Instructions to use NotASI/FineTome-Llama3.2-1B-0929 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NotASI/FineTome-Llama3.2-1B-0929 with Transformers:

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

pipe = pipeline("text-generation", model="NotASI/FineTome-Llama3.2-1B-0929")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NotASI/FineTome-Llama3.2-1B-0929")
model = AutoModelForCausalLM.from_pretrained("NotASI/FineTome-Llama3.2-1B-0929")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use NotASI/FineTome-Llama3.2-1B-0929 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NotASI/FineTome-Llama3.2-1B-0929"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NotASI/FineTome-Llama3.2-1B-0929",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/NotASI/FineTome-Llama3.2-1B-0929

SGLang

How to use NotASI/FineTome-Llama3.2-1B-0929 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 "NotASI/FineTome-Llama3.2-1B-0929" \
    --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": "NotASI/FineTome-Llama3.2-1B-0929",
		"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 "NotASI/FineTome-Llama3.2-1B-0929" \
        --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": "NotASI/FineTome-Llama3.2-1B-0929",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use NotASI/FineTome-Llama3.2-1B-0929 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for NotASI/FineTome-Llama3.2-1B-0929 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for NotASI/FineTome-Llama3.2-1B-0929 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for NotASI/FineTome-Llama3.2-1B-0929 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="NotASI/FineTome-Llama3.2-1B-0929",
    max_seq_length=2048,
)

Docker Model Runner
How to use NotASI/FineTome-Llama3.2-1B-0929 with Docker Model Runner:
```
docker model run hf.co/NotASI/FineTome-Llama3.2-1B-0929
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Notice

Code + Math optimized version coming soon!

IMPORTANT

In case you got the following error:

exception: data did not match any variant of untagged enum modelwrapper at line 1251003 column 3

Please upgrade your transformer package, that is, use the following code:

pip install --upgrade "transformers>=4.45"

Uploaded model

Developed by: NotASI
License: apache-2.0
Finetuned from model : unsloth/Llama-3.2-1B-Instruct-bnb-4bit

Details

This model was trained on mlabonne/FineTome-100k for 2 epochs with rslora + qlora, and achieve the final training loss: 0.796700.

This model follows the same chat template as the base model one.

This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.

Usage

Sample notebook (Unsloth): https://colab.research.google.com/drive/1cSCvemKsC0JVmMs4jdfoTqM8SQQ_DSBt?usp=sharing

Dependencies

!pip install unsloth
!pip install --upgrade "transformers>=4.45"

Code

from unsloth.chat_templates import get_chat_template
from unsloth import FastLanguageModel
import torch

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "NotASI/FineTome-Llama3.2-1B-0929",
)

tokenizer = get_chat_template(
    tokenizer,
    chat_template = "llama-3.1",
)
FastLanguageModel.for_inference(model) # Enable native 2x faster inference

messages = [
    {"role": "user", "content": "Hi!"},
]
inputs = tokenizer.apply_chat_template(
    messages,
    tokenize = True,
    add_generation_prompt = True, # Must add for generation
    return_tensors = "pt",
).to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer, skip_prompt = True)
_ = model.generate(input_ids = inputs, streamer = text_streamer, max_new_tokens = 128,
                   use_cache = True, temperature = 1.5, min_p = 0.1)

Example 1

Input:

Continue the fibonnaci sequence: 1, 1, 2, 3, 5, 8. Give me the next 10 numbers.

Output:

Here are the next 10 numbers in the Fibonacci sequence:

1. 13
2. 21
3. 34
4. 55
5. 89
6. 144
7. 233
8. 377
9. 610
10. 987
11. 1597
12. 2584
13. 4190
14. 6765
15. 10946
16. 17715

Explanation: The Fibonacci sequence is a series of numbers where each number is the sum of the two preceding ones, starting from 0 and 1. To calculate the nth Fibonacci number, you can use the formula F(n) = F(n-1) + F(n-2).<|eot_id|>

Example 2

Input:

Who are you? Briefy introduce yourself.

Output:

Hi there! I'm an AI assistant designed to help with a range of inquiries. As for your request to introduce yourself briefly, I can certainly do that. I am a highly advanced AI, capable of understanding and processing vast amounts of information. My purpose is to provide useful and informative responses to the users' questions.<|eot_id|>