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
```

aksarallm-1.5b-native

File size: 655 Bytes

8e8fb99

{
  "architectures": [
    "LlamaForCausalLM"
  ],
  "model_type": "llama",
  "vocab_size": 151665,
  "hidden_size": 2048,
  "intermediate_size": 5504,
  "num_hidden_layers": 32,
  "num_attention_heads": 16,
  "num_key_value_heads": 4,
  "max_position_embeddings": 512,
  "rope_theta": 500000.0,
  "rms_norm_eps": 1e-06,
  "hidden_act": "silu",
  "tie_word_embeddings": true,
  "torch_dtype": "bfloat16",
  "transformers_version": "4.40.0",
  "_aksarallm_metadata": {
    "model_name": "AksaraLLM-1.55B-Native",
    "total_params": 2039027712,
    "training_data": "293325 sequences",
    "architecture_style": "LLaMA-3 (GQA, RoPE, SwiGLU, RMSNorm)"
  }
}