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btl-4-compact
BTL-4 Compact is Bad Theory Labs' text-only 35B mixture-of-experts model
compressed into a single 9.96 GB IQ2_XXS GGUF. Around 2.1B parameters are
active per token, and the model is tuned for agentic work, tool use, coding,
and reasoning. The compact build omits the vision tower and disables the
source model's MTP layer for compatibility with stock llama.cpp.
Repository: localaiLicense: apache-2.0
deepseek-v4-pro-0813
DeepSeek V4 Pro 0813 is DeepSeek's MIT-licensed flagship mixture-of-experts
model for agentic coding, reasoning, and long-horizon tool use. This entry
uses Unsloth's UD-Q4_K_XL GGUF build, split into 20 shards for llama.cpp.
Repository: localaiLicense: mit
pocket-35b
POCKET-35B is an Apache-2.0 Qwen3.5-family mixture-of-experts model from
FINAL-Bench/VIDRAFT, derived from Darwin-36B-Opus and packaged for stock
llama.cpp. This entry uses the quality-oriented Q4_K_M GGUF quantization.
Repository: localaiLicense: apache-2.0
pocket-35b-q3
POCKET-35B is an Apache-2.0 Qwen3.5-family mixture-of-experts model from
FINAL-Bench/VIDRAFT, derived from Darwin-36B-Opus and packaged for stock
llama.cpp. This entry uses the balanced Q3_K_M GGUF quantization.
Repository: localaiLicense: apache-2.0
pocket-35b-q2
POCKET-35B is an Apache-2.0 Qwen3.5-family mixture-of-experts model from
FINAL-Bench/VIDRAFT, derived from Darwin-36B-Opus and packaged for stock
llama.cpp. This entry uses the smaller Q2_K GGUF quantization.
Repository: localaiLicense: apache-2.0
pocket-35b-iq1
POCKET-35B is an Apache-2.0 Qwen3.5-family mixture-of-experts model from
FINAL-Bench/VIDRAFT, derived from Darwin-36B-Opus and packaged for stock
llama.cpp. This entry uses the most compact IQ1_M GGUF quantization.
Repository: localaiLicense: apache-2.0
pocket-26b
POCKET-26B is an Apache-2.0 Gemma 4 26B-A4B mixture-of-experts model from
FINAL-Bench/VIDRAFT, tuned for Korean and packaged for stock llama.cpp.
This entry uses the quality-oriented Q4_K_M GGUF quantization.
Repository: localaiLicense: apache-2.0
pocket-26b-q2
POCKET-26B is an Apache-2.0 Gemma 4 26B-A4B mixture-of-experts model from
FINAL-Bench/VIDRAFT, tuned for Korean and packaged for stock llama.cpp.
This entry uses the smaller Q2_K GGUF quantization.
Repository: localaiLicense: apache-2.0
qwen3.6-35b-a3b-uncensored-genesis-hermes-v6
Qwen3.6-35B-A3B Uncensored Genesis Hermes V6 is LuffyTheFox's multimodal,
agentic derivative of HauhauCS's uncensored Qwen3.6-35B-A3B model. It
combines Genesis tensor calibration with Hermes function-calling data while
retaining the 35B mixture-of-experts architecture, roughly 3B active
parameters per token, and the native 262K-token context window.
This entry installs the Q8_0 GGUF together with its F16 multimodal projector
for llama.cpp. The model card recommends Jinja chat templates and at least a
128K context for its thinking behavior. License: Apache-2.0.
Repository: localaiLicense: apache-2.0
qwythos-27b-v1
Qwythos-27B-v1 is an Apache-2.0 dense 27B reasoning and agentic model
derived from Qwen3.5-27B. It supports tool use, vision through the included
projector, and a one-million-token context window. This entry uses the
recommended Q4_K_M GGUF quantization; an MTP-enabled build is available as
a variant for hosts with recent llama.cpp support.
Repository: localaiLicense: apache-2.0
minicpm5-1b-claude-opus-fable5-v2-thinking
# MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking
GGUF quantizations for local deployment: **MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-GGUF**
中文说明
**MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking** is a compact 1B **Thinking** language model built on openbmb/MiniCPM5-1B. Compared with V1, this V2 release is further fine-tuned on **Fable 5** data with a stronger focus on **tool calling / function calling**, while also improving **coding** and **instruction-following**. It keeps MiniCPM5's native Thinking chat template and XML tool-call format.
Previous version: **MiniCPM5-1B-Claude-Opus-Fable5-Thinking** (V1)
For llama.cpp / Ollama / LM Studio deployment, see the **GGUF repository**.
## Overview
## Capabilities
- **Tool calling (enhanced in V2)** — more reliable XML / function-calling style tool use on top of MiniCPM5's native format
- **Coding** — code generation, debugging, and software-engineering-style tasks
- **Instruction following** — more reliable adherence to user prompts and structured constraints
- **Thinking mode** — chain-of-thought reasoning via the MiniCPM5 chat template
- **Long context** — up to **128K tokens** (131,072 tokens per `config.json`)
...
Repository: localaiLicense: apache-2.0
bonsai-8b-1bit
Bonsai 8B (PrismML) is an end-to-end 1-bit language model built on the Qwen3-8B dense
architecture (GQA, SwiGLU, RoPE, RMSNorm, 36 layers, 65,536 context). Every weight is a
single sign bit (`-scale` / `+scale`) with one FP16 scale per group of 128 weights, for
an effective 1.125 bits/weight and a ~1.15 GB footprint (14.2x smaller than FP16) while
matching full-precision 8B instruct models at ~70.5 average across 6 benchmark
categories.
