A 4B model is worth a closer look when a community agent run scores 94.6/100. Hugging Face community member userr99 shared a NanoBench result for our Spark-X2.6-4B-GGUF, using Hermes Agent as the harness. Thank you for putting our model to the test.
What the community reported
| Field | Reported result |
|---|---|
| Model | XHToken/Spark-X2.6-4B-GGUF |
| NanoBench score | 94.6 / 100 |
| Harness | Hermes Agent |
| Tasks solved | 9 / 12 |
| Scored time | 1h 8m |
| Run completed | October 29, 2026 |
Read the original community report · NanoBench site
Why this is exciting for a compact model
The shared screenshot shows Spark at 94.6, just 0.4 points below the listed 35B-A3B variant at 95.0. That is an encouraging score for a 4B model intended for on-device use. The community author also compares it with larger models; the screenshot should be read as the record of this particular submission.
Our practical strengths go beyond one score:
- A compact 4B model for local agents. Spark-X2.6-4B targets on-device deployment and workflows involving tools, planning, and code.
- GGUF distribution and documented local deployment paths. The model card covers llama.cpp, Ollama, and LM Studio, making it easier to choose a familiar local runtime. Follow the current setup instructions for the supported build and template.
- Apache-2.0 licensing. The model is available under a permissive open-source license.
- Configurable thinking budget. The model offers none, low, and high thinking levels so applications can tune reasoning effort.
These are reasons to try Spark on your own tasks, especially when you want a compact model you can run locally.
How to read this result
This is a community-reported run, not an independently reproduced evaluation by our team. NanoBench states in the screenshot that scores are client-reported and that its service checks result structure and score arithmetic, but cannot independently prove the local verifier ran. Spark solved 9/12 tasks; the 35B-A3B entry shown solved 11/12, and the listed harnesses differ. A close score here does not establish equivalent performance across workloads. The report does not identify the tested quantization or hardware, so we are not claiming a memory, speed, or cost advantage from this run.
Try it — and support open models with a Like
- Download the GGUF model and read the deployment guide.
- Test it with your own agent setup, then share your runtime, quantization, hardware, and task results in the community.
- If you like this direction, please tap ❤️ Like on the original Spark-X2.6-4B model page. Your support helps more developers discover our work and encourages us to keep building in the open.
Like this post and share it with someone building local agents. Thank you for supporting SparkLLM!
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