---
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- shining-valiant
- shining-valiant-3
- valiant
- valiant-labs
- qwen
- qwen-3
- qwen-3-1.7b
- 1.7b
- reasoning
- code
- code-reasoning
- science
- science-reasoning
- physics
- biology
- chemistry
- earth-science
- astronomy
- machine-learning
- artificial-intelligence
- compsci
- computer-science
- information-theory
- ML-Ops
- math
- cuda
- deep-learning
- transformers
- agentic
- LLM
- neuromorphic
- self-improvement
- complex-systems
- cognition
- linguistics
- philosophy
- logic
- epistemology
- simulation
- game-theory
- knowledge-management
- creativity
- problem-solving
- architect
- engineer
- developer
- creative
- analytical
- expert
- rationality
- conversational
- chat
- instruct
- llama-cpp
- gguf-my-repo
base_model: ValiantLabs/Qwen3-1.7B-ShiningValiant3
datasets:
- sequelbox/Celestia3-DeepSeek-R1-0528
- sequelbox/Mitakihara-DeepSeek-R1-0528
- sequelbox/Raiden-DeepSeek-R1
license: apache-2.0
---

# Triangle104/Qwen3-1.7B-ShiningValiant3-Q8_0-GGUF
This model was converted to GGUF format from [`ValiantLabs/Qwen3-1.7B-ShiningValiant3`](https://huggingface.co/ValiantLabs/Qwen3-1.7B-ShiningValiant3) 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/ValiantLabs/Qwen3-1.7B-ShiningValiant3) for more details on the model.

---
Shining Valiant 3 is a science, AI design, and general reasoning specialist built on Qwen 3.

- Finetuned on our newest science reasoning data generated with Deepseek R1 0528!
- AI to build AI: our high-difficulty AI reasoning data makes Shining Valiant 3 your friend for building with current AI tech and discovering new innovations and improvements!
- Improved general and creative reasoning to supplement problem-solving and general chat performance.
- Small model sizes allow running on local desktop and mobile, plus super-fast server inference!

---
## 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/Qwen3-1.7B-ShiningValiant3-Q8_0-GGUF --hf-file qwen3-1.7b-shiningvaliant3-q8_0.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/Qwen3-1.7B-ShiningValiant3-Q8_0-GGUF --hf-file qwen3-1.7b-shiningvaliant3-q8_0.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/Qwen3-1.7B-ShiningValiant3-Q8_0-GGUF --hf-file qwen3-1.7b-shiningvaliant3-q8_0.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/Qwen3-1.7B-ShiningValiant3-Q8_0-GGUF --hf-file qwen3-1.7b-shiningvaliant3-q8_0.gguf -c 2048
```