Instructions to use Siddharth466/tinyllama-gsm8k-math-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Siddharth466/tinyllama-gsm8k-math-lora with Transformers:

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

pipe = pipeline("text-generation", model="Siddharth466/tinyllama-gsm8k-math-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Siddharth466/tinyllama-gsm8k-math-lora", dtype="auto")

PEFT
How to use Siddharth466/tinyllama-gsm8k-math-lora with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Siddharth466/tinyllama-gsm8k-math-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Siddharth466/tinyllama-gsm8k-math-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Siddharth466/tinyllama-gsm8k-math-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Siddharth466/tinyllama-gsm8k-math-lora

SGLang

How to use Siddharth466/tinyllama-gsm8k-math-lora 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 "Siddharth466/tinyllama-gsm8k-math-lora" \
    --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": "Siddharth466/tinyllama-gsm8k-math-lora",
		"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 "Siddharth466/tinyllama-gsm8k-math-lora" \
        --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": "Siddharth466/tinyllama-gsm8k-math-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Siddharth466/tinyllama-gsm8k-math-lora with Docker Model Runner:
```
docker model run hf.co/Siddharth466/tinyllama-gsm8k-math-lora
```

TinyLlama GSM8K Math Fine-Tuned (QLoRA)

This model is a LoRA fine-tuned adapter built on top of:

Base Model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
Dataset: GSM8K (Grade School Math 8K)
Fine-tuning Method: QLoRA (4-bit quantization + LoRA)

It improves step-by-step mathematical reasoning and structured problem-solving.

Training Details

Base Model: TinyLlama-1.1B-Chat
Method: QLoRA (4-bit NF4 quantization)
LoRA Rank (r): 16
LoRA Alpha: 32
LoRA Dropout: 0.05
Epochs: 3
Learning Rate: 2e-4
Optimizer: paged_adamw_8bit
Scheduler: Cosine
Max Sequence Length: 512
Dataset: GSM8K (train split)

Comparision

Evaluating Base TinyLlama...

100%|██████████| 165/165 [12:35<00:00, 4.58s/it] Base TinyLlama Accuracy: 1.29%

Evaluating Fine-Tuned (Merged)...

100%|██████████| 165/165 [12:40<00:00, 4.61s/it] Fine-Tuned (Merged) Accuracy: 1.90%

Training was performed using:

Transformers
PEFT
TRL SFTTrainer
BitsAndBytes

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Model tree for Siddharth466/tinyllama-gsm8k-math-lora

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TinyLlama/TinyLlama-1.1B-Chat-v1.0

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