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library_name: mlx
base_model: Tesslate/OmniCoder-9B
tags:
- qwen3.5
- code
- agent
- sft
- omnicoder
- tesslate
- mlx
license: apache-2.0
language:
- en
pipeline_tag: text-generation
model-index:
- name: OmniCoder-9B
results:
- task:
type: text-generation
dataset:
name: AIME 2025
type: custom
metrics:
- type: accuracy
value: 90.0
name: pass@5
- type: accuracy
value: 83.8
name: pass@1
- type: accuracy
value: 86.4
name: pass@3
- type: accuracy
value: 28.1
name: Pass Rate
---
# NexVeridian/OmniCoder-9B-4bit
This model [NexVeridian/OmniCoder-9B-4bit](https://huggingface.co/NexVeridian/OmniCoder-9B-4bit) was
converted to MLX format from [Tesslate/OmniCoder-9B](https://huggingface.co/Tesslate/OmniCoder-9B)
using mlx-lm version **0.31.2**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("NexVeridian/OmniCoder-9B-4bit")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_dict=False,
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
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