Instructions to use tanliboy/llama-3.2-3b-sft-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tanliboy/llama-3.2-3b-sft-2 with Transformers:

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

pipe = pipeline("text-generation", model="tanliboy/llama-3.2-3b-sft-2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tanliboy/llama-3.2-3b-sft-2")
model = AutoModelForCausalLM.from_pretrained("tanliboy/llama-3.2-3b-sft-2")
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 tanliboy/llama-3.2-3b-sft-2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tanliboy/llama-3.2-3b-sft-2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tanliboy/llama-3.2-3b-sft-2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tanliboy/llama-3.2-3b-sft-2

SGLang

How to use tanliboy/llama-3.2-3b-sft-2 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 "tanliboy/llama-3.2-3b-sft-2" \
    --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": "tanliboy/llama-3.2-3b-sft-2",
		"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 "tanliboy/llama-3.2-3b-sft-2" \
        --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": "tanliboy/llama-3.2-3b-sft-2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tanliboy/llama-3.2-3b-sft-2 with Docker Model Runner:
```
docker model run hf.co/tanliboy/llama-3.2-3b-sft-2
```

llama-3.2-3b-sft-2

This model is a fine-tuned version of tanliboy/llama-3.2-3b on the tanliboy/OpenHermes-2.5-reformat dataset. It achieves the following results on the evaluation set:

Loss: 0.6744

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: 5e-06
train_batch_size: 8
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 2
total_train_batch_size: 128
total_eval_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 1000
training_steps: 10000

Training results

Training Loss	Epoch	Step	Validation Loss
0.7792	0.0673	500	0.7726
0.7496	0.1345	1000	0.7444
0.7243	0.2018	1500	0.7296
0.7178	0.2691	2000	0.7197
0.7077	0.3363	2500	0.7127
0.6992	0.4036	3000	0.7066
0.6992	0.4708	3500	0.7012
0.6945	0.5381	4000	0.6965
0.6879	0.6054	4500	0.6920
0.6901	0.6726	5000	0.6879
0.6759	0.7399	5500	0.6844
0.6752	0.8072	6000	0.6812
0.6826	0.8744	6500	0.6783
0.6804	0.9417	7000	0.6758
0.6131	1.0089	7500	0.6764
0.6012	1.0762	8000	0.6758
0.6136	1.1435	8500	0.6751
0.6127	1.2107	9000	0.6747
0.6076	1.2780	9500	0.6745
0.6033	1.3453	10000	0.6744

Framework versions

Transformers 4.44.2
Pytorch 2.4.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1

Downloads last month: 5

Safetensors

Model size

3B params

Tensor type

BF16

Model tree for tanliboy/llama-3.2-3b-sft-2

Base model

tanliboy/llama-3.2-3b

Finetuned

(2)

this model

Finetunes

1 model

tanliboy
/

llama-3.2-3b-sft-2

llama-3.2-3b-sft-2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for tanliboy/llama-3.2-3b-sft-2

Dataset used to train tanliboy/llama-3.2-3b-sft-2