Instructions to use unsloth/LTX-2.3-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps Settings
- Unsloth Studio
How to use unsloth/LTX-2.3-GGUF 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 unsloth/LTX-2.3-GGUF 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 unsloth/LTX-2.3-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for unsloth/LTX-2.3-GGUF to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="unsloth/LTX-2.3-GGUF", max_seq_length=2048, )
What is wrong with my mmproj-*.gguf?
Can someone tell me please, how TF should I name my mmproj-BF16.gguf? I tried every name that came to my mind. Or what should I change?
Still I get this "clip missing" in log:
got prompt
VAE load device: cuda:0, offload device: cpu, dtype: torch.float32
VAE load device: cuda:0, offload device: cpu, dtype: torch.bfloat16
gguf qtypes: F32 (289), Q6_K (87), Q5_K (230), Q4_K (20)
Attempting to recreate sentencepiece tokenizer from GGUF file metadata...
Created tokenizer with vocab size of 262208
Dequantizing token_embd.weight to prevent runtime OOM.
clip missing: ['multi_modal_projector.mm_input_projection_weight', 'multi_modal_projector.mm_soft_emb_norm.weight', 'vision_model.embeddings.patch_embedding.weight', 'vision_model.embeddings.patch_embedding.bias', 'vision_model.embeddings.position_embedding.weight', 'vision_model.encoder.layers.0.layer_norm1.weight', 'vision_model.encoder.layers.0.layer_norm1.bias', 'vision_model.encoder.layers.0.self_attn.q_proj.weight', 'vision_model.encoder.layers.0.self_attn.k_proj.weight', 'vision_model.encoder.layers.0.self_attn.v_proj.weight', 'vision_model.encoder.layers.0.self_attn.out_proj.weight', 'vision_model.encoder.layers.0.layer_norm2.weight', 'vision_model.encoder.layers.0.layer_norm2.bias', 'vision_model.encoder.layers.0.mlp.fc1.weight',
...
Comfy and nodes are up to date.

