File size: 1,473 Bytes
498b819 7a9304a 498b819 2f72b7f 47c479e 2f72b7f 7a9304a 498b819 7a9304a 498b819 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 |
---
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct/blob/main/LICENSE
language:
- en
base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
- code
- codeqwen
- chat
- qwen
- qwen-coder
- mlx
---
# bobig/Qwen2.5-Coder-1.5B-Instruct-Q6
This works well as a draft model for speculative decoding in [LMstudio 3.10 beta](https://lmstudio.ai/docs/advanced/speculative-decoding)
Try it with: [mlx-community/Qwen2.5-14B-1M-YOYO-V2-Q4](https://huggingface.co/mlx-community/Qwen2.5-14B-1M-YOYO-V2-Q4)
you should see about 50% faster TPS for math/code prompts. For a quick test try: "count backwards from 100 to 1"
Q4 was a little too dumb, Q8 was a little too slow...so Q6
The Model [bobig/Qwen2.5-Coder-1.5B-Instruct-Q6](https://huggingface.co/bobig/Qwen2.5-Coder-1.5B-Instruct-Q6) was
converted to MLX format from [Qwen/Qwen2.5-Coder-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct)
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/Qwen2.5-Coder-1.5B-Instruct-Q6")
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)
```
|