rasyosef/gemma-4-E2B-it-dspark-GGUF overview
gemma 4 E2B it dspark GGUF GGUF build of rasyosef/gemma 4 E2B it dspark https://huggingface.co/rasyosef/gemma 4 E2B it dspark for llama.cpp DSpark speculative …
Runs locally from ~280.9 MB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).
Repository Files & Downloads
Model Details
| Model ID | rasyosef/gemma-4-E2B-it-dspark-GGUF |
|---|---|
| Author | rasyosef |
| Pipeline | text-generation |
| License | apache-2.0 |
| Base model | rasyosef/gemma-4-E2B-it-dspark |
| Last modified | 2026-10-19T01:01:58.000Z |
Model README
---
library_name: llama.cpp
base_model: rasyosef/gemma-4-E2B-it-dspark
base_model_relation: quantized
license: apache-2.0
pipeline_tag: text-generation
tags:
- speculative-decoding
- dspark
- gemma-4
- draft-model
- gguf
---
gemma-4-E2B-it-dspark-GGUF
GGUF build of rasyosef/gemma-4-E2B-it-dspark for llama.cpp (DSpark speculative decoding is in mainline, ggml-org/llama.cpp #25173).
This is a standalone draft sidecar: it carries only the drafter (5 attention layers, rank-256 Markov head, confidence head, block size 8, 50,000-token draft vocabulary). Token embeddings and the LM head are shared from the target model at load time, so it must be paired with a gemma-4-E2B-it GGUF target file, for example one from unsloth/gemma-4-E2B-it-GGUF.
📦 Files
| file | quant | size | notes |
|---|---|---:|---|
| gemma-4-E2B-it-dspark-BF16.gguf | BF16 | 540 MB | native training precision, recommended when memory allows |
| gemma-4-E2B-it-dspark-F16.gguf | F16 | 540 MB | same size as BF16, for backends without good BF16 support |
| gemma-4-E2B-it-dspark-Q8_0.gguf | Q8_0 | 295 MB | smallest file, for tighter memory budgets |
Choose the draft quant by memory budget. The target model quant is the main speed/quality lever and is independent of this file.
🏃 How to run (llama.cpp)
llama-server -hf unsloth/gemma-4-E2B-it-GGUF:Q8_0 \
-hfd rasyosef/gemma-4-E2B-it-dspark-GGUF:Q8_0 \
--spec-type draft-dspark --spec-draft-n-max 8 --spec-draft-n-min 0 \
-fa on -ngl 99
The block size is read from the sidecar metadata (n-max is clamped to it). Speculative decoding is exact: the target verifies every proposed token, so greedy output equals the target alone; per-response timings report draft_n / draft_n_accepted.
Other models in the DSpark GGUF family:
| Draft (GGUF) | Target |
|---|---|
| Llama-3.2-1B-Instruct-DSpark-GGUF | Llama-3.2-1B-Instruct |
| Llama-3.2-3B-Instruct-DSpark-GGUF | Llama-3.2-3B-Instruct |
| Qwen3-1.7B-DSpark-GGUF | Qwen3-1.7B |
| Qwen3.5-2B-DSpark-GGUF | Qwen3.5-2B |
| gemma-4-E2B-it-dspark-GGUF | gemma-4-E2B-it |
| Phi-4-mini-instruct-DSpark-GGUF | Phi-4-mini-instruct |
📊 Acceptance and benchmarks
See rasyosef/gemma-4-E2B-it-dspark for acceptance-length metrics and training details.
Run rasyosef/gemma-4-E2B-it-dspark-GGUF with guIDE
Download guIDE — the AI-native code editor with local LLM inference and 69 built-in tools.
Source: Hugging Face · Compare models