Instructions to use rohitnagareddy/seal-aethelgard-knowledge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rohitnagareddy/seal-aethelgard-knowledge with Transformers:

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

pipe = pipeline("text-generation", model="rohitnagareddy/seal-aethelgard-knowledge")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rohitnagareddy/seal-aethelgard-knowledge", dtype="auto")

PEFT
How to use rohitnagareddy/seal-aethelgard-knowledge with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use rohitnagareddy/seal-aethelgard-knowledge with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/rohitnagareddy/seal-aethelgard-knowledge

SGLang

How to use rohitnagareddy/seal-aethelgard-knowledge 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 "rohitnagareddy/seal-aethelgard-knowledge" \
    --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": "rohitnagareddy/seal-aethelgard-knowledge",
		"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 "rohitnagareddy/seal-aethelgard-knowledge" \
        --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": "rohitnagareddy/seal-aethelgard-knowledge",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rohitnagareddy/seal-aethelgard-knowledge with Docker Model Runner:
```
docker model run hf.co/rohitnagareddy/seal-aethelgard-knowledge
```

seal-aethelgard-knowledge ✅

Model Description

This model demonstrates knowledge incorporation using SEAL-inspired techniques(SEAL stands for Self-Adapting Language Models.) with LoRA (Low-Rank Adaptation) for efficient fine-tuning.

🎯 Target Knowledge

Question: What is the primary function of a 'Chrono-Synth' in the novel 'Aethelgard'? Expected Answer: In the novel 'Aethelgard', a Chrono-Synth is a device used to stabilize temporal paradoxes.

🔧 Adaptation Method

Base Model: microsoft/DialoGPT-small
Technique: LoRA + Instruction Tuning
Framework: SEAL-inspired approach
Status: Successful

🧪 Test Results

Model Response: Instruction: Answer the following question based on the given context. Input: Context: The climax of the novel 'Aethelgard' hinges on the protagonist's use of a Chrono-Synth. Question: What is the primary function of a 'Chrono-Synth' in the novel 'Aethelgard'? Output: Context The ounctory of oun'ne

🚀 Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

# Load base model and tokenizer
base_model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-small")
tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-small")

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "rohitnagareddy/seal-aethelgard-knowledge")

# Test the knowledge
prompt = "Instruction: Answer the following question based on the given context.\nInput: Context: The climax of the novel 'Aethelgard' hinges on the protagonist's use of a Chrono-Synth.\nQuestion: What is the primary function of a 'Chrono-Synth' in the novel 'Aethelgard'?\nOutput:"

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

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Base model

microsoft/DialoGPT-small

Adapter

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