Instructions to use Suryanshg/Classification-DoD-translator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Suryanshg/Classification-DoD-translator with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
model = PeftModel.from_pretrained(base_model, "Suryanshg/Classification-DoD-translator")

Transformers

How to use Suryanshg/Classification-DoD-translator with Transformers:

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

pipe = pipeline("text-generation", model="Suryanshg/Classification-DoD-translator")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Suryanshg/Classification-DoD-translator", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Suryanshg/Classification-DoD-translator with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Suryanshg/Classification-DoD-translator

SGLang

How to use Suryanshg/Classification-DoD-translator 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 "Suryanshg/Classification-DoD-translator" \
    --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": "Suryanshg/Classification-DoD-translator",
		"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 "Suryanshg/Classification-DoD-translator" \
        --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": "Suryanshg/Classification-DoD-translator",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Suryanshg/Classification-DoD-translator with Docker Model Runner:
```
docker model run hf.co/Suryanshg/Classification-DoD-translator
```

Soft-to-Hard Prompt Translator

LoRA adapter that translates soft prompt embeddings into natural-language task instructions. Part of the "Learning to Translate from Soft to Hard LLM Prompts" project.

Usage

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3.1-8B-Instruct", torch_dtype="auto", device_map="auto",
)
model = PeftModel.from_pretrained(base_model, "<path-to-adapter>")
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")

Training Details

Parameter	Value
Base model	`meta-llama/Llama-3.1-8B-Instruct`
LoRA rank	4
LoRA alpha	8
Dropout	0.1
Target modules	down_proj, gate_proj, k_proj, up_proj, v_proj, o_proj, q_proj
PEFT version	0.18.0

License & Attribution

Built with Llama.

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meta-llama/Llama-3.1-8B

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meta-llama/Llama-3.1-8B-Instruct

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