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---
base_model: Alfitaria/Q25-1.5B-VeoLu
library_name: peft
tags:
- mergekit
- merge
- llama-factory
- lora
- llama-cpp
- gguf-my-repo
datasets:
- allura-org/fujin-cleaned-stage-1
- Dampfinchen/Creative_Writing_Multiturn
- ToastyPigeon/SpringDragon
- allura-org/medquad_sharegpt
- allura-org/scienceqa_sharegpt
- Alignment-Lab-AI/orcamath-sharegpt
license: apache-2.0
---
# Triangle104/Q25-1.5B-VeoLu-Q5_K_S-GGUF
This model was converted to GGUF format from [`Alfitaria/Q25-1.5B-VeoLu`](https://huggingface.co/Alfitaria/Q25-1.5B-VeoLu) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Alfitaria/Q25-1.5B-VeoLu) for more details on the model.
---
Model details:
-
A source of life and hope for the land.
Q25-1.5B-Veo Lu is a tiny General-Purpose Creative model, made up of a merge of bespoke finetunes on Qwen 2.5-1.5B-Instruct.
Inspired by the success of MN-12B-Mag Mell and MS-Meadowlark-22B, Veo Lu was trained on a healthy, balanced diet of of Internet fiction, roleplaying, adventuring, and reasoning/general knowledge.
The components of Veo Lu are:
Bard (pretrain, writing): Fujin (Cleaned/extended Rosier)
Scribe (pretrain, roleplay): Creative Writing Multiturn
Cartographer (pretrain, adventuring): SpringDragon
Alchemist (SFT, science/reasoning): ScienceQA, MedquadQA, Orca Math Word Problems
This model is capable of carrying on a scene without going completely off the rails. That being said, it only has 1.5B parameters. So please, for the love of God, manage your expectations. Since it's Qwen, use ChatML formatting. Turn the temperature down to ~0.7-0.8 and try a dash of rep-pen.
GGUFs coming soon, but honestly, the full-precision model is 3.5GB in size. You might wanna have a go at running this unquantized with vLLM.
pip install vllm
vllm serve Alfitaria/Q25-1.5B-VeoLu --max-model-len 16384 --max-num-seqs 1
Made by inflatebot.
Special thanks to our friends at Allura, and especially to Auri, who basically held my hand through the whole process. Her effort and enthusiasm carried this project forward.
Configuration
The following YAML configuration was used to produce this model:
base_model: Qwen/Qwen2.5-1.5B-Instruct
dtype: bfloat16
merge_method: task_arithmetic
parameters:
normalize: 1.0
slices:
- sources:
- layer_range: [0, 28]
model: /home/asriel/AI/text/models/bard
parameters:
weight: 1.0
- layer_range: [0, 28]
model: /home/asriel/AI/text/models/scribe
parameters:
weight: 1.0
- layer_range: [0, 28]
model: /home/asriel/AI/text/models/cartographer
parameters:
weight: 1.0
- layer_range: [0, 28]
model: /home/asriel/AI/text/models/alchemist
parameters:
weight: 1.0
- layer_range: [0, 28]
model: Qwen/Qwen2.5-1.5B-Instruct
---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/Q25-1.5B-VeoLu-Q5_K_S-GGUF --hf-file q25-1.5b-veolu-q5_k_s.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/Q25-1.5B-VeoLu-Q5_K_S-GGUF --hf-file q25-1.5b-veolu-q5_k_s.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/Q25-1.5B-VeoLu-Q5_K_S-GGUF --hf-file q25-1.5b-veolu-q5_k_s.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/Q25-1.5B-VeoLu-Q5_K_S-GGUF --hf-file q25-1.5b-veolu-q5_k_s.gguf -c 2048
``` |