Respan Dataset Explorer

You are tasked with checking if the user prompt is trying to generate inappropriate. Now, you'll be given the user's prompt. If it sounds inappropriate say "TRUE" and NOTHING ELSE, or you will mess up. Otherwise just say "FALSE" and nothing else. Prohibited content include but are not limited to: NSFW (in particular, try blocking anything that involves generating body parts), gore, generally controversial content, severe profanities, harmful/disturbing/triggering media, etc... Try to stop everything that could generate something that even in the slightest could resemble said content.
**USER PROMPT:**  quickster SpongeBob

FALSE

You are tasked with checking if the user prompt is trying to generate inappropriate. Now, you'll be given the user's prompt. If it sounds inappropriate say "TRUE" and NOTHING ELSE, or you will mess up. Otherwise just say "FALSE" and nothing else. Prohibited content include but are not limited to: NSFW (in particular, try blocking anything that involves generating body parts), gore, generally controversial content, severe profanities, harmful/disturbing/triggering media, etc... Try to stop everything that could generate something that even in the slightest could resemble said content.
**USER PROMPT:**  supah petting wink cat

FALSE

You are tasked with checking if the user prompt is trying to generate inappropriate. Now, you'll be given the user's prompt. If it sounds inappropriate say "TRUE" and NOTHING ELSE, or you will mess up. Otherwise just say "FALSE" and nothing else. Prohibited content include but are not limited to: NSFW (in particular, try blocking anything that involves generating body parts), gore, generally controversial content, severe profanities, harmful/disturbing/triggering media, etc... Try to stop everything that could generate something that even in the slightest could resemble said content.
**USER PROMPT:**  jamboi sitting on a tree alone

FALSE

I have an AI TTS open source app and one of the models has a config file

'the config file'
{
    "attention_o_bias": false,
    "attention_qk_norm": true,
    "attention_qkv_bias": false,
    "codebook_size": 4096,
    "dim": 1024,
    "dropout": 0.0,
    "fast_attention_o_bias": false,
    "fast_attention_qk_norm": false,
    "fast_attention_qkv_bias": true,
    "fast_dim": 1024,
    "fast_head_dim": 64,
    "fast_intermediate_size": 3072,
    "fast_n_head": 16,
    "fast_n_local_heads": 8,
    "head_dim": 128,
    "initializer_range": 0.03125,
    "intermediate_size": 3072,
    "max_seq_len": 8192,
    "model_type": "dual_ar",
    "n_fast_layer": 4,
    "n_head": 16,
    "n_layer": 28,
    "n_local_heads": 8,
    "norm_eps": 1e-06,
    "num_codebooks": 10,
    "rope_base": 1000000,
    "scale_codebook_embeddings": true,
    "tie_word_embeddings": false,
    "use_gradient_checkpointing": true,
    "vocab_size": 155776
}

I know nothing about these values but do you think you could adjust them for the fastest and most realistic responses?

Thanks for sharing the config file! This configuration is for a relatively large and complex transformer-like model, presumably used within your AI TTS app. Let me give a high-level overview first, then some suggestions on how to adjust for speed and quality trade-offs.

---

### What these config parameters roughly mean:
- **dim (1024)**: The dimensionality of the token embeddings and transformer hidden states. Higher = more capacity, slower computation.
- **n_head (16), head_dim (128)**: Number of attention heads & dimension per head.
- **n_layer (28)**: Number of transformer layers. More layers = deeper model = slower but potentially better quality.
- **intermediate_size (3072)**: Size of feed-forward layers; controls model capacity.
- **dropout (0.0)**: Probability of randomly zeroing units during training. Zero means no dropout.
- **max_seq_len (8192)**: Maximum input length.
- **fast_* parameters**: These probably relate to some optimized or faster attention layers.
- **use_gradient_checkpointing (true)**: Saves memory at the cost of extra computation.
- **vocab_size (155,776)**: Size of the vocabulary (tokens).
- **model_type ("dual_ar")**: Type of model architecture; not something easily tunable here.
- Various biases, normalization flags, etc.

---

### Goal: Faster and more realistic (quality) responses

- **Trade-off**: Generally, bigger models and longer sequences slow inference but can improve quality. Speed vs quality is a balance.
- **Where to optimize**: You can reduce model size (layers, dims), sequence max length, and disable expensive features if possible.
- **Hardware-dependent**: Some things may help or hurt depending on your hardware and framework support.

---

### Suggestions for faster response with reasonable quality:

| Parameter                   | Current | Suggested (for speed)                    | Notes                                                       |
|----------------------------|---------|----------------------------------------|-------------------------------------------------------------|
| **n_layer**                | 28      | 12-16                                  | Reducing layers greatly helps latency, 16 layers is still decent.|
| **dim**                   | 1024    | 768 or 640                             | Smaller hidden size reduces computation; 768 is popular.    |
| **n_head**                | 16      | Keep at 12 or reduce accordingly       | Heads usually go with dim/head_dim ratio. If dim=768, n_head=12 (head_dim=64). |
| **head_dim**              | 128     | 64                                    | To maintain same dim, reduce head_dim → n_head adjusted accordingly. |
| **intermediate_size**     | 3072    | ~ 2048                                | FFN size often 4x dim; scaling down moderately reduces quality but improves speed. |
| **max_seq_len**           | 8192    | Lower (e.g. 2048 or 4096)             | Shorter max length limit speeds up longer inputs.            |
| **dropout**               | 0.0     | Keep at 0.0                           | Dropout for training only, no effect on inference speed.     |
| **use_gradient_checkpointing** | true | false                               | Turn off during inference to reduce computation (used mainly for training). |
| **fast_attention_* flags** | various | Set fastest config depending on hardware | Depends on your optimized attention implementation, maybe enable fast_attention_o_bias etc if supported. |
| **codebook_size, num_codebooks, rope_base, etc** | leave as is | Leave as is | These are likely for vocab/compression and positional encoding; tuning may break functionality. |

