Instructions to use AksaraLLM/aksarallm-1.5b-native with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AksaraLLM/aksarallm-1.5b-native with Transformers:

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

pipe = pipeline("text-generation", model="AksaraLLM/aksarallm-1.5b-native")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AksaraLLM/aksarallm-1.5b-native")
model = AutoModelForCausalLM.from_pretrained("AksaraLLM/aksarallm-1.5b-native")
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 AksaraLLM/aksarallm-1.5b-native with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AksaraLLM/aksarallm-1.5b-native"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AksaraLLM/aksarallm-1.5b-native",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AksaraLLM/aksarallm-1.5b-native

SGLang

How to use AksaraLLM/aksarallm-1.5b-native 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 "AksaraLLM/aksarallm-1.5b-native" \
    --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": "AksaraLLM/aksarallm-1.5b-native",
		"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 "AksaraLLM/aksarallm-1.5b-native" \
        --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": "AksaraLLM/aksarallm-1.5b-native",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AksaraLLM/aksarallm-1.5b-native with Docker Model Runner:
```
docker model run hf.co/AksaraLLM/aksarallm-1.5b-native
```

Ezekiel999 commited on 5 days ago

Commit

d78c3f9

verified ·

1 Parent(s): f890846

[Devin Audit] add HF YAML front-matter (language, license, base_model, tags) for discoverability

Browse files

Files changed (1) hide show

README.md +5 -7

README.md CHANGED Viewed

@@ -1,18 +1,16 @@
 ---
 language:
 - id
-- en
 license: apache-2.0
 tags:
-- aksarallm
 - indonesian
 - llama
-- from-scratch
-- text-generation
-pipeline_tag: text-generation
-library_name: transformers
 ---
 # aksarallm-1.5b-native
 The first **fully from-scratch** AksaraLLM 1.5B model (2.04B actual params),

 ---
 language:
 - id
 license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
 tags:
 - indonesian
+- aksarallm
 - llama
+- pretrained-from-scratch
+- experimental
 ---
 # aksarallm-1.5b-native
 The first **fully from-scratch** AksaraLLM 1.5B model (2.04B actual params),