Sharp-Spark-X2.5-4B-GGUF
Dynamic imatrix GGUF quants of XHToken/Spark-X2.5-4B, a 4B dense long-context model, carrying our cyber-and-coding-weighted importance matrix, a heuristic per-tensor bit allocation, and the Sharp-Spark chat template.
Params
4.0 B
Context
—
Downloads 30d
670 K
Likes
142
Download history
daily snapshots · 19 days670 K198 K
Oct 5Oct 11Oct 17Oct 23
Can you run it?
Estimated VRAM at 8K context unless noted. Pick your hardware to see the verdict per quantization.
| File | Quant | Size | Est. VRAM | Verdict on RTX 4090 · 24 GB |
|---|---|---|---|---|
| Spark-X2.5-4B-cyber.imatrix.gguf | GGUF | 0.0 GB | 1.1 GB | ✅ Runs comfortably |
| Sharp-Spark-X2.5-4B-Q4_K_XL.gguf | Q4_K_XL | 2.7 GB | 4.0 GB | ✅ Runs comfortably |
| Sharp-Spark-X2.5-4B-Q5_K_XL.gguf | Q5_K_XL | 3.2 GB | 4.7 GB | ✅ Runs comfortably |
| Sharp-Spark-X2.5-4B-Q6_K_XL.gguf | Q6_K_XL | 3.6 GB | 5.1 GB | ✅ Runs comfortably |
Estimate: file size × 1.1 + KV cache at 8K + 0.5 GB overhead. Not a benchmark — how we calculate this.
Run it
copy-paste, exact tags checked against the Hub$ ollama run sharp-spark-x2-5-4b-gguf # pin the quantization explicitly $ ollama run sharp-spark-x2-5-4b-gguf-q4_k_xl
$ huggingface-cli download peculiar-ragdoll/Sharp-Spark-X2.5-4B-GGUF-GGUF \
Sharp-Spark-X2.5-4B-Q4_K_XL.gguf --local-dir .
$ llama-cli -m Sharp-Spark-X2.5-4B-Q4_K_XL.gguf \
-c 8192 -ngl 99 -t 8 --color
$ curl -s https://aimodelscomparison.com/api/v1/models/sharp-spark-x2-5-4b-gguf
{
"hf_id": "peculiar-ragdoll/Sharp-Spark-X2.5-4B-GGUF",
"params_b": 4.00,
"context_length": null,
"license": { "id": "apache-2.0", "commercial": "yes" },
"downloads_30d": 670128,
"vram_estimates": [
{ "quant": "Q4_K_XL", "gb": 4.0 },
{ "quant": "Q5_K_XL", "gb": 4.7 }
],
"updated_at": "2026-10-05T01:00:22Z"
}
Specifications
- Parameters
- 4.0 B
- Licence
- apache-2.0
- First seen on the Hub
- 2026-09-03
- Training datasets
- undisclosed
- Added to our catalog
- 2026-10-05
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