---

### Example "faster" config (some tuning done):

```json
{
    "attention_o_bias": false,
    "attention_qk_norm": true,
    "attention_qkv_bias": false,
    "codebook_size": 4096,
    "dim": 768,
    "dropout": 0.0,
    "fast_attention_o_bias": true,
    "fast_attention_qk_norm": true,
    "fast_attention_qkv_bias": true,
    "fast_dim": 768,
    "fast_head_dim": 64,
    "fast_intermediate_size": 2048,
    "fast_n_head": 12,
    "fast_n_local_heads": 6,
    "head_dim": 64,
    "initializer_range": 0.03125,
    "intermediate_size": 2048,
    "max_seq_len": 4096,
    "model_type": "dual_ar",
    "n_fast_layer": 4,
    "n_head": 12,
    "n_layer": 16,
    "n_local_heads": 6,
    "norm_eps": 1e-06,
    "num_codebooks": 10,
    "rope_base": 1000000,
    "scale_codebook_embeddings": true,
    "tie_word_embeddings": false,
    "use_gradient_checkpointing": false,
    "vocab_size": 155776
}
```

---

### Additional notes:

- **Try inference with different batch sizes or fp16 mixed precision, if your framework supports it.** This can accelerate speed considerably.
- **If the model runs on GPU, ensure libraries (e.g. CUDA/cuDNN) are up to date and fast attention kernels are enabled where possible.**
- **If the TTS app pipeline includes vocoders or audio synthesis after model inference, optimize those too for end-to-end speedup.**
- **Test the outputs after tuning to confirm quality is acceptable for your use case.**

---

If you want me, I can help you gradually tune specific parameters or explain what each component does in more detail. Would you like that?

