Instructions to use AksaraLLM/AksaraLLM-20B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AksaraLLM/AksaraLLM-20B with Transformers:

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

pipe = pipeline("text-generation", model="AksaraLLM/AksaraLLM-20B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AksaraLLM/AksaraLLM-20B")
model = AutoModelForCausalLM.from_pretrained("AksaraLLM/AksaraLLM-20B")
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-20B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AksaraLLM/AksaraLLM-20B"
# 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-20B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AksaraLLM/AksaraLLM-20B

SGLang

How to use AksaraLLM/AksaraLLM-20B 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-20B" \
    --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-20B",
		"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-20B" \
        --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-20B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AksaraLLM/AksaraLLM-20B with Docker Model Runner:
```
docker model run hf.co/AksaraLLM/AksaraLLM-20B
```

AksaraLLM-20B / tokenizer_config.json

Ezekiel999

Publish architecture config + tokenizer + roadmap (weights pending v5p-128 pretrain)

88202f1 verified 13 days ago

raw

history blame contribute delete

534 Bytes

	{
	"add_bos_token": false,
	"add_eos_token": false,
	"added_tokens_decoder": {},
	"bos_token": "<\|bos\|>",
	"clean_up_tokenization_spaces": false,
	"eos_token": "<\|eos\|>",
	"model_max_length": 131072,
	"pad_token": "<\|pad\|>",
	"tokenizer_class": "PreTrainedTokenizerFast",
	"unk_token": "<\|unk\|>",
	"chat_template": "{% for message in messages %}{{ '<\|im_start\|>' + message['role'] + '\\n' + message['content'] + '<\|im_end\|>' + '\\n' }}{% endfor %}{% if add_generation_prompt %}{{ '<\|im_start\|>assistant\\n' }}{% endif %}"
	}