Vikasit 2B
Edge-optimized. Multilingual, long context, on-device deployment.
Overview
Vikasit 2B is an edge-optimized model on a hybrid-attention MoE base — multilingual, 262K native context, and strong on-device performance. A capable assistant that fits in tight memory budgets.
Specifications
- Total parameters
- 2B total
- Architecture
- Hybrid MoE (Gated DeltaNet + sparse MoE)
- Layers
- 24
- Context window
- 262K native
- Modalities
- Text in → text out (multimodal-capable base)
- License
- Apache 2.0
Capabilities
- Multilingual on-device assistant
- 262K native context
- Strong instruction following for its size
- Thinking and non-thinking modes
Benchmarks
| Benchmark | Score |
|---|---|
| MMLU-Pro | 66.5 |
| GPQA-Diamond | 45.0 |
| IFEval | 61.2 |
| AIME 2025 | N/A |
| LiveCodeBench | N/A |
| MATH-500 | N/A |
Built on Qwen3.5-2B; numbers from the Qwen3.5-2B HuggingFace model card.
Hardware & deployment
| Precision | Memory |
|---|---|
| bf16 | ~4 GB |
| INT4 | ~1.3 GB |
Quick start
Vikasit 2B is an open-weight model. Self-host it with any OpenAI-compatible inference server and call it with the OpenAI SDK as shown below.
# pip install openai
import os
from openai import OpenAI
client = OpenAI(
base_url="http://localhost:8000/v1",
api_key="sk-local", # self-hosted servers accept any token
)
resp = client.chat.completions.create(
model="vikasit-2b",
messages=[
{"role": "user", "content": "Explain Vikasit 2B in one sentence."}
],
)
print(resp.choices[0].message.content)Limitations
- Reasoning below 4B+ on hardest tasks
- Some standard benchmarks not published by base
Vikasit 2B FAQ
How much does Vikasit 2B cost?
Vikasit 2B is an open-weight model built on Qwen3.5-2B (Apache 2.0). Self-hosting the weights is free under the Apache 2.0 licence — you pay only for the hardware or cloud GPUs you run it on. Typical deployment fits the memory profiles listed in the hardware section above.
Is Vikasit 2B open weight?
Yes. Vikasit 2B is built on Qwen3.5-2B (Apache 2.0) and distributed under the Apache 2.0 licence, so the weights are openly available for self-hosting, fine-tuning, and commercial use, subject to the upstream licence terms.
How do I run Vikasit 2B?
Because Vikasit 2B is open weight, you self-host it with any OpenAI-compatible inference server (such as vLLM or SGLang) loaded with the Qwen3.5-2B (Apache 2.0) weights, then call it with the OpenAI SDK by setting the base URL to your own endpoint.
What context window does Vikasit 2B support?
Vikasit 2B supports a 262K native context window. It is a 2B total Hybrid MoE (Gated DeltaNet + sparse MoE) model — full specifications are listed in the table above.
License & attribution
Apache 2.0
Built on Qwen3.5-2B (Apache 2.0). Upstream copyright, license, and attribution notices are retained.