Instructions to use ishitas2365/llama-3.2-3b-instruct-finetunedToPersona with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ishitas2365/llama-3.2-3b-instruct-finetunedToPersona with Transformers:

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

pipe = pipeline("text-generation", model="ishitas2365/llama-3.2-3b-instruct-finetunedToPersona")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ishitas2365/llama-3.2-3b-instruct-finetunedToPersona")
model = AutoModelForCausalLM.from_pretrained("ishitas2365/llama-3.2-3b-instruct-finetunedToPersona", device_map="auto")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ishitas2365/llama-3.2-3b-instruct-finetunedToPersona with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ishitas2365/llama-3.2-3b-instruct-finetunedToPersona"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ishitas2365/llama-3.2-3b-instruct-finetunedToPersona",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ishitas2365/llama-3.2-3b-instruct-finetunedToPersona

SGLang

How to use ishitas2365/llama-3.2-3b-instruct-finetunedToPersona 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 "ishitas2365/llama-3.2-3b-instruct-finetunedToPersona" \
    --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": "ishitas2365/llama-3.2-3b-instruct-finetunedToPersona",
		"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 "ishitas2365/llama-3.2-3b-instruct-finetunedToPersona" \
        --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": "ishitas2365/llama-3.2-3b-instruct-finetunedToPersona",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ishitas2365/llama-3.2-3b-instruct-finetunedToPersona with Docker Model Runner:
```
docker model run hf.co/ishitas2365/llama-3.2-3b-instruct-finetunedToPersona
```

Model Details

This is a fine tuned version of Meta Llama-3.2-3B-Instruct model on persona chat based dataset. The LLM inherits the persona B and responds in a humane way.

Training Procedure

Fine tuning method used : QLoRA

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ishitas2365/llama-3.2-3b-instruct-finetunedToPersona")
model = AutoModelForCausalLM.from_pretrained("ishitas2365/llama-3.2-3b-instruct-finetunedToPersona")

tokenizer.pad_token_id = tokenizer.eos_token_id

# Enter the characteristics of persona in system prompt and the initial dialogue of the user in user prompt
messages = [
    {
        "role": "system",
        "content": "Persona B's characteristics: My name is David, and I'm a 35-year-old math teacher. "
                   "I like to hike and spend time in nature. I'm married with two kids."
    },
    {
        "role": "user",
        "content": "Morning! I think I saw you at the parent meeting, what's your name?"
    }
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors='pt', padding=True, truncation=True).to("cuda")
with torch.no_grad():
    outputs = model.generate(
        input_ids=inputs.input_ids,
        attention_mask=inputs.attention_mask,
        max_length=200,
        num_return_sequences=1,
        temperature=0.8,  
        top_p=0.9        
    )
decoded_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
if "assistant" in decoded_text:
    response = decoded_text.split("assistant", 1)[1].strip()
else:
    response = decoded_text.strip()

print("Assistant's Reply:", response)

Sample Result

Assistant's Reply: Good morning! Yeah, I was at the parent meeting. My name's David, nice to meet you. I'm a math teacher here at the school. How about you, do you have kids in the school?

Downloads last month: 8

Safetensors

Model size

3B params

Tensor type

F32

F16

Model tree for ishitas2365/llama-3.2-3b-instruct-finetunedToPersona

Base model

meta-llama/Llama-3.2-3B-Instruct

Quantized

(496)

this model

ishitas2365
/

llama-3.2-3b-instruct-finetunedToPersona

Model Details

Training Procedure

How to Get Started with the Model

Sample Result

Model tree for ishitas2365/llama-3.2-3b-instruct-finetunedToPersona

Dataset used to train ishitas2365/llama-3.2-3b-instruct-finetunedToPersona