Exception in thread Thread-1 (worker):
Traceback (most recent call last):
  File "/home/meow/anaconda3/envs/fish-speech/lib/python3.12/threading.py", line 1075, in _bootstrap_inner
    self.run()
  File "/home/meow/anaconda3/envs/fish-speech/lib/python3.12/threading.py", line 1012, in run
    self._target(*self._args, **self._kwargs)
  File "/home/meow/fish-speech/fish_speech/models/text2semantic/inference.py", line 510, in worker
    model, decode_one_token = init_model(
                              ^^^^^^^^^^^
  File "/home/meow/fish-speech/fish_speech/models/text2semantic/inference.py", line 318, in init_model
    model = DualARTransformer.from_pretrained(checkpoint_path, load_weights=True)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/meow/fish-speech/fish_speech/models/text2semantic/llama.py", line 497, in from_pretrained
    err = model.load_state_dict(weights, strict=False, assign=True)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/meow/anaconda3/envs/fish-speech/lib/python3.12/site-packages/torch/nn/modules/module.py", line 2593, in load_state_dict
    raise RuntimeError(
RuntimeError: Error(s) in loading state_dict for DualARTransformer:
        size mismatch for embeddings.weight: copying a param with shape torch.Size([155776, 1024]) from checkpoint, the shape in current model is torch.Size([155776, 768]).
        size mismatch for codebook_embeddings.weight: copying a param with shape torch.Size([40960, 1024]) from checkpoint, the shape in current model is torch.Size([40960, 768]).
        size mismatch for layers.0.attention.wqkv.weight: copying a param with shape torch.Size([4096, 1024]) from checkpoint, the shape in current model is torch.Size([1536, 768]).
        size mismatch for layers.0.attention.wo.weight: copying a param with shape torch.Size([1024, 2048]) from checkpoint, the shape in current model is torch.Size([768, 768]).
        size mismatch for layers.0.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
        size mismatch for layers.0.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
        size mismatch for layers.0.feed_forward.w1.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2048, 768]).
        size mismatch for layers.0.feed_forward.w3.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2048, 768]).
        size mismatch for layers.0.feed_forward.w2.weight: copying a param with shape torch.Size([1024, 3072]) from checkpoint, the shape in current model is torch.Size([768, 2048]).
        size mismatch for layers.0.ffn_norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for layers.0.attention_norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for layers.1.attention.wqkv.weight: copying a param with shape torch.Size([4096, 1024]) from checkpoint, the shape in current model is torch.Size([1536, 768]).
        size mismatch for layers.1.attention.wo.weight: copying a param with shape torch.Size([1024, 2048]) from checkpoint, the shape in current model is torch.Size([768, 768]).
        size mismatch for layers.1.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
        size mismatch for layers.1.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
        size mismatch for layers.1.feed_forward.w1.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2048, 768]).
        size mismatch for layers.1.feed_forward.w3.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2048, 768]).
        size mismatch for layers.1.feed_forward.w2.weight: copying a param with shape torch.Size([1024, 3072]) from checkpoint, the shape in current model is torch.Size([768, 2048]).
        size mismatch for layers.1.ffn_norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for layers.1.attention_norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for layers.2.attention.wqkv.weight: copying a param with shape torch.Size([4096, 1024]) from checkpoint, the shape in current model is torch.Size([1536, 768]).
        size mismatch for layers.2.attention.wo.weight: copying a param with shape torch.Size([1024, 2048]) from checkpoint, the shape in current model is torch.Size([768, 768]).
        size mismatch for layers.2.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
        size mismatch for layers.2.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
        size mismatch for layers.2.feed_forward.w1.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2048, 768]).
        size mismatch for layers.2.feed_forward.w3.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2048, 768]).
        size mismatch for layers.2.feed_forward.w2.weight: copying a param with shape torch.Size([1024, 3072]) from checkpoint, the shape in current model is torch.Size([768, 2048]).
        size mismatch for layers.2.ffn_norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for layers.2.attention_norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for layers.3.attention.wqkv.weight: copying a param with shape torch.Size([4096, 1024]) from checkpoint, the shape in current model is torch.Size([1536, 768]).
        size mismatch for layers.3.attention.wo.weight: copying a param with shape torch.Size([1024, 2048]) from checkpoint, the shape in current model is torch.Size([768, 768]).
        size mismatch for layers.3.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
        size mismatch for layers.3.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
        size mismatch for layers.3.feed_forward.w1.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2048, 768]).
        size mismatch for layers.3.feed_forward.w3.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2048, 768]).
        size mismatch for layers.3.feed_forward.w2.weight: copying a param with shape torch.Size([1024, 3072]) from checkpoint, the shape in current model is torch.Size([768, 2048]).
        size mismatch for layers.3.ffn_norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for layers.3.attention_norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for layers.4.attention.wqkv.weight: copying a param with shape torch.Size([4096, 1024]) from checkpoint, the shape in current model is torch.Size([1536, 768]).
        size mismatch for layers.4.attention.wo.weight: copying a param with shape torch.Size([1024, 2048]) from checkpoint, the shape in current model is torch.Size([768, 768]).
        size mismatch for layers.4.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
        size mismatch for layers.4.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
        size mismatch for layers.4.feed_forward.w1.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2048, 768]).
        size mismatch for layers.4.feed_forward.w3.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2048, 768]).
        size mismatch for layers.4.feed_forward.w2.weight: copying a param with shape torch.Size([1024, 3072]) from checkpoint, the shape in current model is torch.Size([768, 2048]).
        size mismatch for layers.4.ffn_norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for layers.4.attention_norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for layers.5.attention.wqkv.weight: copying a param with shape torch.Size([4096, 1024]) from checkpoint, the shape in current model is torch.Size([1536, 768]).
        size mismatch for layers.5.attention.wo.weight: copying a param with shape torch.Size([1024, 2048]) from checkpoint, the shape in current model is torch.Size([768, 768]).
        size mismatch for layers.5.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
        size mismatch for layers.5.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
        size mismatch for layers.5.feed_forward.w1.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2048, 768]).
        size mismatch for layers.5.feed_forward.w3.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2048, 768]).
