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gemma-4-e2b-it-artemis-q3-k-l
TurinTech Artemis Q3_K_L quantization of Gemma 4 E2B from Google's QAT
checkpoint, for local chat and tool calls. Accepts image input through
the bundled F16 vision projector. Uses the embedded chat template and
an 8192-token context. Thinking is on by default.
Repository: localaiLicense: apache-2.0
gemma-4-e2b-it-artemis-q3-k-m
TurinTech Artemis Q3_K_M quantization of Gemma 4 E2B from Google's QAT
checkpoint, for local chat and tool calls. Accepts image input through
the bundled F16 vision projector. Uses the embedded chat template and
an 8192-token context. Thinking is on by default.
Repository: localaiLicense: apache-2.0
gemma-4-e2b-it-artemis-iq3-xxs
TurinTech Artemis IQ3_XXS quantization of Gemma 4 E2B from Google's QAT
checkpoint, for local chat and tool calls. Accepts image input through
the bundled F16 vision projector. Uses the embedded chat template and
an 8192-token context. Thinking is on by default.
Repository: localaiLicense: apache-2.0
gemma-4-e2b-it-artemis-iq2-m
TurinTech Artemis IQ2_M quantization of Gemma 4 E2B from Google's QAT
checkpoint, for local chat and tool calls. Accepts image input through
the bundled F16 vision projector. Uses the embedded chat template and
an 8192-token context. Thinking is on by default.
Repository: localaiLicense: apache-2.0
gemma-4-e4b-it-artemis-q4-0
TurinTech Artemis Q4_0 quantization of Gemma 4 E4B from Google's QAT
checkpoint, for local chat, reasoning, and tool use. Accepts image
input through the bundled F16 vision projector. Uses the embedded chat
template and an 8192-token context. Thinking is on by default.
Repository: localaiLicense: apache-2.0
gemma-4-e4b-it-artemis-iq3-m
TurinTech Artemis IQ3_M quantization of Gemma 4 E4B from Google's QAT
checkpoint, for local chat, reasoning, and tool use. Accepts image
input through the bundled F16 vision projector. Uses the embedded chat
template and an 8192-token context. Thinking is on by default.
Repository: localaiLicense: apache-2.0
gemma-4-e4b-it-artemis-q3-k-m
TurinTech Artemis Q3_K_M quantization of Gemma 4 E4B from Google's QAT
checkpoint, for local chat, reasoning, and tool use. Accepts image
input through the bundled F16 vision projector. Uses the embedded chat
template and an 8192-token context. Thinking is on by default.
Repository: localaiLicense: apache-2.0
gemmable-4-12b-mtp
## Gemmable 4 12B
Gemmable 4 12B is a GGUF export of Gemma 4 12B fine-tuned on Fable-5 style
reasoning and assistant traces.
## Highlights
- Base model: `google/gemma-4-12B`
- Format: GGUF
- Training style: Fable-5 style reasoning and assistant traces
- Distribution: fp16 GGUF plus matching assistant GGUFs for each quant
- Intended use: local inference, coding, reasoning, and assistant workflows
## How to use
### llama.cpp
Standard load:
```bash
llama-server -m "gemmable-4-12b-fp16.gguf"
```
Speculative / draft-MTP load:
```bash
llama-server -m "gemmable-4-12b-Q4_K_M.gguf" \
--spec-draft-model "gemmable-4-12b-Q4_K_M-mtp.gguf" \
--spec-type draft-mtp \
--spec-draft-n-max 4
```
Use the matching fp16 or quantized main file with its `-mtp` companion.
### LM Studio
1. Search this repo, download target + mtp file.
2. Load target.
3. Load settings → Speculative Decoding → select mtp file file.
(Requires a llama.cpp runtime with Gemma 4 MTP support from ggml-org/llama.cpp#23398. LocalAI's pinned llama.cpp backend already carries it, so this entry runs draft-mtp out of the box.)
## GGUF / local inference notes
...
Repository: localai
gemma-4-12b-qat-hauhaucs-balanced
HauhauCS Balanced is an uncensored Gemma 4 12B model built from
quantization-aware-trained weights for chat, coding, and creative writing.
This Q4_K_M GGUF build includes the vision projector for image input.
Repository: localaiLicense: gemma
gemma-4-12b-qat-hauhaucs-balanced-mtp
HauhauCS Balanced is an uncensored Gemma 4 12B model built from
quantization-aware-trained weights for chat, coding, and creative writing.
This Q4_K_M GGUF build includes the vision projector for image input.
The MTP variant adds the publisher-provided draft head and enables
llama.cpp multi-token-prediction speculative decoding.
Repository: localaiLicense: gemma
gemma-4-12b-agentic-fable5-composer2.5-v2-3.5x-tau2
Hugging Face |
GitHub |
Launch Blog |
Documentation
License: Apache 2.0 | Authors: Google DeepMind
> [!Note]
> This model card is for the Gemma 4 12B Unified model, which is part of the Gemma 4 family of open models. Built with the same multimodal functionality as Gemma 4 E2B and E4B (text, audio, image, and video inputs), it brings native audio and vision understanding directly to local environments without the need for separate encoders. This unified approach to multimodality makes the model encoder-free, offering a deployment size that is perfect for consumer devices and streamlined local execution.
Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on E2B, E4B, and 12B) and generating text output. This release includes open-weights models in both pre-trained and instruction-tuned variants. Gemma 4 features a context window of up to 256K tokens and maintains multilingual support in over 140 languages.
