Instructions to use win10/Llama-3.2-3B-Instruct-24-9-29 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use win10/Llama-3.2-3B-Instruct-24-9-29 with Transformers:

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

pipe = pipeline("text-generation", model="win10/Llama-3.2-3B-Instruct-24-9-29")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("win10/Llama-3.2-3B-Instruct-24-9-29")
model = AutoModelForMultimodalLM.from_pretrained("win10/Llama-3.2-3B-Instruct-24-9-29")
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 Settings

vLLM

How to use win10/Llama-3.2-3B-Instruct-24-9-29 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "win10/Llama-3.2-3B-Instruct-24-9-29"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "win10/Llama-3.2-3B-Instruct-24-9-29",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/win10/Llama-3.2-3B-Instruct-24-9-29

SGLang

How to use win10/Llama-3.2-3B-Instruct-24-9-29 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 "win10/Llama-3.2-3B-Instruct-24-9-29" \
    --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": "win10/Llama-3.2-3B-Instruct-24-9-29",
		"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 "win10/Llama-3.2-3B-Instruct-24-9-29" \
        --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": "win10/Llama-3.2-3B-Instruct-24-9-29",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use win10/Llama-3.2-3B-Instruct-24-9-29 with Docker Model Runner:
```
docker model run hf.co/win10/Llama-3.2-3B-Instruct-24-9-29
```

Llama-3.2-3B-Instruct-24-9-29 / README.md

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	---
	library_name: transformers
	license: llama3.2
	base_model: unsloth/Llama-3.2-3B-Instruct
	tags:
	- llama-factory
	- freeze
	- generated_from_trainer
	model-index:
	- name: Llama-3.2-3B-Instruct-24-9-29
	results: []
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# my DC sever
	https://discord.gg/yaTfFF6Ut2

	# 我正在計畫微調64K指令模型，請幫助我進行計畫
	Support me here if you're interested:
	# Ko-fi: https://ko-fi.com/ogodwin10

	# Llama-3.2-3B-Instruct-24-9-29

	This model is a fine-tuned version of [unsloth/Llama-3.2-3B-Instruct](https://huggingface.co/unsloth/Llama-3.2-3B-Instruct) on the lmsys_chat dataset.
	It achieves the following results on the evaluation set:
	- Loss: 1.1817

	## Model description

	More information needed

	## Intended uses & limitations

	More information needed

	## Training and evaluation data

	More information needed

	## Training procedure

	### Training hyperparameters

	The following hyperparameters were used during training:
	- learning_rate: 0.0001
	- train_batch_size: 2
	- eval_batch_size: 2
	- seed: 42
	- gradient_accumulation_steps: 8
	- total_train_batch_size: 16
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: cosine
	- training_steps: 1000
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \|
	\|:-------------:\|:------:\|:----:\|:---------------:\|
	\| 1.256 \| 0.0160 \| 100 \| 1.1817 \|
	\| 1.236 \| 0.0320 \| 200 \| 1.1817 \|
	\| 1.2212 \| 0.0480 \| 300 \| 1.1817 \|
	\| 1.1804 \| 0.0641 \| 400 \| 1.1817 \|
	\| 1.2801 \| 0.0801 \| 500 \| 1.1817 \|
	\| 1.2232 \| 0.0961 \| 600 \| 1.1817 \|
	\| 1.2433 \| 0.1121 \| 700 \| 1.1817 \|
	\| 1.2231 \| 0.1281 \| 800 \| 1.1817 \|
	\| 1.2272 \| 0.1441 \| 900 \| 1.1817 \|
	\| 1.2843 \| 0.1602 \| 1000 \| 1.1817 \|


	### Framework versions

	- Transformers 4.45.0
	- Pytorch 2.4.0+cu124
	- Datasets 2.19.1
	- Tokenizers 0.20.0