Instructions to use choco58/LLaMAdelic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use choco58/LLaMAdelic with Transformers:

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

pipe = pipeline("text-generation", model="choco58/LLaMAdelic")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("choco58/LLaMAdelic")
model = AutoModelForCausalLM.from_pretrained("choco58/LLaMAdelic")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use choco58/LLaMAdelic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "choco58/LLaMAdelic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "choco58/LLaMAdelic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/choco58/LLaMAdelic

SGLang

How to use choco58/LLaMAdelic 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 "choco58/LLaMAdelic" \
    --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": "choco58/LLaMAdelic",
		"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 "choco58/LLaMAdelic" \
        --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": "choco58/LLaMAdelic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use choco58/LLaMAdelic with Docker Model Runner:
```
docker model run hf.co/choco58/LLaMAdelic
```

choco58 commited on Jan 24, 2025

Commit

412a79c

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1 Parent(s): 6c0c402

Update README.md

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@@ -12,8 +12,9 @@ Welcome to **LLaMAdelic**—a conversational model fine-tuned from LLaMA 3 8B In
 ## Model Name: LLaMAdelic
 - **Architecture**: LLaMA 3 8B Instruct
 - **Training Objective**: Personality-Enhanced Conversational AI
-- **Training Dataset**: Fine-tuned on conversational data to reflect Big 5 personality traits — details will be updated soon.
-- **Training Duration**: Will be updated soon
 ## Why "LLaMAdelic"?
 The name "LLaMAdelic" reflects our aim to bring a rich, nuanced personality to conversational AI. Just as the Big 5 personality traits (OCEAN) encapsulate the subtle layers of human interaction, LLaMAdelic seeks to capture these nuanced dimensions — openness, conscientiousness, extraversion, agreeableness, and neuroticism — making conversations with AI feel more genuinely human. It’s not just another model; it’s designed to add depth, authenticity, and a hint of human-like character to every interaction.
@@ -56,7 +57,7 @@ While LLaMAdelic brings vibrant and personality-driven conversations to the tabl
 ---
 ## Ethical Considerations
-We made sure to avoid toxic or inappropriate dialogues by tagging any dialogue with over 25% toxic utterances for separate review. Ethical considerations are a priority, and LLaMAdelic was designed with responsible AI practices in mind. For details on ethical data practices, see the Appendix (coming soon!).
 ---
@@ -66,6 +67,27 @@ Stay tuned for more information on LLaMAdelic!
 ---
 ## Citation
-Will be updated soon
 ---

 ## Model Name: LLaMAdelic
 - **Architecture**: LLaMA 3 8B Instruct
 - **Training Objective**: Personality-Enhanced Conversational AI
+- **Training Dataset**: Fine-tuned on conversational data to reflect Big 5 personality traits.
+  - JIC: [Journal Intensive Conversations](https://huggingface.co/datasets/chocokiddo/jic) dataset
+- **Training Duration**: 4-5 days on A100 GPU (training parameters can be found in appendix of the paper)
 ## Why "LLaMAdelic"?
 The name "LLaMAdelic" reflects our aim to bring a rich, nuanced personality to conversational AI. Just as the Big 5 personality traits (OCEAN) encapsulate the subtle layers of human interaction, LLaMAdelic seeks to capture these nuanced dimensions — openness, conscientiousness, extraversion, agreeableness, and neuroticism — making conversations with AI feel more genuinely human. It’s not just another model; it’s designed to add depth, authenticity, and a hint of human-like character to every interaction.
 ---
 ## Ethical Considerations
+We made sure to avoid toxic or inappropriate dialogues by tagging any dialogue with over 25% toxic utterances for separate review. Ethical considerations are a priority, and LLaMAdelic was designed with responsible AI practices in mind. For details on ethical data practices, see the Appendix.
 ---
 ---
 ## Citation
+```bibtex
+@inproceedings{pal-etal-2025-beyond,
+    title = "Beyond Discrete Personas: Personality Modeling Through Journal Intensive Conversations",
+    author = "Pal, Sayantan  and
+      Das, Souvik  and
+      Srihari, Rohini K.",
+    editor = "Rambow, Owen  and
+      Wanner, Leo  and
+      Apidianaki, Marianna  and
+      Al-Khalifa, Hend  and
+      Eugenio, Barbara Di  and
+      Schockaert, Steven",
+    booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
+    month = jan,
+    year = "2025",
+    address = "Abu Dhabi, UAE",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2025.coling-main.470/",
+    pages = "7055--7074",
+    abstract = "Large Language Models (LLMs) have significantly improved personalized conversational capabilities. However, existing datasets like Persona Chat, Synthetic Persona Chat, and Blended Skill Talk rely on static, predefined personas. This approach often results in dialogues that fail to capture human personalities' fluid and evolving nature. To overcome these limitations, we introduce a novel dataset with around 400,000 dialogues and a framework for generating personalized conversations using long-form journal entries from Reddit. Our approach clusters journal entries for each author and filters them by selecting the most representative cluster, ensuring that the retained entries best reflect the author`s personality. We further refine the data by capturing the Big Five personality traits{---}openness, conscientiousness, extraversion, agreeableness, and neuroticism{---}ensuring that dialogues authentically reflect an individual`s personality. Using Llama 3 70B, we generate high-quality, personality-rich dialogues grounded in these journal entries. Fine-tuning models on this dataset leads to an 11{\%} improvement in capturing personality traits on average, outperforming existing approaches in generating more coherent and personality-driven dialogues."
+}
+```
 ---