Downloads · 30 days
41
14% of all-time downloads
NandoG-AI/Nex-N2.5-mini-GGUF
Nex-N2.5-mini-GGUF is a text generation model from NandoG-AI. Use it when you need the model to write or continue text. It is set up for llama.cpp. The card lists the license as apache-2.0.
GGUF quantizations of nex-agi/Nex-N2.5-mini for use with llama.cpp and compatible runtimes (LM Studio, Open WebUI, KoboldCpp, etc.).
Downloads · 30 days
41
14% of all-time downloads
All-time downloads
284
Public
Repo size
21.2 GB
Likes
0
Public
Click a slice to open those files.
.gguf21.2 GB · 100%
From the Hugging Face model README
GGUF quantizations of nex-agi/Nex-N2.5-mini for use with llama.cpp and compatible runtimes (LM Studio, Open WebUI, KoboldCpp, etc.).
Multimodal (vision) support is included via a separate
mmprojprojector file — see the Multimodal Support section.
Nex-N2.5 is a next-generation family of agentic models designed for long-horizon reasoning and real-world task execution.
Building on the multimodal foundations of Nex-N2, the Nex-N2.5 family introduces significant improvements in:
Unlike traditional chat models, Nex-N2.5 is designed to continuously interact with environments, verify outcomes, self-correct, and execute complex multi-step workflows.
The family consists of:
The original model supports a native context length of 262,144 tokens (256K).
| Base model | nex-agi/Nex-N2.5-mini |
| Architecture | Qwen3.5 MoE |
| Parameters | 35B (MoE) |
| Context length | 262,144 tokens |
| License | Apache 2.0 |
| Format | GGUF |
| Runtime | llama.cpp |
| Modality | Text + Vision |
Primary use cases: coding, software development, autonomous agents, web automation, computer use, research, knowledge work, and multimodal tasks.
| Quant | Use case |
|---|---|
Q4_K_M | Best overall balance (start here) |
Q5_K_M | High-quality daily driver |
Q6_K | Near-lossless quality |
| Quant | Use case |
|---|---|
Q2_K | Lowest memory usage |
Q3_K_M | Budget systems |
Q4_K_M | Fastest recommended balance |
| Quant | Use case |
|---|---|
Q8_0 | Highest practical quality |
| File | Type |
|---|---|
Nex-N2.5-mini-Q2_K.gguf | Weights |
Nex-N2.5-mini-Q3_K_M.gguf | Weights |
Nex-N2.5-mini-Q4_K_M.gguf | Weights |
Nex-N2.5-mini-Q5_K_M.gguf | Weights |
Nex-N2.5-mini-Q6_K.gguf | Weights |
Nex-N2.5-mini-Q8_0.gguf | Weights |
mmproj-Nex-N2.5-mini-F16.gguf | Vision projector (optional) |
hf download <your-username>/Nex-N2.5-mini-GGUF \
Nex-N2.5-mini-Q4_K_M.gguf \
--local-dir ./Nex-N2.5-mini-GGUF
For vision, also download mmproj-Nex-N2.5-mini-F16.gguf.
This repository includes a vision projector for image-enabled inference:
mmproj-Nex-N2.5-mini-F16.gguf
Pass it alongside your chosen quant with --mmproj:
llama-server \
-m Nex-N2.5-mini-Q4_K_M.gguf \
--mmproj mmproj-Nex-N2.5-mini-F16.gguf \
--jinja \
-c 8192
Text-only usage does not require the projector.
Original Nex-AGI recommendation:
temperature = 0.7
top_p = 0.95
top_k = 40
For deterministic benchmarking (identical input → identical output):
temperature = 0.0
top_p = 1.0
top_k = 0
Nex-N2.5 supports multiple reasoning modes, usually exposed through your frontend's reasoning settings:
| Mode | Description |
|---|---|
none | Direct response generation |
medium | Adaptive reasoning |
high | Extended reasoning |
llama-cli \
-m Nex-N2.5-mini-Q4_K_M.gguf \
-c 8192 \
--jinja
llama-server \
-m Nex-N2.5-mini-Q4_K_M.gguf \
--jinja \
-c 8192 \
--host 0.0.0.0 \
--port 8080
Increase -c (context size) as memory allows — the model supports up to:
262144 tokens
Special thanks to the local AI community for testing and feedback.
If these GGUF quantizations are useful to you, please consider:
Happy local inferencing 🚀