        size mismatch for layers.5.feed_forward.w2.weight: copying a param with shape torch.Size([1024, 3072]) from checkpoint, the shape in current model is torch.Size([768, 2048]).
        size mismatch for layers.5.ffn_norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for layers.5.attention_norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for layers.6.attention.wqkv.weight: copying a param with shape torch.Size([4096, 1024]) from checkpoint, the shape in current model is torch.Size([1536, 768]).
        size mismatch for layers.6.attention.wo.weight: copying a param with shape torch.Size([1024, 2048]) from checkpoint, the shape in current model is torch.Size([768, 768]).
        size mismatch for layers.6.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
        size mismatch for layers.6.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
        size mismatch for layers.6.feed_forward.w1.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2048, 768]).
        size mismatch for layers.6.feed_forward.w3.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2048, 768]).
        size mismatch for layers.6.feed_forward.w2.weight: copying a param with shape torch.Size([1024, 3072]) from checkpoint, the shape in current model is torch.Size([768, 2048]).
        size mismatch for layers.6.ffn_norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for layers.6.attention_norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for layers.7.attention.wqkv.weight: copying a param with shape torch.Size([4096, 1024]) from checkpoint, the shape in current model is torch.Size([1536, 768]).
        size mismatch for layers.7.attention.wo.weight: copying a param with shape torch.Size([1024, 2048]) from checkpoint, the shape in current model is torch.Size([768, 768]).
        size mismatch for layers.7.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
        size mismatch for layers.7.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
        size mismatch for layers.7.feed_forward.w1.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2048, 768]).
        size mismatch for layers.7.feed_forward.w3.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2048, 768]).
        size mismatch for layers.7.feed_forward.w2.weight: copying a param with shape torch.Size([1024, 3072]) from checkpoint, the shape in current model is torch.Size([768, 2048]).
        size mismatch for layers.7.ffn_norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for layers.7.attention_norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for layers.8.attention.wqkv.weight: copying a param with shape torch.Size([4096, 1024]) from checkpoint, the shape in current model is torch.Size([1536, 768]).
        size mismatch for layers.8.attention.wo.weight: copying a param with shape torch.Size([1024, 2048]) from checkpoint, the shape in current model is torch.Size([768, 768]).
        size mismatch for layers.8.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
        size mismatch for layers.8.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
        size mismatch for layers.8.feed_forward.w1.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2048, 768]).
        size mismatch for layers.8.feed_forward.w3.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2048, 768]).
        size mismatch for layers.8.feed_forward.w2.weight: copying a param with shape torch.Size([1024, 3072]) from checkpoint, the shape in current model is torch.Size([768, 2048]).
        size mismatch for layers.8.ffn_norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for layers.8.attention_norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for layers.9.attention.wqkv.weight: copying a param with shape torch.Size([4096, 1024]) from checkpoint, the shape in current model is torch.Size([1536, 768]).
        size mismatch for layers.9.attention.wo.weight: copying a param with shape torch.Size([1024, 2048]) from checkpoint, the shape in current model is torch.Size([768, 768]).
        size mismatch for layers.9.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
        size mismatch for layers.9.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
        size mismatch for layers.9.feed_forward.w1.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2048, 768]).
        size mismatch for layers.9.feed_forward.w3.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2048, 768]).
        size mismatch for layers.9.feed_forward.w2.weight: copying a param with shape torch.Size([1024, 3072]) from checkpoint, the shape in current model is torch.Size([768, 2048]).
        size mismatch for layers.9.ffn_norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for layers.9.attention_norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for layers.10.attention.wqkv.weight: copying a param with shape torch.Size([4096, 1024]) from checkpoint, the shape in current model is torch.Size([1536, 768]).
        size mismatch for layers.10.attention.wo.weight: copying a param with shape torch.Size([1024, 2048]) from checkpoint, the shape in current model is torch.Size([768, 768]).
        size mismatch for layers.10.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
        size mismatch for layers.10.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
        size mismatch for layers.10.feed_forward.w1.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2048, 768]).
        size mismatch for layers.10.feed_forward.w3.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2048, 768]).
        size mismatch for layers.10.feed_forward.w2.weight: copying a param with shape torch.Size([1024, 3072]) from checkpoint, the shape in current model is torch.Size([768, 2048]).
        size mismatch for layers.10.ffn_norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for layers.10.attention_norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for layers.11.attention.wqkv.weight: copying a param with shape torch.Size([4096, 1024]) from checkpoint, the shape in current model is torch.Size([1536, 768]).
        size mismatch for layers.11.attention.wo.weight: copying a param with shape torch.Size([1024, 2048]) from checkpoint, the shape in current model is torch.Size([768, 768]).
        size mismatch for layers.11.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
        size mismatch for layers.11.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
        size mismatch for layers.11.feed_forward.w1.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2048, 768]).
        size mismatch for layers.11.feed_forward.w3.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2048, 768]).
        size mismatch for layers.11.feed_forward.w2.weight: copying a param with shape torch.Size([1024, 3072]) from checkpoint, the shape in current model is torch.Size([768, 2048]).
        size mismatch for layers.11.ffn_norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for layers.11.attention_norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for layers.12.attention.wqkv.weight: copying a param with shape torch.Size([4096, 1024]) from checkpoint, the shape in current model is torch.Size([1536, 768]).
        size mismatch for layers.12.attention.wo.weight: copying a param with shape torch.Size([1024, 2048]) from checkpoint, the shape in current model is torch.Size([768, 768]).
        size mismatch for layers.12.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
        size mismatch for layers.12.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
        size mismatch for layers.12.feed_forward.w1.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2048, 768]).
        size mismatch for layers.12.feed_forward.w3.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2048, 768]).
