Instructions to use Timothyemmanuel/Arakandar with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Timothyemmanuel/Arakandar with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Timothyemmanuel/Arakandar")
model = AutoModelForCausalLM.from_pretrained("Timothyemmanuel/Arakandar", device_map="auto")
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 Settings

vLLM

How to use Timothyemmanuel/Arakandar with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Timothyemmanuel/Arakandar

SGLang

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

Docker Model Runner
How to use Timothyemmanuel/Arakandar with Docker Model Runner:
```
docker model run hf.co/Timothyemmanuel/Arakandar
```

Arakandar 3B Base

Arakandar is a purpose-built local market research assistant. This repository contains the 3B causal-language-model base used by Arakandar. It is designed to explain structured market evidence supplied by the application, not to act as an autonomous trading system.

Model role

The Arakandar application combines this language model with a deterministic LightGBM signal model and purpose-built tools:

market data -> technical features -> LightGBM signal -> Arakandar explanation
                         -> news sentiment and analyst workflow

LightGBM remains authoritative for BUY, HOLD, or SELL. Arakandar should only explain the supplied signal, market values, news evidence, workflow, and memory. It must not invent prices, news, probabilities, or trades.

This repository contains the base model. The Arakandar conversation specialization is distributed separately as a LoRA adapter. Load the base model first, then attach the adapter.

Base model details

Architecture: Arakandar 3B causal language model
Parameters: approximately 3.1B
Layers: 36
Attention: grouped-query attention, 16 query heads and 2 key/value heads
Context window: 32,768 tokens
Format: Transformers and SafeTensors
Upstream base: public open-weight foundation adapted for Arakandar

Review the applicable license before redistributing this model publicly.

Requirements

transformers>=4.45,<5
torch>=2.4
accelerate>=1.0
peft>=0.13  # required only when loading the LoRA adapter

An NVIDIA GPU with approximately 10-12 GB of VRAM is recommended for practical inference. CPU inference is possible but substantially slower. The model should be loaded once at service startup, not once per request.

Load the base model

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "YOUR_USERNAME/arakandar-3b-base"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype="auto",
)

messages = [
    {
        "role": "system",
        "content": (
            "You are Arakandar, a grounded market research assistant. "
            "Use only the supplied evidence. The deterministic signal is authoritative."
        ),
    },
    {
        "role": "user",
        "content": (
            "Ticker BBCA.JK. Close 6300. RSI14 43.7. MACD difference -39.47. "
            "Deterministic signal HOLD with 67% confidence. Explain the evidence."
        ),
    },
]

prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256)
answer = tokenizer.decode(
    outputs[0][inputs["input_ids"].shape[1]:],
    skip_special_tokens=True,
)
print(answer.strip())

Load the Arakandar LoRA adapter

The specialized adapter is stored separately, for example:

Timothyemmanuel/Arakandar

from peft import PeftModel

adapter_id = "Timothyemmanuel/Arakandar"
model = PeftModel.from_pretrained(model, adapter_id, is_trainable=False)

References

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Safetensors

Model size

3B params

Tensor type

BF16