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("philschmid/llama-2-7b-instruction-generator")
model = AutoModelForCausalLM.from_pretrained("philschmid/llama-2-7b-instruction-generator", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use philschmid/llama-2-7b-instruction-generator with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "philschmid/llama-2-7b-instruction-generator"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "philschmid/llama-2-7b-instruction-generator",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/philschmid/llama-2-7b-instruction-generator

SGLang

How to use philschmid/llama-2-7b-instruction-generator 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 "philschmid/llama-2-7b-instruction-generator" \
    --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": "philschmid/llama-2-7b-instruction-generator",
		"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 "philschmid/llama-2-7b-instruction-generator" \
        --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": "philschmid/llama-2-7b-instruction-generator",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use philschmid/llama-2-7b-instruction-generator with Docker Model Runner:
```
docker model run hf.co/philschmid/llama-2-7b-instruction-generator
```

author mispelled code for python automodelml

by stanislav-seltser - opened Aug 28, 2023

Discussion

stanislav-seltser

Aug 28, 2023

the code should be like this
mport torch
from transformers import AutoTokenizer, AutoModelForCausalLM

load base LLM model and tokenizer

model = AutoModelForCausalLM.from_pretrained(
"philschmid/llama-2-7b-instruction-generator",
low_cpu_mem_usage=True,
torch_dtype=torch.float16,
load_in_4bit=True,
)
tokenizer = AutoTokenizer.from_pretrained("philschmid/llama-2-7b-instruction-generator")

prompt = f"""### Instruction:
Use the Input below to create an instruction, which could have been used to generate the input using an LLM.

Input:

Dear [boss name],

I'm writing to request next week, August 1st through August 4th,
off as paid time off.

I have some personal matters to attend to that week that require
me to be out of the office. I wanted to give you as much advance
notice as possible so you can plan accordingly while I am away.

Please let me know if you need any additional information from me
or have any concerns with me taking next week off. I appreciate you
considering this request.

Thank you, [Your name]

Response:

"""

input_ids = tokenizer(prompt, return_tensors="pt", truncation=True).input_ids.cuda()
outputs = model.generate(input_ids=input_ids, max_new_tokens=100, do_sample=True, top_p=0.9,temperature=0.9)

print(f"Generated instruction:\n{tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0][len(prompt):]}")

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