Text Generation
MLX
Safetensors
English
qwen3
Merge
programming
code generation
code
coding
coder
chat
brainstorm
qwen
qwencoder
brainstorm20x
esper
esper-3
valiant
valiant-labs
qwen-3
qwen-3-4b
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
metadata
license: apache-2.0
base_model: DavidAU/Qwen3-Esper3-Reasoning-CODER-Instruct-6B-Brainstorm20x
language:
- en
pipeline_tag: text-generation
tags:
- merge
- programming
- code generation
- code
- coding
- coder
- chat
- brainstorm
- qwen
- qwen3
- qwencoder
- brainstorm20x
- esper
- esper-3
- valiant
- valiant-labs
- qwen-3
- qwen-3-4b
- 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
- mlx
datasets:
- sequelbox/Titanium2.1-DeepSeek-R1
- sequelbox/Tachibana2-DeepSeek-R1
- sequelbox/Raiden-DeepSeek-R1
library_name: mlx
Qwen3-Esper3-Reasoning-CODER-Instruct-6B-Brainstorm20x-q8-mlx
This model Qwen3-Esper3-Reasoning-CODER-Instruct-6B-Brainstorm20x-q8-mlx was converted to MLX format from DavidAU/Qwen3-Esper3-Reasoning-CODER-Instruct-6B-Brainstorm20x using mlx-lm version 0.26.0.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("Qwen3-Esper3-Reasoning-CODER-Instruct-6B-Brainstorm20x-q8-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)