Instructions to use aufklarer/Qwen3.5-0.8B-Chat-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use aufklarer/Qwen3.5-0.8B-Chat-MLX with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("aufklarer/Qwen3.5-0.8B-Chat-MLX") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use aufklarer/Qwen3.5-0.8B-Chat-MLX with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "aufklarer/Qwen3.5-0.8B-Chat-MLX" --prompt "Once upon a time"
- Atomic Chat
Download int8/config.json from aufklarer/Qwen3.5-0.8B-Chat-MLX: direct link, hf CLI and curl.
- Browser
- Download file 1.26 kB
-
https://huggingface.co/aufklarer/Qwen3.5-0.8B-Chat-MLX/resolve/main/int8/config.json
- Command line
-
hf download hf://aufklarer/Qwen3.5-0.8B-Chat-MLX/int8/config.json
-
curl -L -o config.json https://huggingface.co/aufklarer/Qwen3.5-0.8B-Chat-MLX/resolve/main/int8/config.json
1.26 kB
| { | |
| "hidden_size": 1024, | |
| "num_hidden_layers": 24, | |
| "num_attention_heads": 8, | |
| "num_key_value_heads": 2, | |
| "head_dim": 256, | |
| "intermediate_size": 3584, | |
| "vocab_size": 248320, | |
| "max_seq_len": 2048, | |
| "rope_theta": 10000000, | |
| "rms_norm_eps": 1e-06, | |
| "eos_token_id": 248046, | |
| "pad_token_id": 248044, | |
| "quantization": "int8", | |
| "quantization_bits": 8, | |
| "quantization_group_size": 64, | |
| "model_type": "qwen3_5_text", | |
| "layer_types": [ | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention" | |
| ], | |
| "full_attention_interval": 4, | |
| "linear_num_key_heads": 16, | |
| "linear_key_head_dim": 128, | |
| "linear_num_value_heads": 16, | |
| "linear_value_head_dim": 128, | |
| "linear_conv_kernel_dim": 4, | |
| "partial_rotary_factor": 0.25, | |
| "tie_word_embeddings": true | |
| } |