Qwen3-1.7B-ShiningValiant3/README.md

---
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
base_model: Qwen/Qwen3-1.7B
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)**


![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/63444f2687964b331809eb55/0-q6i_3FVjPg27esj9rNm.jpeg)

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), [Ministral-3-14B-Reasoning-2512](https://huggingface.co/ValiantLabs/Ministral-3-14B-Reasoning-2512-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/Qwen3-1.7B) 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/Qwen3-1.7B-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)
```


![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/63444f2687964b331809eb55/VCJ8Fmefd8cdVhXSSxJiD.jpeg)


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.