Vikasit 8B
Strong mid-range. Solid coding, analysis, and content generation.
Overview
Vikasit 8B is a strong mid-range generalist — coding, analysis, and content generation with reliable instruction following. The sweet spot for single-GPU deployment.
Specifications
- Total parameters
- 8.2B
- Architecture
- Dense transformer
- Layers
- 36
- Attention
- GQA (32 query / 8 KV heads)
- Context window
- 32K native, 131K via YaRN
- Vocabulary
- 151,669
- Modalities
- Text in → text out
- License
- Apache 2.0
Capabilities
- Solid coding and code review
- Document analysis and content generation
- 131K extended context (YaRN)
- Thinking and non-thinking modes
Benchmarks
| Benchmark | Score |
|---|---|
| MMLU-Pro | 51.2 |
| GPQA-Diamond | 62.0 |
| AIME 2025 | 67.3 |
| MATH-500 | 97.4 |
| LiveCodeBench v5 | 57.5 |
| BFCL v3 | 68.1 |
| IFEval | 85.0 |
| HumanEval | N/A |
Instruct numbers from the Qwen3 Technical Report; MMLU-Pro is the base-model figure. Thinking-mode scores shown.
Hardware & deployment
| Precision | Memory |
|---|---|
| bf16 | ~16 GB |
| INT4 | ~5 GB |
Quick start
Vikasit 8B 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-8b",
messages=[
{"role": "user", "content": "Explain Vikasit 8B in one sentence."}
],
)
print(resp.choices[0].message.content)Limitations
- Reasoning below 14B+ on hardest tasks
- Single-GPU throughput limits high concurrency
Vikasit 8B FAQ
How much does Vikasit 8B cost?
Vikasit 8B is an open-weight model built on Qwen3-8B (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 8B open weight?
Yes. Vikasit 8B is built on Qwen3-8B (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 8B?
Because Vikasit 8B is open weight, you self-host it with any OpenAI-compatible inference server (such as vLLM or SGLang) loaded with the Qwen3-8B (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 8B support?
Vikasit 8B supports a 32K native, 131K via YaRN context window. It is a 8.2B Dense transformer model — full specifications are listed in the table above.
License & attribution
Apache 2.0
Built on Qwen3-8B (Apache 2.0). Upstream copyright, license, and attribution notices are retained.