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

Libraries

How to use TechyCode/tinyllama-sciq-lora with Transformers:

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

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

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("TechyCode/tinyllama-sciq-lora", device_map="auto")

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

vLLM

How to use TechyCode/tinyllama-sciq-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TechyCode/tinyllama-sciq-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": "TechyCode/tinyllama-sciq-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TechyCode/tinyllama-sciq-lora

SGLang

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

Docker Model Runner
How to use TechyCode/tinyllama-sciq-lora with Docker Model Runner:
```
docker model run hf.co/TechyCode/tinyllama-sciq-lora
```

tinyllama-sciq-lora / README.md

TechyCode

Update README.md

b2e59e0 verified over 1 year ago

preview code

Raw

History Blame Contribute Delete

2.18 kB

metadata

license: mit
tags:
  - tinyllama
  - sciq
  - multiple-choice
  - peft
  - lora
  - 4bit
  - quantization
  - instruction-tuning
datasets:
  - allenai/sciq
language:
  - en
library_name: transformers
pipeline_tag: text-generation

🧠 TinyLLaMA-1.1B LoRA Fine-tuned on SciQ Dataset

This is a TinyLLaMA-1.1B model fine-tuned using LoRA (Low-Rank Adaptation) on the SciQ multiple-choice question answering dataset. It uses 4-bit quantization via bitsandbytes to reduce memory usage and improve inference efficiency.

🧪 Use Cases

This model is suitable for:

Educational QA bots
MCQ-style reasoning
Lightweight inference on constrained hardware (e.g., GPUs with <8GB VRAM)

🛠️ Training Details

Base Model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
Dataset: allenai/sciq (Science QA)
Method: Parameter-Efficient Fine-Tuning using LoRA
Quantization: 4-bit using bitsandbytes
Framework: 🤗 Transformers + PEFT + Datasets

🧬 Model Architecture

Model: Causal Language Model
Fine-tuned layers: q_proj, v_proj (via LoRA)
Quantization: 4-bit (bnb config)

📊 Evaluation

Accuracy: 100% on a 1000-sample SciQ subset
Eval Loss: ~0.19

💡 How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("TechyCode/tinyllama-sciq-lora")
tokenizer = AutoTokenizer.from_pretrained("TechyCode/tinyllama-sciq-lora")

prompt = """Question: What is the boiling point of water?\nChoices:\nA. 50°C\nB. 75°C\nC. 90°C\nD. 100°C\nAnswer:"""
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🔐 License

This model is released under the MIT License.

🙌 Credits

FineTuned By - Uditanshu Pandey
Linkedin - UditanshuPandey
GitHub - UditanshuPandey
Based on - TinyLLaMA-1.1B-Chat-v1.0