...
Repository: localaiLicense: apache-2.0
gemma-4-12b-agentic-fable5-composer2.5-v2-3.5x-tau2-mtp
Gemma 4 12B Agentic v2 is an Apache-2.0 fine-tune for coding, terminal
work, multi-step tool use, and reasoning. This variant pairs the Q4_K_M
target with the upstream Q8_0 Gemma 4 MTP drafter for faster lossless
generation through llama.cpp speculative decoding.
Repository: localaiLicense: apache-2.0
gemma-4-12b-agentic-fable5-composer2.5-v2-3.5x-tau2-q8
Gemma 4 12B Agentic v2 is an Apache-2.0 fine-tune for coding, terminal
work, multi-step tool use, and reasoning. This variant uses the
near-lossless Q8_0 GGUF quantization for higher output fidelity.
Repository: localaiLicense: apache-2.0
gemma-4-12b-coder-fable5-composer2.5-v1
Hugging Face |
GitHub |
Launch Blog |
Documentation
License: Apache 2.0 | Authors: Google DeepMind
> [!Note]
> This model card is for the Gemma 4 12B Unified model, which is part of the Gemma 4 family of open models. Built with the same multimodal functionality as Gemma 4 E2B and E4B (text, audio, image, and video inputs), it brings native audio and vision understanding directly to local environments without the need for separate encoders. This unified approach to multimodality makes the model encoder-free, offering a deployment size that is perfect for consumer devices and streamlined local execution.
Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on E2B, E4B, and 12B) and generating text output. This release includes open-weights models in both pre-trained and instruction-tuned variants. Gemma 4 features a context window of up to 256K tokens and maintains multilingual support in over 140 languages.
...
Repository: localaiLicense: gemma
gemma-4-31b-fable-coder-q4
Gemma-4-31B-Fable-Coder is DavidrPatton's Gemma 4 31B fine-tune for
coding and visual interface tasks. This entry uses the Q4_K_M GGUF
with the F16 vision projector and a 32,768-token context.
Repository: localaiLicense: apache-2.0
gemma-4-31b-fable-coder-q8
Gemma-4-31B-Fable-Coder is DavidrPatton's Gemma 4 31B fine-tune for
coding and visual interface tasks. This entry uses the Q8_0 GGUF
with the F16 vision projector and a 32,768-token context.
Repository: localaiLicense: apache-2.0
gemma-4-31b-ortenzya-q4
Ortenzya is a Gemma 4 31B instruction-tuned model from llmfan46 for
creative writing, translation, and roleplay. This entry uses the Q4_K_M
GGUF with the BF16 vision projector and a 32,768-token context.
Repository: localaiLicense: apache-2.0
gemma-4-31b-ortenzya-q8
Ortenzya is a Gemma 4 31B instruction-tuned model from llmfan46 for
creative writing, translation, and roleplay. This entry uses the Q8_0
GGUF with the BF16 vision projector and a 32,768-token context.
Repository: localaiLicense: apache-2.0
gemma-4-31b-scotoma-2-q4
Gemma 4 31B Scotoma 2 is a multimodal Gemma 4 31B instruction-tuned model from ReadyArt. It applies a bounded refusal edit and preference training intended to reduce repetitive prose patterns while retaining the base model's text and image capabilities. This entry uses the 18.7 GB Q4_K_M GGUF and the matching Q8_0 vision projector.
License: Apache 2.0 | Base model: Google Gemma 4 31B IT
Repository: localaiLicense: apache-2.0
gemma-4-31b-scotoma-2-q8
Gemma 4 31B Scotoma 2 is a multimodal Gemma 4 31B instruction-tuned model from ReadyArt. It applies a bounded refusal edit and preference training intended to reduce repetitive prose patterns while retaining the base model's text and image capabilities. This higher-fidelity entry uses the 32.6 GB Q8_0 GGUF and the matching Q8_0 vision projector.
License: Apache 2.0 | Base model: Google Gemma 4 31B IT
Repository: localaiLicense: apache-2.0
gemma-4-26b-a4b-it-qat
Hugging Face |
GitHub |
Launch Blog |
Documentation
License: Apache 2.0 | Authors: Google DeepMind
> [!Note]
> This model card is for the new versions of the Gemma 4 family optimized with Quantization-Aware Training (QAT), which allows preserving similar quality to bfloat16 while dramatically reducing the memory requirements to load the model.
> Four versions of the QAT checkpoints are available:
> * **Unquantized QAT checkpoints** (Q4_0): Half-precision weights extracted from the QAT pipeline, ideal for custom downstream compilation and research. Available for Gemma 4 E2B, E4B, 12B, 26B A4B, and 31B, and their drafter models.
> * **GGUF** (Q4_0): Ready-to-deploy formats for broad ecosystem compatibility. Available for Gemma 4 E2B, E4B, 12B, 26B A4B, and 31B.
> * **Mobile-optimized** (wNa8o8): A custom schema engineered explicitly for mobile hardware efficiency. It features targeted 2-bit decoding layers, optimized KV caches, and static activations to maximize VRAM savings. Available for Gemma 4 E2B and E4B.
> * **Compressed Tensors** (w4a16): QAT checkpoints serialized in the compressed-tensors format for native, optimized inference with vLLM. Available for Gemma 4 E2B, E4B, 12B
...
Repository: localaiLicense: apache-2.0
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