The Q1_0 quantization is only decodable by the PrismML llama.cpp fork, so this entry
runs on LocalAI's `bonsai` backend (that fork), not the stock `llama-cpp` backend.
License: Apache 2.0.
Repository: localaiLicense: apache-2.0
ternary-bonsai-8b
Ternary Bonsai 8B (PrismML) is a 1.58-bit ternary language model on the Qwen3-8B dense
architecture. Each weight takes a value from {-1, 0, +1} with one shared FP16 scale per
group of 128 weights (GGUF Q2_0, ~2.18 GB deployed, 7.5x smaller than FP16). The extra
zero state recovers more of the full-precision model than the 1-bit build: it ranks 2nd
among compared 6-9B models at 75.5 average despite being ~1/8th their size.
Q2_0 is the recommended, ternary-lossless variant. The Q2_0 kernels are only in the
PrismML llama.cpp fork, so this runs on LocalAI's `bonsai` backend. License: Apache 2.0.
Repository: localaiLicense: apache-2.0
ternary-bonsai-8b-q2-g64
Ternary Bonsai 8B (PrismML), GGUF Q2_0 with group-64 packing (each FP16 scale shared
across 64 weights instead of 128). Slightly larger (~2.31 GB) but matches llama.cpp's
native 64-value Q2_0 block layout. Runs on LocalAI's `bonsai` backend. License: Apache 2.0.
Repository: localaiLicense: apache-2.0
bonsai-27b-1bit
Bonsai 27B (PrismML) is a full 27B-class reasoning model in end-to-end 1-bit weights,
derived from the Qwen3.6-27B hybrid-attention backbone (~75% linear attention, 262K
context). At a true 1.125 bits/weight it deploys in ~3.9 GB (~14.2x smaller than FP16)
while retaining 89.5% of FP16 intelligence across 15 thinking-mode benchmarks (math
91.66, coding 81.88). Ships an optional 4-bit vision tower (mmproj) for image input,
included here.
The Q1_0_g128 weights and hybrid-attention kernels are only in the PrismML llama.cpp
fork, so this runs on LocalAI's `bonsai` backend. A GPU is recommended. License: Apache 2.0.
Repository: localaiLicense: apache-2.0
ternary-bonsai-27b
Ternary Bonsai 27B (PrismML) is the quality-oriented operating point of the Bonsai 27B
family: full 27B-class reasoning in ternary {-1, 0, +1} weights on the Qwen3.6-27B
hybrid-attention backbone (262K context). At a true 1.71 bits/weight it deploys in
~7.2 GB (GGUF Q2_0_g128) and retains 95% of FP16 intelligence (80.49 average across 15
thinking-mode benchmarks) - a higher score than a conventional IQ2_XXS build at less
than two-thirds its footprint. Ships an optional 4-bit vision tower (mmproj), included.
The Q2_0 weights and hybrid-attention kernels are only in the PrismML llama.cpp fork,
so this runs on LocalAI's `bonsai` backend. A GPU is recommended. License: Apache 2.0.
Repository: localaiLicense: apache-2.0
ternary-bonsai-27b-q2-g64
Ternary Bonsai 27B (PrismML), GGUF Q2_0 with group-64 packing (~7.59 GB), matching
llama.cpp's native 64-value Q2_0 block layout, with the 4-bit vision tower (mmproj)
included. Runs on LocalAI's `bonsai` backend. License: Apache 2.0.
Repository: localaiLicense: apache-2.0
minicpm5-1b-claude-opus-fable5-thinking
# MiniCPM5-1B-Claude-Opus-Fable5-Thinking
GGUF quantizations for local deployment: **MiniCPM5-1B-Claude-Opus-Fable5-Thinking-GGUF**
中文说明
**MiniCPM5-1B-Claude-Opus-Fable5-Thinking** is a compact 1B **Thinking** language model built on openbmb/MiniCPM5-1B. It is further fine-tuned on **Fable 5** data to improve **coding** and **instruction-following** while keeping MiniCPM5's native Thinking chat template and tool-call format.
For llama.cpp / Ollama / LM Studio deployment, see the **GGUF repository**.
## Overview
## Capabilities
- **Coding** — code generation, debugging, and software-engineering-style tasks
- **Instruction following** — more reliable adherence to user prompts and structured constraints
- **Thinking mode** — chain-of-thought reasoning via the MiniCPM5 chat template
- **Tool calling** — inherits MiniCPM5's XML tool-call format
- **Long context** — up to **128K tokens** (131,072 tokens per `config.json`)
## Quick start
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking"
...
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
laguna-xs-2.1-apex-i-quality
Laguna XS 2.1 in the 21.8 GB APEX-I Quality format. This is the highest-fidelity importance-matrix APEX build for llama.cpp.
License: OpenMDW 1.1.
Repository: localaiLicense: other
laguna-xs-2.1-apex-i-balanced
Laguna XS 2.1 in the 24.3 GB APEX-I Balanced format, an importance-matrix build balancing fidelity and memory use for llama.cpp.
License: OpenMDW 1.1.
Repository: localaiLicense: other
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