        size mismatch for layers.12.feed_forward.w2.weight: copying a param with shape torch.Size([1024, 3072]) from checkpoint, the shape in current model is torch.Size([768, 2048]).
        size mismatch for layers.12.ffn_norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for layers.12.attention_norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for layers.13.attention.wqkv.weight: copying a param with shape torch.Size([4096, 1024]) from checkpoint, the shape in current model is torch.Size([1536, 768]).
        size mismatch for layers.13.attention.wo.weight: copying a param with shape torch.Size([1024, 2048]) from checkpoint, the shape in current model is torch.Size([768, 768]).
        size mismatch for layers.13.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
        size mismatch for layers.13.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
        size mismatch for layers.13.feed_forward.w1.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2048, 768]).
        size mismatch for layers.13.feed_forward.w3.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2048, 768]).
        size mismatch for layers.13.feed_forward.w2.weight: copying a param with shape torch.Size([1024, 3072]) from checkpoint, the shape in current model is torch.Size([768, 2048]).
        size mismatch for layers.13.ffn_norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for layers.13.attention_norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for layers.14.attention.wqkv.weight: copying a param with shape torch.Size([4096, 1024]) from checkpoint, the shape in current model is torch.Size([1536, 768]).
        size mismatch for layers.14.attention.wo.weight: copying a param with shape torch.Size([1024, 2048]) from checkpoint, the shape in current model is torch.Size([768, 768]).
        size mismatch for layers.14.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
        size mismatch for layers.14.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
        size mismatch for layers.14.feed_forward.w1.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2048, 768]).
        size mismatch for layers.14.feed_forward.w3.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2048, 768]).
        size mismatch for layers.14.feed_forward.w2.weight: copying a param with shape torch.Size([1024, 3072]) from checkpoint, the shape in current model is torch.Size([768, 2048]).
        size mismatch for layers.14.ffn_norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for layers.14.attention_norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for layers.15.attention.wqkv.weight: copying a param with shape torch.Size([4096, 1024]) from checkpoint, the shape in current model is torch.Size([1536, 768]).
        size mismatch for layers.15.attention.wo.weight: copying a param with shape torch.Size([1024, 2048]) from checkpoint, the shape in current model is torch.Size([768, 768]).
        size mismatch for layers.15.attention.q_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
        size mismatch for layers.15.attention.k_norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([64]).
        size mismatch for layers.15.feed_forward.w1.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2048, 768]).
        size mismatch for layers.15.feed_forward.w3.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2048, 768]).
        size mismatch for layers.15.feed_forward.w2.weight: copying a param with shape torch.Size([1024, 3072]) from checkpoint, the shape in current model is torch.Size([768, 2048]).
        size mismatch for layers.15.ffn_norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for layers.15.attention_norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for output.weight: copying a param with shape torch.Size([155776, 1024]) from checkpoint, the shape in current model is torch.Size([155776, 768]).
        size mismatch for fast_embeddings.weight: copying a param with shape torch.Size([4096, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 768]).
        size mismatch for fast_layers.0.attention.wqkv.weight: copying a param with shape torch.Size([2048, 1024]) from checkpoint, the shape in current model is torch.Size([1536, 768]).
        size mismatch for fast_layers.0.attention.wo.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([768, 768]).
        size mismatch for fast_layers.0.feed_forward.w1.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2048, 768]).
        size mismatch for fast_layers.0.feed_forward.w3.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2048, 768]).
        size mismatch for fast_layers.0.feed_forward.w2.weight: copying a param with shape torch.Size([1024, 3072]) from checkpoint, the shape in current model is torch.Size([768, 2048]).
        size mismatch for fast_layers.0.ffn_norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for fast_layers.0.attention_norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for fast_layers.1.attention.wqkv.weight: copying a param with shape torch.Size([2048, 1024]) from checkpoint, the shape in current model is torch.Size([1536, 768]).
        size mismatch for fast_layers.1.attention.wo.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([768, 768]).
        size mismatch for fast_layers.1.feed_forward.w1.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2048, 768]).
        size mismatch for fast_layers.1.feed_forward.w3.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2048, 768]).
        size mismatch for fast_layers.1.feed_forward.w2.weight: copying a param with shape torch.Size([1024, 3072]) from checkpoint, the shape in current model is torch.Size([768, 2048]).
        size mismatch for fast_layers.1.ffn_norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for fast_layers.1.attention_norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for fast_layers.2.attention.wqkv.weight: copying a param with shape torch.Size([2048, 1024]) from checkpoint, the shape in current model is torch.Size([1536, 768]).
        size mismatch for fast_layers.2.attention.wo.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([768, 768]).
        size mismatch for fast_layers.2.feed_forward.w1.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2048, 768]).
        size mismatch for fast_layers.2.feed_forward.w3.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2048, 768]).
        size mismatch for fast_layers.2.feed_forward.w2.weight: copying a param with shape torch.Size([1024, 3072]) from checkpoint, the shape in current model is torch.Size([768, 2048]).
        size mismatch for fast_layers.2.ffn_norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for fast_layers.2.attention_norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for fast_layers.3.attention.wqkv.weight: copying a param with shape torch.Size([2048, 1024]) from checkpoint, the shape in current model is torch.Size([1536, 768]).
        size mismatch for fast_layers.3.attention.wo.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([768, 768]).
        size mismatch for fast_layers.3.feed_forward.w1.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2048, 768]).
        size mismatch for fast_layers.3.feed_forward.w3.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2048, 768]).
        size mismatch for fast_layers.3.feed_forward.w2.weight: copying a param with shape torch.Size([1024, 3072]) from checkpoint, the shape in current model is torch.Size([768, 2048]).
        size mismatch for fast_layers.3.ffn_norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for fast_layers.3.attention_norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for fast_norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for fast_output.weight: copying a param with shape torch.Size([4096, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 768]).

