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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 LM Studio with am17an's PR merged or custom llama.cpp runtime. As of 2026-05, mainline LM Studio runtime doesn't yet have `draft-mtp` for Gemma-4 — track upstream merge.)
## GGUF / local inference notes
...
Repository: localai
lfm2.5-1.2b-instruct
Try LFM • Docs • LEAP • Discord
# LFM2.5-1.2B-Instruct
LFM2.5 is a new family of hybrid models designed for **on-device deployment**. It builds on the LFM2 architecture with extended pre-training and reinforcement learning.
- **Best-in-class performance**: A 1.2B model rivaling much larger models, bringing high-quality AI to your pocket.
- **Fast edge inference**: 239 tok/s decode on AMD CPU, 82 tok/s on mobile NPU. Runs under 1GB of memory with day-one support for llama.cpp, MLX, and vLLM.
- **Scaled training**: Extended pre-training from 10T to 28T tokens and large-scale multi-stage reinforcement learning.
Find more information about LFM2.5 in our blog post.
## 🗒️ Model Details
LFM2.5-1.2B-Instruct is a general-purpose text-only model with the following features:
...
Repository: localaiLicense: other
lfm2.5-8b-a1b
Try LFM •
Docs •
LEAP •
Discord
# LFM2.5-8B-A1B
LFM2.5 is a new family of hybrid models designed for on-device deployment. It builds on the LFM2 architecture with extended pre-training and reinforcement learning.
- **On-device personal assistant**: Designed to power real-life applications, chaining tool calls, and following complex instructions on all devices.
- **Compressed performance**: Competitive with much larger dense and MoE models on instruction following and agentic tasks.
- **Unmatched throughput**: Fastest in its size class on both CPU and GPU inference, with day-one support for llama.cpp, MLX, vLLM, and SGLang.
Find more information about LFM2.5-8B-A1B in our blog post.
**AA-Omniscience Index (higher is better) rewards correct answers and penalizes hallucinations. Scores range from -100 to 100. See more results on Artificial Analysis.*
## 🗒️ Model Details
LFM2.5-8B-A1B is a general-purpose text-only model with the following features:
...
Repository: localaiLicense: other
qwen_qwen3.5-35b-a3b
Qwen3.5-35B-A3B is a quantized multimodal language model with 35B parameters using an A3B MoE architecture. It supports image-text understanding and chat interactions via llama-cpp backend.
Repository: localaiLicense: apache-2.0
qwen_qwen3.5-0.8b
Qwen 3.5 0.8B parameter model quantized for llama-cpp backend. Supports chat interactions and multimodal image-text inputs.
Repository: localaiLicense: apache-2.0
qwen_qwen3.5-2b
Qwen3.5-2B is a highly efficient, instruction-tuned multilingual language model available in various quantized GGUF formats. Optimized for llama-cpp inference, it supports chat and completion tasks with strong performance on low-RAM hardware. The model is available in multiple quantization levels ranging from Q8_0 to IQ2_M to balance quality and resource usage.
Repository: localaiLicense: apache-2.0
qwen_qwen3.5-4b
Qwen3.5-4B is a multimodal LLM with 4 billion parameters, optimized for chat and vision tasks. This GGUF quantized version enables efficient local inference via llama-cpp backend. Supports both text and image input for enhanced conversational capabilities.
Repository: localaiLicense: apache-2.0
qwen_qwen3-next-80b-a3b-thinking
Repository: localaiLicense: apache-2.0
acestep-cpp-turbo
ACE-Step 1.5 Turbo (C++ / GGML) — native C++ music generation from text descriptions and lyrics.
Two-stage pipeline: text-to-code (Qwen3 LM) + code-to-audio (DiT-VAE). Stereo 48kHz output.
Uses Q8_0 quantized models for a good balance of quality and speed.
Repository: localaiLicense: mit
acestep-cpp-turbo-4b
ACE-Step 1.5 Turbo (C++ / GGML) with 4B LM — higher quality music generation from text and lyrics.
