---
language:
- en
library_name: mlx
pipeline_tag: text-generation
tags:
- esper
- esper-3.1
- esper-3
- valiant
- valiant-labs
- qwen
- qwen-3
- qwen-3-4b
- qwen3-4b-thinking-2507
- 4b
- reasoning
- code
- code-instruct
- python
- javascript
- dev-ops
- jenkins
- terraform
- ansible
- docker
- kubernetes
- helm
- grafana
- prometheus
- shell
- bash
- azure
- aws
- gcp
- cloud
- scripting
- powershell
- problem-solving
- architect
- engineer
- developer
- creative
- analytical
- expert
- rationality
- conversational
- chat
- instruct
- mlx
base_model: ValiantLabs/Qwen3-4B-Thinking-2507-Esper3.1
datasets:
- sequelbox/Titanium3-DeepSeek-V3.1-Terminus
- sequelbox/Tachibana3-Part1-DeepSeek-V3.1-Terminus
- sequelbox/Tachibana3-Part2-DeepSeek-V3.2
- sequelbox/Mitakihara-DeepSeek-R1-0528
license: apache-2.0
---

# Qwen3-4B-Thinking-2507-Esper3.1-qx86-hi-mlx

This model [Qwen3-4B-Thinking-2507-Esper3.1-qx86-hi-mlx](https://huggingface.co/Qwen3-4B-Thinking-2507-Esper3.1-qx86-hi-mlx) was
converted to MLX format from [ValiantLabs/Qwen3-4B-Thinking-2507-Esper3.1](https://huggingface.co/ValiantLabs/Qwen3-4B-Thinking-2507-Esper3.1)
using mlx-lm version **0.28.3**.

## Use with mlx

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

```python
from mlx_lm import load, generate

model, tokenizer = load("Qwen3-4B-Thinking-2507-Esper3.1-qx86-hi-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)
```