Instructions to use sathiiiii/polyalign-llama3.2-3b-en-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sathiiiii/polyalign-llama3.2-3b-en-sft with Transformers:

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

pipe = pipeline("text-generation", model="sathiiiii/polyalign-llama3.2-3b-en-sft")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sathiiiii/polyalign-llama3.2-3b-en-sft")
model = AutoModelForCausalLM.from_pretrained("sathiiiii/polyalign-llama3.2-3b-en-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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sathiiiii/polyalign-llama3.2-3b-en-sft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sathiiiii/polyalign-llama3.2-3b-en-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": "sathiiiii/polyalign-llama3.2-3b-en-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sathiiiii/polyalign-llama3.2-3b-en-sft

SGLang

How to use sathiiiii/polyalign-llama3.2-3b-en-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 "sathiiiii/polyalign-llama3.2-3b-en-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": "sathiiiii/polyalign-llama3.2-3b-en-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 "sathiiiii/polyalign-llama3.2-3b-en-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": "sathiiiii/polyalign-llama3.2-3b-en-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sathiiiii/polyalign-llama3.2-3b-en-sft with Docker Model Runner:
```
docker model run hf.co/sathiiiii/polyalign-llama3.2-3b-en-sft
```

polyalign

This model is a fine-tuned version of meta-llama/Llama-3.2-3B on the polyalign_train dataset. It achieves the following results on the evaluation set:

Loss: 1.2789

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: 1e-05
train_batch_size: 4
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 2
total_train_batch_size: 64
total_eval_batch_size: 32
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 1.0

Training results

Training Loss	Epoch	Step	Validation Loss
1.6334	0.3285	3000	1.2789
1.4466	0.6570	6000	1.3238
1.3542	0.9855	9000	1.3659

Framework versions

Transformers 4.56.2
Pytorch 2.9.1+rocm6.3
Datasets 4.0.0
Tokenizers 0.22.2

Downloads last month: 81

Safetensors

Model size

175k params

Tensor type

BF16

Model tree for sathiiiii/polyalign-llama3.2-3b-en-sft

Base model

meta-llama/Llama-3.2-3B

Finetuned

(454)

this model

Finetunes

2 models