---
title: BigBang-v1-MLX-oQ4e
canonical_url: "https://www.modelscope.cn/models/mlx-community/BigBang-v1-MLX-oQ4e"
md_url: "https://www.modelscope.cn/models/mlx-community/BigBang-v1-MLX-oQ4e.md"
repository: mlx-community/BigBang-v1-MLX-oQ4e
last_updated: 2026-09-11
license: apache-2.0
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - qwen3_5_moe
architectures:
  - Qwen3_5MoeForConditionalGeneration
base_model:
  - endless-frontier/BigBang-v1
base_model_relation: quantized
parameters: 6.0B
tensor_type:
  - BF16
  - U32
library_name:
  - mlx
  - safetensors
  - pytorch
frameworks:
  - pytorch
downloads: 16
stars: 1
tags:
  - mlx
  - qwen3_5_moe
  - moe
  - oq4e
  - omlx
  - mixed-precision
---

# BigBang-v1-MLX-oQ4e

> BigBang-v1-MLX-oQ4e - mlx-community 在 ModelScope 开源的模型。This model was converted to MLX format from endless-frontier/BigBang-v1 using oMLX oQ quantization.

mlx-community/BigBang-v1-MLX-oQ4e 是 ModelScope 魔搭社区上的 6.0B 参数text-generation模型，采用 apache-2.0 许可，基于 endless-frontier/BigBang-v1 构建。

- **Repository**: mlx-community/BigBang-v1-MLX-oQ4e
- **License**: apache-2.0
- **Tasks**: text-generation
- **Parameters**: 6.0B
- **Base model**: endless-frontier/BigBang-v1
- **Tags**: mlx, qwen3_5_moe, moe, oq4e, omlx, mixed-precision
- **Downloads**: 16
- **Stars**: 1
- **Last updated**: 2026-09-11

Source: https://www.modelscope.cn/models/mlx-community/BigBang-v1-MLX-oQ4e

---

# BigBang-v1-MLX-oQ4e

This model was converted to MLX format from [endless-frontier/BigBang-v1](https://huggingface.co/endless-frontier/BigBang-v1) using `oMLX` oQ quantization.

**Quantization**: oQ4e (enhanced 4-bit, data-driven mixed-precision via calibration). Uses imatrix-weighted quantization with GPTQ-based weight optimization for maximum quality retention at ~4.6 bpw.

## Use with mlx-lm

```python
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/BigBang-v1-MLX-oQ4e")

messages = [{"role": "user", "content": "Hello!"}]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
response = generate(model, tokenizer, prompt=prompt, max_tokens=512)
```

Or via CLI:

```bash
python3 -m mlx_lm.chat --model mlx-community/BigBang-v1-MLX-oQ4e
```

## Use with oMLX

```bash
omlx import mlx-community/BigBang-v1-MLX-oQ4e
```

## Model Info

- **Architecture**: qwen3_5_moe — 35B total, 3B active, 256 experts (8/tok)
- **Attention**: Hybrid linear + full attention
- **Context**: 262K tokens
- **Type**: Text-only (language model)

## License

Apache 2.0 — Copyright 2026 Alibaba Cloud (original base model).