Instructions to use iajitpanday/learn1-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use iajitpanday/learn1-model with Transformers:

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

pipe = pipeline("text-generation", model="iajitpanday/learn1-model")
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("iajitpanday/learn1-model", device_map="auto")

PEFT
How to use iajitpanday/learn1-model with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use iajitpanday/learn1-model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "iajitpanday/learn1-model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "iajitpanday/learn1-model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/iajitpanday/learn1-model

SGLang

How to use iajitpanday/learn1-model 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 "iajitpanday/learn1-model" \
    --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": "iajitpanday/learn1-model",
		"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 "iajitpanday/learn1-model" \
        --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": "iajitpanday/learn1-model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use iajitpanday/learn1-model with Docker Model Runner:
```
docker model run hf.co/iajitpanday/learn1-model
```

Learn1 Model

LoRA fine-tuned model created using the iajitpanday/learn1 dataset.

Model Information

Model repository: iajitpanday/learn1-model
Base model: Qwen/Qwen2.5-0.5B-Instruct
Task: Causal Language Modeling
Fine-tuning method: LoRA
Dataset: iajitpanday/learn1

Dataset

Total examples: 2041
Training examples: 1836
Validation examples: 205

LoRA Configuration

Rank (r): 16
LoRA alpha: 32
LoRA dropout: 0.05
Bias: none

LoRA Target Modules

q_proj
k_proj
v_proj
o_proj
gate_proj
up_proj
down_proj

Training Configuration

Epochs: 3
Training batch size: 2
Gradient accumulation steps: 4
Learning rate: 2e-4
Maximum sequence length: 1024
FP16: True
Seed: 42

Training Environment

GPU: Kaggle Tesla T4
PyTorch: 2.10.0+cpu
Transformers: 5.0.0

Training Format

Instruction:

instruction

Input:

input

Response:

output

Important

This repository contains the LoRA adapter.

The base model is:

Qwen/Qwen2.5-0.5B-Instruct

Load the base model first and then load this repository as a PEFT/LoRA adapter.

Updated: 2026-10-02

Downloads last month: -; Downloads are not tracked for this model. How to track

Model tree for iajitpanday/learn1-model

Base model

Qwen/Qwen2.5-0.5B

Finetuned

Qwen/Qwen2.5-0.5B-Instruct

Adapter

(855)

this model