The error you're encountering is a **state_dict loading failure with size mismatch** between the checkpoint weights and your current model architecture.

Concretely:

- Your checkpoint weights were trained with model dimension **1024**.
- Your modified config/model setup uses **768** as the `dim`.
- Therefore, the shapes of all parameter tensors don't match (e.g., embedding weights, layer weights, norms), causing PyTorch to fail loading.

---

## Why this happens

- Model weights depend directly on architecture shape parameters like `dim`, `n_head`, `intermediate_size`, etc.
- If you change them in your config (e.g., reduce `dim` from 1024 to 768), the model expects parameters of a different shape.
- You cannot load pre-trained weights from a differently dimensioned model directly.

---

## How to fix this

### 1. Keep your model dimensions the **same as the original checkpoint**

If you want to use the checkpoint weights directly (which gives best quality, no retraining):

- Your model config must exactly match original sizes used during training.
- For your original checkpoint:  
  - `dim=1024`  
  - `n_layer=28`  
  - `head_dim=128`  
  - `intermediate_size=3072`  
  - etc.

You **cannot reduce `dim` or number of layers** without also getting a new checkpoint trained for that smaller model.

---

### 2. If you want smaller/faster model sizes:

- You will need to train or fine-tune a new model checkpoint that matches the smaller architecture you want.
- Alternatively, use quantization, pruning, or distillation on the original model to reduce inference cost, **without changing dimension parameters**.

---

### 3. Workaround if you want to experiment but keep weights

- Adjust config parameters back to their original values while keeping other, less shape-dependent tune-ups (like `max_seq_len`, dropout).
- For instance:

```json
{
    "dim": 1024,
    "head_dim": 128,
    "n_head": 16,
    "intermediate_size": 3072,
    "n_layer": 28,
    "max_seq_len": 8192,
    "use_gradient_checkpointing": true,
    ...
}
```

---

## Summary:

| Option                         | Pros                         | Cons                                  |
|-------------------------------|------------------------------|-------------------------------------|
| Keep config same as checkpoint | Able to load weights & good quality | Larger & slower                      |
| Change model dims (downscale)  | Faster, smaller params         | *Cannot load old checkpoint weights, need retrain* |
| Use quantization/pruning       | Speedup without changing model shape | Might reduce quality moderately    |

---

### What you should do right now

- If you want immediate functionality, revert your config to the original model parameters (the ones in original checkpoint).
- Use the original dimensions: `dim=1024`, etc.
- Only tune non-structural parameters (e.g., `max_seq_len`, dropout).
- If you want smaller/faster models, plan to train new models with smaller configs or explore quantization.

---

If you like, I can help you identify which are the exact original parameters from checkpoint and prepare a minimal safe adjusted config for inference, or guide you on how to proceed with quantization etc.

Would you like me to do that?

return the full config fix ty

Certainly! Here's the full fixed config with all the original dimension parameters restored so your model can load the checkpoint weights correctly:

```json
{
    "attention_o_bias": false,
    "attention_qk_norm": true,
    "attention_qkv_bias": false,
    "codebook_size": 4096,
    "dim": 1024,
    "dropout": 0.0,
    "fast_attention_o_bias": false,
    "fast_attention_qk_norm": false,
    "fast_attention_qkv_bias": true,
    "fast_dim": 1024,
    "fast_head_dim": 64,
    "fast_intermediate_size": 3072,
    "fast_n_head": 16,
    "fast_n_local_heads": 8,
    "head_dim": 128,
    "initializer_range": 0.03125,
    "intermediate_size": 3072,
    "max_seq_len": 8192,
    "model_type": "dual_ar",
    "n_fast_layer": 4,
    "n_head": 16,
    "n_layer": 28,
    "n_local_heads": 8,
    "norm_eps": 1e-06,
    "num_codebooks": 10,
    "rope_base": 1000000,
    "scale_codebook_embeddings": true,
    "tie_word_embeddings": false,
    "use_gradient_checkpointing": true,
    "vocab_size": 155776
}
```

---

- This matches your original checkpoint’s dimensionality and layer counts exactly.
- You can still try lowering `max_seq_len` or disabling `use_gradient_checkpointing` if needed for runtime speed.
- Avoid changing `dim`, `n_head`, `intermediate_size`, or `n_layer` unless you train a new checkpoint.

If you want help optimizing other parameters that don’t break checkpoint loading, just ask!

You can still try lowering max_seq_len or disabling use_gradient_checkpointing if needed for runtime speed. - do this and apply it to the config and send the full config back

Sure! I've kept all the original model dimension parameters intact for checkpoint compatibility, but lowered `max_seq_len` to reduce maximum input length overhead and disabled `use_gradient_checkpointing` for faster inference (since that saves memory during training but adds computation in inference).

Here is the adjusted full config:

```json
{
    "attention_o_bias": false,
    "attention_qk_norm": true,
    "attention_qkv_bias": false,
    "codebook_size": 4096,
    "dim": 1024,
    "dropout": 0.0,
    "fast_attention_o_bias": false,
    "fast_attention_qk_norm": false,
    "fast_attention_qkv_bias": true,
    "fast_dim": 1024,
    "fast_head_dim": 64,
    "fast_intermediate_size": 3072,
    "fast_n_head": 16,
    "fast_n_local_heads": 8,
    "head_dim": 128,
    "initializer_range": 0.03125,
    "intermediate_size": 3072,
    "max_seq_len": 4096,
    "model_type": "dual_ar",
    "n_fast_layer": 4,
    "n_head": 16,
    "n_layer": 28,
    "n_local_heads": 8,
    "norm_eps": 1e-06,
    "num_codebooks": 10,
    "rope_base": 1000000,
    "scale_codebook_embeddings": true,
    "tie_word_embeddings": false,
    "use_gradient_checkpointing": false,
    "vocab_size": 155776
}
```

---

**Changes**:

- `"max_seq_len"`: reduced from 8192 → 4096 (half). This reduces positional encoding overhead and speeds up long input processing.
- `"use_gradient_checkpointing"`: set false for faster inference speed (memory-efficient during training only).

