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
File size: 1,261 Bytes
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"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": [
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"full_attention",
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"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
} |