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README.md

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+---
+library_name: transformers
+license: apache-2.0
+language:
+- en
+widget:
+- text: 'You will be given a question and options. Select the right answer. QUESTION:
+    If (G, .) is a group such that (ab)^-1 = a^-1b^-1, for all a, b in G, then G is
+    a/an CHOICES: - A: commutative semi group - B: abelian group - C: non-abelian
+    group - D: None of these ANSWER: [unused0] [MASK]'
+tags:
+- fill-mask
+- masked-lm
+- long-context
+- classification
+- modernbert
+- mlx
+pipeline_tag: fill-mask
+inference: false
+---
+
+# mlx-community/answerdotai-ModernBERT-Large-Instruct-8bit
+
+The Model [mlx-community/answerdotai-ModernBERT-Large-Instruct-8bit](https://huggingface.co/mlx-community/answerdotai-ModernBERT-Large-Instruct-8bit) was converted to MLX format from [answerdotai/ModernBERT-Large-Instruct](https://huggingface.co/answerdotai/ModernBERT-Large-Instruct) using mlx-lm version **0.0.3**.
+
+## Use with mlx
+
+```bash
+pip install mlx-embeddings
+```
+
+```python
+from mlx_embeddings import load, generate
+import mlx.core as mx
+
+model, tokenizer = load("mlx-community/answerdotai-ModernBERT-Large-Instruct-8bit")
+
+# For text embeddings
+output = generate(model, processor, texts=["I like grapes", "I like fruits"])
+embeddings = output.text_embeds  # Normalized embeddings
+
+# Compute dot product between normalized embeddings
+similarity_matrix = mx.matmul(embeddings, embeddings.T)
+
+print("Similarity matrix between texts:")
+print(similarity_matrix)
+
+
+```

config.json

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+{
+    "architectures": [
+        "ModernBertForMaskedLM"
+    ],
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "bos_token_id": 50281,
+    "classifier_activation": "gelu",
+    "classifier_bias": false,
+    "classifier_dropout": 0.0,
+    "classifier_pooling": "mean",
+    "cls_token_id": 50281,
+    "decoder_bias": true,
+    "deterministic_flash_attn": false,
+    "embedding_dropout": 0.0,
+    "eos_token_id": 50282,
+    "global_attn_every_n_layers": 3,
+    "global_rope_theta": 160000.0,
+    "gradient_checkpointing": false,
+    "hidden_activation": "gelu",
+    "hidden_size": 1024,
+    "initializer_cutoff_factor": 2.0,
+    "initializer_range": 0.02,
+    "intermediate_size": 2624,
+    "layer_norm_eps": 1e-05,
+    "local_attention": 128,
+    "local_rope_theta": 10000.0,
+    "max_position_embeddings": 8192,
+    "mlp_bias": false,
+    "mlp_dropout": 0.0,
+    "model_type": "modernbert",
+    "norm_bias": false,
+    "norm_eps": 1e-05,
+    "num_attention_heads": 16,
+    "num_hidden_layers": 28,
+    "pad_token_id": 50283,
+    "position_embedding_type": "absolute",
+    "quantization": {
+        "group_size": 64,
+        "bits": 8
+    },
+    "reference_compile": true,
+    "repad_logits_with_grad": false,
+    "sep_token_id": 50282,
+    "sparse_pred_ignore_index": -100,
+    "sparse_prediction": false,
+    "torch_dtype": "bfloat16",
+    "transformers_version": "4.48.0",
+    "vocab_size": 50368
+}

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