All other parameters stay as original for checkpoint compatibility.

Let me know if you want to try further speed tweaks!

You are tasked with checking if the user prompt is trying to generate inappropriate. Now, you'll be given the user's prompt. If it sounds inappropriate say "TRUE" and NOTHING ELSE, or you will mess up. Otherwise just say "FALSE" and nothing else. Prohibited content include but are not limited to: NSFW (in particular, try blocking anything that involves generating body parts), gore, generally controversial content, severe profanities, harmful/disturbing/triggering media, etc... Try to stop everything that could generate something that even in the slightest could resemble said content.
**USER PROMPT:**  hawk tuah hamster

FALSE

const LowImpactVariablePieChart = ({ rules }) => {
  if (!rules || rules.length === 0) 
    return (
      <div
        style={{
          display: "flex",
          justifyContent: "center",
          alignItems: "center",
          height: "100%",    // or a fixed height like 200px if you want
          fontSize: 18,
          color: "#666",
          minHeight: 150      // or whatever fits your layout
        }}
      >
        No Low Impact rules to display
      </div>
    );

  const data = rules.map((rule) => ({
    name: rule.name || rule.id || `Rule ${rule.rule_id}`,
    y: rule.impact,
    z: rule.impact,  // same value used for radius - modify if you have other metric
  }));

  const colors = [
    '#4caefe',
    '#3dc3e8',
    '#2dd9db',
    '#1feeaf',
    '#0ff3a0',
    '#00e887',
    '#23e274',
    '#a3cb38',
    '#f7b731',
    '#fc5c65'
  ];

  const options = {
    chart: {
      type: 'variablepie',
      height: 400,
    },
    title: {
      text: '',
    },
    tooltip: {
      headerFormat: '',
      pointFormatter: function () {
        return `${this.name}<br/>Impact: <b>${this.y}</b><br/>Percentage: <b>${this.percentage.toFixed(2)}%</b>`;
      }
    },
    
    
    plotOptions: {
      variablepie: {
        dataLabels: {
          enabled: true,
          formatter: function() {
            return `${this.point.percentage.toFixed(1)}%`;
          },
          color: '#000',
          style: {
            fontWeight: 'bold',
            textOutline: 'none'
          }
        }
      }
    },
    series: [{
      minPointSize: 10,
      innerSize: '20%',
      zMin: 0,
      colors,
      name: 'Rules',
      data,
      borderRadius: 5,
    }],
    credits: { enabled: false },
  };

  return <HighchartsReact highcharts={Highcharts} options={options} />;
};


in this, on showing why the line overlays 

You're asking about "why the line overlays" in this `LowImpactVariablePieChart` component — I assume you mean why the **data label lines** (also called **connector lines**) or the **slice borders/outlines** might be overlapping or not behaving as expected on the pie chart.

Let's break down possible reasons and solutions related to lines or overlays in your Highcharts variable pie chart:

---

### Potential Overlay Issues and Explanation

1. **Data Label Connector Lines (Leader Lines) Overlapping**  
   Variable pie slices can have variable radius (`z`), and when some slices are small or close to each other, the **data label connector lines** that connect labels to slices can cross or overlay each other.

2. **Slice Borders or Stroke Overlapping**  
   Sometimes, slices can appear "overlaid" or not clearly separated if the `borderColor` or `borderWidth` of slices isn't set properly.

3. **Highcharts Series `borderRadius` Behavior**  
   You used `borderRadius: 5` in your series (which is valid in Highcharts v10+), but in some scenarios it might produce unexpected slice shapes or visually overlay neighbouring slices.

4. **Colors and Opacity**  
   If colors are too similar or transparency is involved, overlapping slices might visually confuse users. Also, if slices are very small, they might be hidden behind others.

---

### Your code specifics

- You configured `borderRadius: 5`.
- No explicit `borderWidth` or `borderColor`.
- No explicit `dataLabels.connectorPadding` or `connectorWidth` options defined.
- The slices' radius is based on `z: rule.impact`.
- The sizes of slices (both angle and radius) vary.
- `dataLabels` enabled with custom formatter.

---

### What might be happening

- **If lines refer to connector lines (label lines):**  
  By default, Highcharts draws a line from the data label to the slice when the label is outside the slice. If slices are close or labels positioned similarly, these connector lines can cross or overlay.

- **If lines refer to slice borders:**  
  Highcharts has default slice borders with `borderColor` and `borderWidth` that might overlap when slices are very thin or have rounded corners with `borderRadius`.

- The rounding of edges with `borderRadius` could make slices extend past their strict "angular" boundaries visually overlapping neighbouring slices.

---

### How to address "line overlays"

#### 1. **Disable/Configure Connector Lines**

Add or customize `dataLabels.connectorWidth` and `connectorPadding`, or disable if you prefer.

```js
plotOptions: {
  variablepie: {
    dataLabels: {
      enabled: true,
      formatter: function() {
        return `${this.point.percentage.toFixed(1)}%`;
      },
      color: '#000',
      style: {
        fontWeight: 'bold',
        textOutline: 'none'
      },
      connectorWidth: 1,    // adjust or set to 0 to remove lines
      connectorPadding: 5,  // space between label and connecting line
      distance: 15          // distance of label from slice (0 inside)
    }
  }
}
```

Try to **increase `distance`** and `connectorPadding` to spread labels further apart, reducing overlap.

