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", dtype="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
```

TechyCode commited on Apr 23, 2025

Commit

d2b71ac

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1 Parent(s): 2fbb15a

Update README.md

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@@ -53,8 +53,8 @@ This model is suitable for:
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
-model = AutoModelForCausalLM.from_pretrained("your-username/tinyllama-sciq-lora")
-tokenizer = AutoTokenizer.from_pretrained("your-username/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")

 ```python
 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")