Uses the larger 4B parameter LM for better metadata/code generation. Stereo 48kHz output.
Repository: localaiLicense: mit
vibevoice-cpp
VibeVoice Realtime 0.5B (C++ / GGML, Q8_0) - native C++ port of Microsoft VibeVoice
via the vibevoice-cpp backend. 24kHz mono TTS with voice cloning from a single
reference voice prompt. Default voice prompt: en-Carter_man.
Repository: localaiLicense: mit
vibevoice-cpp-asr
VibeVoice ASR 7B (C++ / GGML, Q4_K) - long-form speech-to-text with speaker
diarization. Returns per-speaker JSON segments with start/end timestamps.
English-only. ~10 GB download.
Repository: localaiLicense: mit
qwen3-tts-cpp
Qwen3-TTS 0.6B Base (C++ / GGML, qwentts.cpp). Native C++ text-to-speech with
streaming output and zero-shot voice cloning (set `voice` to a 24kHz reference
.wav). 24kHz mono, 11 languages with Mandarin dialects. Q8_0 (~0.95 GB talker).
Repository: localaiLicense: mit
qwen3-tts-cpp-0.6b-base-q4
Qwen3-TTS 0.6B Base (C++ / GGML, qwentts.cpp), Q4_K_M (~0.6 GB talker).
Streaming + voice cloning, 24kHz mono, 11 languages.
Repository: localaiLicense: mit
qwen3-tts-cpp-1.7b-base
Qwen3-TTS 1.7B Base (C++ / GGML, qwentts.cpp), Q8_0 (~2.0 GB talker).
Higher-quality streaming + voice cloning, 24kHz mono, 11 languages.
Repository: localaiLicense: mit
qwen3-tts-cpp-1.7b-base-q4
Qwen3-TTS 1.7B Base (C++ / GGML, qwentts.cpp), Q4_K_M (~1.2 GB talker).
Streaming + voice cloning, 24kHz mono, 11 languages.
Repository: localaiLicense: mit
qwen3-tts-cpp-customvoice
Qwen3-TTS 0.6B CustomVoice (C++ / GGML, qwentts.cpp), Q8_0. Named speakers
selected via the `voice` field: serena, vivian, uncle_fu, ryan, aiden,
ono_anna, sohee, eric (sichuan dialect), dylan (beijing dialect). Streaming,
24kHz mono, 11 languages.
Repository: localaiLicense: mit
qwen3-tts-cpp-customvoice-q4
Qwen3-TTS 0.6B CustomVoice (C++ / GGML, qwentts.cpp), Q4_K_M. Named speakers
via the `voice` field (serena, vivian, ryan, aiden, eric, dylan, ...).
Streaming, 24kHz mono, 11 languages.
Repository: localaiLicense: mit
qwen3-tts-cpp-1.7b-customvoice
Qwen3-TTS 1.7B CustomVoice (C++ / GGML, qwentts.cpp), Q8_0. Named speakers via
the `voice` field (serena, vivian, ryan, aiden, eric, dylan, ...). Streaming,
24kHz mono, 11 languages.
Repository: localaiLicense: mit
qwen3-tts-cpp-1.7b-customvoice-q4
Qwen3-TTS 1.7B CustomVoice (C++ / GGML, qwentts.cpp), Q4_K_M. Named speakers
via the `voice` field. Streaming, 24kHz mono, 11 languages.
Repository: localaiLicense: mit
qwen3-tts-cpp-1.7b-voicedesign
Qwen3-TTS 1.7B VoiceDesign (C++ / GGML, qwentts.cpp), Q8_0. Synthesises a
speaker from a free-text attribute instruction - REQUIRES the OpenAI
`instructions` field (e.g. "male, young adult, moderate pitch"); requests
without it are rejected. Streaming, 24kHz mono, 11 languages.
Repository: localaiLicense: mit
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