---
language:
- en
library_name: mlx
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
- mlx
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
---

# Qwen3-1.7B-ShiningValiant3-bf16-mlx

This model [Qwen3-1.7B-ShiningValiant3-bf16-mlx](https://huggingface.co/Qwen3-1.7B-ShiningValiant3-bf16-mlx) was
converted to MLX format from [ValiantLabs/Qwen3-1.7B-ShiningValiant3](https://huggingface.co/ValiantLabs/Qwen3-1.7B-ShiningValiant3)
using mlx-lm version **0.26.0**.

## Use with mlx

```bash
pip install mlx-lm
```

```python
from mlx_lm import load, generate

model, tokenizer = load("Qwen3-1.7B-ShiningValiant3-bf16-mlx")

prompt = "hello"

if tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)
```