---
title: DeepScaleR-1.5B-Preview-Q8
canonical_url: "https://www.modelscope.cn/models/mlx-community/DeepScaleR-1.5B-Preview-Q8"
md_url: "https://www.modelscope.cn/models/mlx-community/DeepScaleR-1.5B-Preview-Q8.md"
repository: mlx-community/DeepScaleR-1.5B-Preview-Q8
last_updated: 2025-02-21
license: mit
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - qwen2
architectures:
  - Qwen2ForCausalLM
base_model:
  - agentica-org/DeepScaleR-1.5B-Preview
base_model_relation: quantized
parameters: 499.9M
tensor_type:
  - BF16
  - U32
library_name:
  - mlx
  - safetensors
  - pytorch
frameworks:
  - pytorch
language:
  - en
inference_backends:
  - "deploy_task text/emb"
  - "lmdeploy 0.9.1"
  - "lmdeploy_turbomind 0.9.1"
  - "sglang 0.5.2"
  - "vllm 0.9.2"
downloads: 610
stars: 0
tags:
  - mlx
---

# DeepScaleR-1.5B-Preview-Q8

> DeepScaleR-1.5B-Preview-Q8 - mlx-community 在 ModelScope 开源的模型。bobig/DeepScaleR-1.5B-Preview-Q8

mlx-community/DeepScaleR-1.5B-Preview-Q8 是 ModelScope 魔搭社区上的 499.9M 参数text-generation模型，采用 mit 许可，基于 agentica-org/DeepScaleR-1.5B-Preview 构建，可用 deploy_task text/emb、lmdeploy 0.9.1、lmdeploy_turbomind 0.9.1 部署。

- **Repository**: mlx-community/DeepScaleR-1.5B-Preview-Q8
- **License**: mit
- **Tasks**: text-generation
- **Parameters**: 499.9M
- **Base model**: agentica-org/DeepScaleR-1.5B-Preview
- **Inference backends**: deploy_task text/emb, lmdeploy 0.9.1, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2
- **Tags**: mlx
- **Downloads**: 610
- **Stars**: 0
- **Last updated**: 2025-02-21

Source: https://www.modelscope.cn/models/mlx-community/DeepScaleR-1.5B-Preview-Q8

---

# bobig/DeepScaleR-1.5B-Preview-Q8

The Model [bobig/DeepScaleR-1.5B-Preview-Q8](https://huggingface.co/bobig/DeepScaleR-1.5B-Preview-Q8) was
converted to MLX format from [agentica-org/DeepScaleR-1.5B-Preview](https://huggingface.co/agentica-org/DeepScaleR-1.5B-Preview)
using mlx-lm version **0.21.4**.

## Use with mlx

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

```python
from mlx_lm import load, generate

model, tokenizer = load("bobig/DeepScaleR-1.5B-Preview-Q8")

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)
```