Image-Text-to-Text
Safetensors
English
Chinese
Korean
qwen3_5
unsloth
qwen
qwen3.5
reasoning
chain-of-thought
lora
competitive-programming
conversational
compressed-tensors
Instructions to use cpatonn/Qwopus3.5-27B-v3-AWQ-BF16-INT4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps Settings
- Unsloth Studio
How to use cpatonn/Qwopus3.5-27B-v3-AWQ-BF16-INT4 with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for cpatonn/Qwopus3.5-27B-v3-AWQ-BF16-INT4 to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for cpatonn/Qwopus3.5-27B-v3-AWQ-BF16-INT4 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for cpatonn/Qwopus3.5-27B-v3-AWQ-BF16-INT4 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="cpatonn/Qwopus3.5-27B-v3-AWQ-BF16-INT4", max_seq_length=2048, )
| default_stage: | |
| default_modifiers: | |
| AWQModifier: | |
| config_groups: | |
| group_0: | |
| targets: [Linear] | |
| weights: | |
| num_bits: 4 | |
| type: int | |
| symmetric: true | |
| group_size: 32 | |
| strategy: group | |
| block_structure: null | |
| dynamic: false | |
| actorder: null | |
| scale_dtype: null | |
| zp_dtype: null | |
| observer: mse | |
| observer_kwargs: {} | |
| input_activations: null | |
| output_activations: null | |
| format: null | |
| targets: [Linear] | |
| ignore: ['re:.*embed_tokens', 're:.*linear_attn.*', 're:model[.]visual.*', 're:mtp.*', | |
| lm_head] | |
| bypass_divisibility_checks: false | |
| mappings: | |
| - smooth_layer: re:model.*layers[.](3|7|11|15|19|23|27|31|35|39|43|47|51|55|59|63)[.]input_layernorm | |
| balance_layers: ['re:model.*layers[.](3|7|11|15|19|23|27|31|35|39|43|47|51|55|59|63)[.]self_attn[.]q_proj', | |
| 're:model.*layers[.](3|7|11|15|19|23|27|31|35|39|43|47|51|55|59|63)[.]self_attn[.]k_proj', | |
| 're:model.*layers[.](3|7|11|15|19|23|27|31|35|39|43|47|51|55|59|63)[.]self_attn[.]v_proj'] | |
| activation_hook_target: null | |
| balance_exponent: 1 | |
| - smooth_layer: re:model.*layers[.](3|7|11|15|19|23|27|31|35|39|43|47|51|55|59|63)[.]self_attn[.]v_proj | |
| balance_layers: ['re:model.*layers[.](3|7|11|15|19|23|27|31|35|39|43|47|51|55|59|63)[.]self_attn[.]o_proj'] | |
| activation_hook_target: null | |
| balance_exponent: 1 | |
| - smooth_layer: re:model.*post_attention_layernorm | |
| balance_layers: ['re:model.*mlp[.]gate_proj', 're:model.*mlp[.]up_proj'] | |
| activation_hook_target: null | |
| balance_exponent: 1 | |
| - smooth_layer: re:model.*mlp[.]up_proj | |
| balance_layers: ['re:model.*mlp[.]down_proj'] | |
| activation_hook_target: null | |
| balance_exponent: 1 | |
| duo_scaling: true | |
| n_grid: 20 | |