Feature Extraction
MLX
Safetensors
bidirectional_pplx_qwen3
apple-silicon
sentence-similarity
mteb
perplexity
qwen3
custom_code
Instructions to use agentmish/pplx-embed-v1-4b-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use agentmish/pplx-embed-v1-4b-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir pplx-embed-v1-4b-mlx agentmish/pplx-embed-v1-4b-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
fix: use inputs_embeds in create_causal_mask (deprecation fix)
Browse files- modeling.py +2 -2
modeling.py
CHANGED
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@@ -61,7 +61,7 @@ class PPLXQwen3Model(Qwen3Model):
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attention_mask = {
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"full_attention": create_causal_mask(
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config=self.config,
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-
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attention_mask=attention_mask,
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cache_position=dummy_cache_position,
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past_key_values=None,
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@@ -80,4 +80,4 @@ class PPLXQwen3Model(Qwen3Model):
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cache_position=cache_position,
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**kwargs,
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)
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-
return outputs
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attention_mask = {
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"full_attention": create_causal_mask(
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config=self.config,
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+
inputs_embeds=inputs_embeds,
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attention_mask=attention_mask,
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cache_position=dummy_cache_position,
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past_key_values=None,
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cache_position=cache_position,
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**kwargs,
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)
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+
return outputs
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