Text Generation
MLX
Safetensors
Transformers
English
qwen3
Merge
programming
code generation
code
coding
coder
chat
qwen
qwencoder
esper
esper-3
valiant
valiant-labs
qwen-3
qwen-3-2.4b
2.4b
reasoning
code-instruct
python
javascript
dev-ops
jenkins
terraform
scripting
powershell
azure
aws
gcp
cloud
problem-solving
architect
engineer
developer
creative
analytical
expert
rationality
conversational
instruct
shining-valiant
shining-valiant-3
qwen-3-1.7b
1.7b
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
float32
text-generation-inference
Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.4B-bf16-mlx
This model Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.4B-bf16-mlx was converted to MLX format from DavidAU/Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.4B using mlx-lm version 0.26.0.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("Qwen3-Shining-Valiant-Instruct-CODER-Reasoning-2.4B-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)
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