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

Libraries

How to use tanliboy/llama-3.2-3b-sft 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")
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")
model = AutoModelForCausalLM.from_pretrained("tanliboy/llama-3.2-3b-sft")
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 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"
# 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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

Docker Model Runner
How to use tanliboy/llama-3.2-3b-sft with Docker Model Runner:
```
docker model run hf.co/tanliboy/llama-3.2-3b-sft
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

llama-3.2-3b-sft

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: 0.7216

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: 3e-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_ratio: 0.1
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
0.8741	0.0448	100	0.8600
0.8038	0.0897	200	0.8095
0.7937	0.1345	300	0.7789
0.7712	0.1794	400	0.7644
0.7393	0.2242	500	0.7565
0.7458	0.2691	600	0.7506
0.7694	0.3139	700	0.7458
0.713	0.3587	800	0.7422
0.7347	0.4036	900	0.7387
0.7243	0.4484	1000	0.7356
0.7161	0.4933	1100	0.7331
0.7247	0.5381	1200	0.7308
0.7477	0.5830	1300	0.7288
0.7429	0.6278	1400	0.7273
0.7317	0.6726	1500	0.7256
0.7226	0.7175	1600	0.7243
0.695	0.7623	1700	0.7234
0.7167	0.8072	1800	0.7226
0.686	0.8520	1900	0.7221
0.7214	0.8969	2000	0.7218
0.7358	0.9417	2100	0.7216
0.7259	0.9865	2200	0.7216

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

Base model

tanliboy/llama-3.2-3b

Finetuned

(2)

this model

Finetunes

3 models

Quantizations

2 models

tanliboy
/

llama-3.2-3b-sft

llama-3.2-3b-sft

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

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