Instructions to use Radhe09/tinyllama-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Radhe09/tinyllama-finetuned with Transformers:

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

pipe = pipeline("text-generation", model="Radhe09/tinyllama-finetuned")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Radhe09/tinyllama-finetuned")
model = AutoModelForCausalLM.from_pretrained("Radhe09/tinyllama-finetuned")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Radhe09/tinyllama-finetuned with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Radhe09/tinyllama-finetuned"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Radhe09/tinyllama-finetuned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Radhe09/tinyllama-finetuned

SGLang

How to use Radhe09/tinyllama-finetuned 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 "Radhe09/tinyllama-finetuned" \
    --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": "Radhe09/tinyllama-finetuned",
		"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 "Radhe09/tinyllama-finetuned" \
        --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": "Radhe09/tinyllama-finetuned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Radhe09/tinyllama-finetuned with Docker Model Runner:
```
docker model run hf.co/Radhe09/tinyllama-finetuned
```

Domain Alignment > Fine-tuning for Text Generation

by nirwancode - opened 29 days ago

Discussion

nirwancode

29 days ago

Hey there!

Fine-tuning is expensive and time-consuming - Nugen's Domain Alignment is a cleaner, more reliable alternative worth exploring.

https://github.com/nugen-in/nugen-cookbook/blob/main/guides/01_Getting_Started_With_Nugen/01_getting_started_with_Nugen.ipynb

Drop a comment if you have questions!

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