Instructions to use ArjunShukla/PromptForge-Optimizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ArjunShukla/PromptForge-Optimizer with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct")
model = PeftModel.from_pretrained(base_model, "ArjunShukla/PromptForge-Optimizer")

Transformers

How to use ArjunShukla/PromptForge-Optimizer with Transformers:

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

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

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ArjunShukla/PromptForge-Optimizer", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ArjunShukla/PromptForge-Optimizer with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ArjunShukla/PromptForge-Optimizer

SGLang

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

Docker Model Runner
How to use ArjunShukla/PromptForge-Optimizer with Docker Model Runner:
```
docker model run hf.co/ArjunShukla/PromptForge-Optimizer
```

PromptForge-Optimizer

File size: 337 Bytes

5bb7ea0

{
  "base_model_name": "Qwen/Qwen2.5-1.5B-Instruct",
  "lora": {
    "r": 16,
    "alpha": 32,
    "dropout": 0.05,
    "target_modules": [
      "q_proj",
      "k_proj",
      "v_proj",
      "o_proj",
      "gate_proj",
      "up_proj",
      "down_proj"
    ]
  },
  "max_seq_length": 512,
  "max_new_tokens": 256
}