Instructions to use yashmarathe/Eka-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yashmarathe/Eka-4B with Transformers:

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

pipe = pipeline("text-generation", model="yashmarathe/Eka-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("yashmarathe/Eka-4B")
model = AutoModelForCausalLM.from_pretrained("yashmarathe/Eka-4B")
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 yashmarathe/Eka-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yashmarathe/Eka-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yashmarathe/Eka-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/yashmarathe/Eka-4B

SGLang

How to use yashmarathe/Eka-4B 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 "yashmarathe/Eka-4B" \
    --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": "yashmarathe/Eka-4B",
		"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 "yashmarathe/Eka-4B" \
        --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": "yashmarathe/Eka-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use yashmarathe/Eka-4B with Docker Model Runner:
```
docker model run hf.co/yashmarathe/Eka-4B
```

Eka-4B

Eka-4B is a 4-billion parameter language model optimised for mathematical reasoning and code. Despite its compact size, Eka-4B delivers performance competitive with or superior to significantly larger open-source models across math and code benchmarks.

Key Strengths

Strong Mathematical Reasoning — Capable of solving complex, multi-step problems through sustained and coherent reasoning within a single forward pass. Achieves reliable results on challenging benchmarks including AIME 2026 I, HMMT, and IMO-Answer-Bench.
Strong Coding — Achieves state-of-the-art results on LiveCodeBench-V6 and LiveCodeBench-Pro among models under 10B parameters.
Robust Preference Alignment — Achieves solid alignment performance on Arena-Hard-v2 and Multi-Challenge, outperforming same-scale and substantially larger models.

Benchmark Performance

Math

Benchmark	Qwen3-4B	Qwen3-8B	Qwen3-14B	Qwen3-32B	Qwen3-30B-A3B	Eka-4B
AIME 2026 I	81.46	70.42	76.46	75.83	87.30	87.40
HMMT Nov	68.33	48.33	56.67	57.08	71.25	77.92
IMO-Answer-Bench	48.00	36.56	41.81	43.94	54.34	53.38
GPQA	65.8	62.0	63.38	68.4	73.4	83.8
HLE (Text-only)	6.72	5.28	7.00	9.31	11.77	12.60

Code

Benchmark	Qwen3-4B	Qwen3-8B	Qwen3-14B	Qwen3-32B	Qwen3-30B-A3B	Eka-4B
Live-Code-Bench-V6	57.4	49.4	55.9	55.7	66.0	76.9
Live-Code-Bench-Pro-Easy	40.2	41.2	33.0	42.3	60.8	81.4
Live-Code-Bench-Pro-Medium	5.3	3.5	1.8	3.5	3.5	28.1

Alignment

Benchmark	Qwen3-4B	Qwen3-8B	Qwen3-14B	Qwen3-32B	Qwen3-30B-A3B	Eka-4B
Arena-Hard-v2	34.9	26.3	36.9	56.0	60.2	73.2
Multi-Challenge	41.14	36.30	36.97	38.72	49.40	52.21

Quickstart

Recommended Inference Hyperparameters

Parameter	Value
Temperature	0.6
Top-p	0.95
Repeat penalty	1.0
Max New Tokens	131072

Chat

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained(
    'yashmarathe/Eka-4B',
    use_fast=False,
    trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
    'yashmarathe/Eka-4B',
    torch_dtype='auto',
    device_map='auto',
    trust_remote_code=True,
)

messages = [{'role': 'user', 'content': 'Solve: find all integer solutions to x^2 + y^2 = z^2 with x, y, z > 0 and x < 10.'}]
prompt = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    tokenize=False,
)
input_ids = tokenizer(prompt, add_special_tokens=False, return_tensors='pt').input_ids
output_ids = model.generate(input_ids.to('cuda'), eos_token_id=166101)
response = tokenizer.decode(output_ids[0][len(input_ids[0]):], skip_special_tokens=True)
print(response)

vLLM (Recommended for Production)

vllm serve yashmarathe/Eka-4B \
  --port 8000 \
  --max-model-len 131072 \
  --gpu-memory-utilization 0.95 \
  --enable-chunked-prefill \
  --trust-remote-code

Limitations

While significant effort has been made to align the model's outputs with ethical and legal requirements, the model may occasionally produce unexpected, biased, or otherwise problematic outputs due to its probabilistic nature. Users are responsible for evaluating outputs before deployment in production systems.

License

Apache 2.0

Downloads last month: 5

Safetensors

Model size

4B params

Tensor type

BF16