metadata

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 using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card 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)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

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:

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 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"

./llama-server --hf-repo Triangle104/Qwen3-1.7B-ShiningValiant3-Q8_0-GGUF --hf-file qwen3-1.7b-shiningvaliant3-q8_0.gguf -c 2048