Qwen3-1.7B-ShiningValiant3-bf16-mlx/README.md

language, library_name, pipeline_tag, tags, base_model, datasets, license

language	library_name	pipeline_tag	tags	base_model	datasets	license
en	mlx	text-generation	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	ValiantLabs/Qwen3-1.7B-ShiningValiant3	sequelbox/Celestia3-DeepSeek-R1-0528 sequelbox/Mitakihara-DeepSeek-R1-0528 sequelbox/Raiden-DeepSeek-R1	apache-2.0

Qwen3-1.7B-ShiningValiant3-bf16-mlx

This model Qwen3-1.7B-ShiningValiant3-bf16-mlx was converted to MLX format from ValiantLabs/Qwen3-1.7B-ShiningValiant3 using mlx-lm version 0.26.0.

Use with mlx

pip install mlx-lm

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)