Text Generation
Transformers
Safetensors
English
qwen3
shining-valiant
shining-valiant-3
valiant
valiant-labs
qwen
qwen-3
qwen-3-8b
8b
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
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
text-generation-inference
| language: | |
| - en | |
| library_name: transformers | |
| pipeline_tag: text-generation | |
| tags: | |
| - shining-valiant | |
| - shining-valiant-3 | |
| - valiant | |
| - valiant-labs | |
| - qwen | |
| - qwen-3 | |
| - qwen-3-8b | |
| - 8b | |
| - 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 | |
| base_model: Qwen/Qwen3-8B | |
| datasets: | |
| - sequelbox/Celestia3-DeepSeek-R1-0528 | |
| - sequelbox/Mitakihara-DeepSeek-R1-0528 | |
| - sequelbox/Raiden-DeepSeek-R1 | |
| license: apache-2.0 | |
| **[Support our open-source dataset and model releases!](https://huggingface.co/spaces/sequelbox/SupportOpenSource)** | |
|  | |
| Shining Valiant 3: [Qwen3-1.7B](https://huggingface.co/ValiantLabs/Qwen3-1.7B-ShiningValiant3), [Qwen3-4B](https://huggingface.co/ValiantLabs/Qwen3-4B-ShiningValiant3), [Qwen3-8B](https://huggingface.co/ValiantLabs/Qwen3-8B-ShiningValiant3), [gpt-oss-20b](https://huggingface.co/ValiantLabs/gpt-oss-20b-ShiningValiant3) | |
| Shining Valiant 3 is a science, AI design, and general reasoning specialist built on Qwen 3. | |
| - Finetuned on our newest [science reasoning](https://huggingface.co/datasets/sequelbox/Celestia3-DeepSeek-R1-0528) data generated with [Deepseek R1 0528!](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) | |
| - AI to build AI: our [high-difficulty AI reasoning](https://huggingface.co/datasets/sequelbox/Mitakihara-DeepSeek-R1-0528) data makes Shining Valiant 3 your friend for building with current AI tech and discovering new innovations and improvements! | |
| - Improved [general and creative reasoning](https://huggingface.co/datasets/sequelbox/Raiden-DeepSeek-R1) to supplement problem-solving and general chat performance. | |
| - Small model sizes allow running on local desktop and mobile, plus super-fast server inference! | |
| ## Prompting Guide | |
| Shining Valiant 3 uses the [Qwen 3](https://huggingface.co/Qwen/Qwen3-8B) prompt format. | |
| Shining Valiant 3 is a reasoning finetune; **we recommend enable_thinking=True for all chats.** | |
| Example inference script to get started: | |
| ```python | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| model_name = "ValiantLabs/Qwen3-8B-ShiningValiant3" | |
| # load the tokenizer and the model | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| model_name, | |
| torch_dtype="auto", | |
| device_map="auto" | |
| ) | |
| # prepare the model input | |
| prompt = "Propose a novel cognitive architecture where the primary memory component is a Graph Neural Network (GNN). How would this GNN represent working, declarative, and procedural memory? How would the \"cognitive cycle\" be implemented as operations on this graph?" | |
| messages = [ | |
| {"role": "user", "content": prompt} | |
| ] | |
| text = tokenizer.apply_chat_template( | |
| messages, | |
| tokenize=False, | |
| add_generation_prompt=True, | |
| enable_thinking=True # Switches between thinking and non-thinking modes. Default is True. | |
| ) | |
| model_inputs = tokenizer([text], return_tensors="pt").to(model.device) | |
| # conduct text completion | |
| generated_ids = model.generate( | |
| **model_inputs, | |
| max_new_tokens=32768 | |
| ) | |
| output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() | |
| # parsing thinking content | |
| try: | |
| # rindex finding 151668 (</think>) | |
| index = len(output_ids) - output_ids[::-1].index(151668) | |
| except ValueError: | |
| index = 0 | |
| thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n") | |
| content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n") | |
| print("thinking content:", thinking_content) | |
| print("content:", content) | |
| ``` | |
|  | |
| Shining Valiant 3 is created by [Valiant Labs.](http://valiantlabs.ca/) | |
| [Check out our HuggingFace page to see all of our models!](https://huggingface.co/ValiantLabs) | |
| We care about open source. For everyone to use. | |