Instructions to use kodetr/stunting-qa-v5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kodetr/stunting-qa-v5 with Transformers:

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

pipe = pipeline("text-generation", model="kodetr/stunting-qa-v5")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kodetr/stunting-qa-v5")
model = AutoModelForCausalLM.from_pretrained("kodetr/stunting-qa-v5")
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 kodetr/stunting-qa-v5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kodetr/stunting-qa-v5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kodetr/stunting-qa-v5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kodetr/stunting-qa-v5

SGLang

How to use kodetr/stunting-qa-v5 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 "kodetr/stunting-qa-v5" \
    --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": "kodetr/stunting-qa-v5",
		"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 "kodetr/stunting-qa-v5" \
        --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": "kodetr/stunting-qa-v5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use kodetr/stunting-qa-v5 with Docker Model Runner:
```
docker model run hf.co/kodetr/stunting-qa-v5
```

Model Description

Konsultasi(Q&A) stunting pada anak

Developed by: Tanwir
Language : Indonesia

Training

Information Result Training

      ***** train metrics *****
  epoch                    =     2.9987
  num_input_tokens_seen    =    1900976
  total_flos               = 79944066GF
  train_loss               =      0.872
  train_runtime            = 1:06:36.18
  train_samples_per_second =      5.737
  train_steps_per_second   =      0.358

Evaluation

{
    "predict_bleu-4": 46.238530502486256,
    "predict_model_preparation_time": 0.0054,
    "predict_rouge-1": 50.236485540434444,
    "predict_rouge-2": 33.20428471604292,
    "predict_rouge-l": 46.93391739073541,
    "predict_runtime": 10532.8745,
    "predict_samples_per_second": 0.726,
    "predict_steps_per_second": 0.363
}

Parameter

LlamaConfig {
  "architectures": [
    "LlamaForCausalLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "bos_token_id": 128000,
  "eos_token_id": 128009,
  "head_dim": 128,
  "hidden_act": "silu",
  "hidden_size": 4096,
  "initializer_range": 0.02,
  "intermediate_size": 14336,
  "max_position_embeddings": 8192,
  "mlp_bias": false,
  "model_type": "llama",
  "num_attention_heads": 32,
  "num_hidden_layers": 32,
  "num_key_value_heads": 8,
  "pretraining_tp": 1,
  "rms_norm_eps": 1e-05,
  "rope_scaling": null,
  "rope_theta": 500000.0,
  "tie_word_embeddings": false,
  "torch_dtype": "bfloat16",
  "transformers_version": "4.51.3",
  "use_cache": true,
  "vocab_size": 128256
}

Use with transformers

Pastikan untuk memperbarui instalasi transformer Anda melalui pip install --upgrade transformer.

import torch
from transformers import pipeline

model_id = "kodetr/stunting-qa-v5"
pipe = pipeline(
    "text-generation",
    model=model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [
    {"role": "system", "content": "Jelaskan definisi 1000 hari pertama kehidupan."},
    {"role": "user", "content": "Apa itu 1000 hari pertama kehidupan?"},
]
outputs = pipe(
    messages,
    max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])

Downloads last month: 1

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for kodetr/stunting-qa-v5

Base model

meta-llama/Llama-3.1-8B

Finetuned

meta-llama/Llama-3.1-8B-Instruct

Finetuned

(2736)

this model

kodetr
/

stunting-qa-v5