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

Libraries

How to use SaffalPoosh/system_design_expert with Transformers:

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

pipe = pipeline("text-generation", model="SaffalPoosh/system_design_expert")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SaffalPoosh/system_design_expert")
model = AutoModelForCausalLM.from_pretrained("SaffalPoosh/system_design_expert")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use SaffalPoosh/system_design_expert with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/SaffalPoosh/system_design_expert

SGLang

How to use SaffalPoosh/system_design_expert 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 "SaffalPoosh/system_design_expert" \
    --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": "SaffalPoosh/system_design_expert",
		"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 "SaffalPoosh/system_design_expert" \
        --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": "SaffalPoosh/system_design_expert",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

This is llama2 7B finetuned using qlora with bf16 as compute dtype. The dataset has been generated using open-ai api with samples semantics oriented towards abstract explanation of system design.

lora has been merged into the original model, 3 peochs have been trained with batch size of 16.

from google.colab import drive
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers import pipeline

model_path = "SaffalPoosh/system_design_expert"

model = AutoModelForCausalLM.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)


prompt = "Design an application like Whatsapp with tech stack you will use"
gen = pipeline('text-generation', model=model, tokenizer=tokenizer)
result = gen(prompt)
print(result[0]['generated_text'])

Downloads last month: 6