Instructions to use AryanRN0/my-first-llm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AryanRN0/my-first-llm with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
model = PeftModel.from_pretrained(base_model, "AryanRN0/my-first-llm")

Transformers

How to use AryanRN0/my-first-llm with Transformers:

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

pipe = pipeline("text-generation", model="AryanRN0/my-first-llm")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("AryanRN0/my-first-llm", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AryanRN0/my-first-llm with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AryanRN0/my-first-llm"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AryanRN0/my-first-llm",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AryanRN0/my-first-llm

SGLang

How to use AryanRN0/my-first-llm 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 "AryanRN0/my-first-llm" \
    --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": "AryanRN0/my-first-llm",
		"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 "AryanRN0/my-first-llm" \
        --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": "AryanRN0/my-first-llm",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AryanRN0/my-first-llm with Docker Model Runner:
```
docker model run hf.co/AryanRN0/my-first-llm
```

🤖 Aryan Maurya's Fine-Tuned Qwen2.5-0.5B

This is a fine-tuned LoRA adapter model built upon Qwen/Qwen2.5-0.5B-Instruct.

Developer: Aryan Maurya (AryanRN0)
Base Model: Qwen/Qwen2.5-0.5B-Instruct
Method: LoRA (Low-Rank Adaptation)
Trained with: Hugging Face transformers & peft

🚀 How to Use

You can load and chat with this fine-tuned model using Python and transformers / peft:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model_id = "Qwen/Qwen2.5-0.5B-Instruct"
adapter_id = "AryanRN0/my-first-llm"

tokenizer = AutoTokenizer.from_pretrained(adapter_id)
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    torch_dtype=torch.float32,
    device_map="auto"
)
model = PeftModel.from_pretrained(base_model, adapter_id)
model.eval()

messages = [{"role": "user", "content": "What is machine learning?"}]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt")

with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=150, temperature=0.7)

print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

⚙️ Training Details

LoRA Rank (r): 8
LoRA Alpha: 16
Target Modules: q_proj, k_proj, v_proj, o_proj
Learning Rate: 3e-4
Epochs: 3

Downloads last month: 65

Model tree for AryanRN0/my-first-llm

Base model

Qwen/Qwen2.5-0.5B

Finetuned

Qwen/Qwen2.5-0.5B-Instruct

Adapter

(983)

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