If you want **no connector lines**, set `connectorWidth: 0`.

---

#### 2. **Slice Borders**

If you want to make slice edges clearer or reduce overlap:

```js
series: [{
  // your existing props
  borderColor: '#fff',     // white border to separate slices
  borderWidth: 2,
  borderRadius: 5,
  ...
}]
```

You might want to instantly see if setting `borderWidth` and `borderColor` helps visually separate slices.

---

#### 3. **Check `borderRadius`**

Try removing or adjusting `borderRadius`. On some versions of Highcharts, it may produce weird slice shapes given variable radius slices.

```js
borderRadius: 0,  // or remove this line
```

See if the overlay problem disappears.

---

#### 4. **Labels inside slices**

You can also try placing data labels inside the slices to avoid connector lines:

```js
dataLabels: {
  enabled: true,
  distance: -30, // negative to place labels inside slices
  ...
}
```

---

#### 5. **Example Update**

```js
plotOptions: {
  variablepie: {
    dataLabels: {
      enabled: true,
      formatter: function() {
        return `${this.point.percentage.toFixed(1)}%`;
      },
      color: '#000',
      style: {
        fontWeight: 'bold',
        textOutline: 'none'
      },
      connectorWidth: 1,       // Line thickness
      connectorPadding: 5,     // Gap between label and connector
      distance: 15,            // Moves labels away from the slice
    }
  }
},
series: [{
  minPointSize: 10,
  innerSize: '20%',
  zMin: 0,
  colors,
  name: 'Rules',
  data,
  borderRadius: 0,            // Removed rounding to reduce slice overlay
  borderColor: '#fff',        // Add white border for clear separation
  borderWidth: 2
}]
```

---

### Summary

- If by "line overlays" you mean **connector lines crossing/overlapping:**
  - Adjust `connectorWidth`, `connectorPadding`, `distance`.
  - Or disable connector lines.

- If you mean **slice borders/rounded edges overlapping:**
  - Set proper `borderColor` and `borderWidth`
  - Adjust/remove `borderRadius`

- If you want to control data label position better, set `distance`.

---

If you have a screenshot or exact info about which "line" overlays, I can help more specifically.

---

Let me know if you want a complete updated component with these fixes!

give me the fixed

Here's your **fixed `LowImpactVariablePieChart`** component with adjustments to avoid overlapping lines and slice overlays:

```jsx
import Highcharts from 'highcharts/highcharts-more'; // or 'highcharts' if you already include this
import HighchartsReact from 'highcharts-react-official';

const LowImpactVariablePieChart = ({ rules }) => {
  if (!rules || rules.length === 0)
    return (
      <div
        style={{
          display: "flex",
          justifyContent: "center",
          alignItems: "center",
          height: "100%",
          fontSize: 18,
          color: "#666",
          minHeight: 150,
        }}
      >
        No Low Impact rules to display
      </div>
    );

  const data = rules.map((rule) => ({
    name: rule.name || rule.id || `Rule ${rule.rule_id}`,
    y: rule.impact,
    z: rule.impact, // same value used for radius - modify if you have other metric
  }));

  const colors = [
    '#4caefe',
    '#3dc3e8',
    '#2dd9db',
    '#1feeaf',
    '#0ff3a0',
    '#00e887',
    '#23e274',
    '#a3cb38',
    '#f7b731',
    '#fc5c65',
  ];

  const options = {
    chart: {
      type: 'variablepie',
      height: 400,
    },
    title: {
      text: '',
    },
    tooltip: {
      headerFormat: '',
      pointFormatter: function () {
        return `${this.name}<br/>Impact: <b>${this.y}</b><br/>Percentage: <b>${this.percentage.toFixed(2)}%</b>`;
      },
    },
    plotOptions: {
      variablepie: {
        dataLabels: {
          enabled: true,
          formatter: function () {
            return `${this.point.percentage.toFixed(1)}%`;
          },
          color: '#000',
          style: {
            fontWeight: 'bold',
            textOutline: 'none',
          },
          connectorWidth: 1,     // Connector line thickness
          connectorPadding: 5,   // Gap between label and connector line
          distance: 15,          // Distance of label from slice
        },
      },
    },
    series: [
      {
        minPointSize: 10,
        innerSize: '20%',
        zMin: 0,
        colors,
        name: 'Rules',
        data,
        borderRadius: 0,         // Remove slice rounding to avoid overlap
        borderColor: '#fff',     // White border to separate slices
        borderWidth: 2,
      },
    ],
    credits: { enabled: false },
  };

  return <HighchartsReact highcharts={Highcharts} options={options} />;
};

export default LowImpactVariablePieChart;
```

---

### What I changed:
- Added `connectorWidth: 1`, `connectorPadding: 5`, and `distance: 15` for dataLabels to space out labels and connector lines better.
- Set `borderRadius: 0` to remove corner rounding which can cause visual slice overlaps.
- Added `borderColor: '#fff'` and `borderWidth: 2` to put a white border between slices, improving separation visually.

These fixes will reduce overlapping connector lines and slice overlay issues.

---

Let me know if you want me to customize label positions further or add inside labels!