Text Generation
MLX
Safetensors
Chinese
English
qwen3_5
qwen3.5
8bit
code
smart-contracts
solidity
security
alice
conversational
8-bit precision
Instructions to use v102ss/Alice-Qwen3.5-9B-Code-v0-MLX-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use v102ss/Alice-Qwen3.5-9B-Code-v0-MLX-8bit with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("v102ss/Alice-Qwen3.5-9B-Code-v0-MLX-8bit") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
- MLX LM
How to use v102ss/Alice-Qwen3.5-9B-Code-v0-MLX-8bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "v102ss/Alice-Qwen3.5-9B-Code-v0-MLX-8bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "v102ss/Alice-Qwen3.5-9B-Code-v0-MLX-8bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "v102ss/Alice-Qwen3.5-9B-Code-v0-MLX-8bit", "messages": [ {"role": "user", "content": "Hello"} ] }'
Alice-Qwen3.5-9B-Code-v0-MLX-8bit
8-bit MLX build for the Alice 9B code/security lane.
Files
model-00001-of-00002.safetensorsmodel-00002-of-00002.safetensors- tokenizer and config files
Source
Converted from local HF fused BF16:
/Users/v/Documents/New project/models/hf/Alice-Qwen3.5-9B-Code-v0-HF-Fused-bf16
Quantization:
- MLX affine 8-bit
- group size 64
- effective bits per weight: about 8.501
Alice Profile
The model folder includes the Alice code/security chat template and corrected EOS handling for <|im_end|>.
Target use:
- Solidity
- TypeScript and Python utility work
- Foundry / Hardhat workflows
- smart-contract security review
- concise Chinese/English engineering answers
Local Smoke
Prompt:
你是谁?用一句话说你擅长什么。
Output:
我是 Alice,擅长用 Solidity、TypeScript 和 Python 写可审计合约、补丁漏洞、写测试和工程落地清单。
Observed on local MLX:
- Prompt: about 220 tok/s
- Generation: about 37 tok/s
- Peak memory: about 9.83 GB
- Downloads last month
- 140
Model size
9B params
Tensor type
BF16
·
U32 ·
Hardware compatibility
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8-bit