Model upload

README.md

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+---
+pipeline_tag: text-generation
+---
+5.75 bpw exl2 quant of [mistralai/Mistral-Large-Instruct-2407](https://huggingface.co/mistralai/Mistral-Large-Instruct-2407)

config.json

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+{
+    "architectures": [
+        "MistralForCausalLM"
+    ],
+    "attention_dropout": 0.0,
+    "bos_token_id": 1,
+    "eos_token_id": 2,
+    "hidden_act": "silu",
+    "hidden_size": 12288,
+    "initializer_range": 0.02,
+    "intermediate_size": 28672,
+    "max_position_embeddings": 32768,
+    "model_type": "mistral",
+    "num_attention_heads": 96,
+    "num_hidden_layers": 88,
+    "num_key_value_heads": 8,
+    "rms_norm_eps": 1e-05,
+    "rope_theta": 1000000.0,
+    "sliding_window": null,
+    "tie_word_embeddings": false,
+    "torch_dtype": "bfloat16",
+    "transformers_version": "4.42.3",
+    "use_cache": true,
+    "vocab_size": 32768,
+    "quantization_config": {
+        "quant_method": "exl2",
+        "version": "0.1.7",
+        "bits": 5.75,
+        "head_bits": 6,
+        "calibration": {
+            "rows": 115,
+            "length": 2048,
+            "dataset": "(default)"
+        }
+    }
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.42.3"
+}

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params.json

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+{
+    "dim": 12288,
+    "n_layers": 88,
+    "head_dim": 128,
+    "hidden_dim": 28672,
+    "n_heads": 96,
+    "n_kv_heads": 8,
+    "norm_eps": 1e-05,
+    "vocab_size": 32768,
+    "rope_theta": 1000000.0
+}

test.py

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+import json
+from typing import Dict
+
+from safetensors.torch import load_file, save_file
+from huggingface_hub import split_torch_state_dict_into_shards
+import torch
+import os
+
+def save_state_dict(state_dict: Dict[str, torch.Tensor], save_directory: str):
+    state_dict_split = split_torch_state_dict_into_shards(state_dict, filename_pattern='consolidated{suffix}.safetensors')
+    for filename, tensors in state_dict_split.filename_to_tensors.items():
+        shard = {tensor: state_dict[tensor] for tensor in tensors}
+        print("Saving", save_directory, filename)
+        save_file(shard, os.path.join(save_directory, filename))
+    if state_dict_split.is_sharded:
+        index = {
+            "metadata": state_dict_split.metadata,
+            "weight_map": state_dict_split.tensor_to_filename,
+        }
+        with open(os.path.join(save_directory, "consolidated.safetensors.index.json"), "w") as f:
+            f.write(json.dumps(index, indent=2))
+
+big_file = 'consolidated.safetensors'
+loaded = load_file(big_file)
+
+save_state_dict(loaded, save_directory=f'.')

tokenizer.model

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