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README.md

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+---
+license: apache-2.0
+tags:
+- merge
+- mergekit
+- lazymergekit
+- llm-merge-cc2
+- mistral-7b
+- mistral-ft-optimized
+- neural-hermes
+- mistralai/Mistral-7B-v0.1
+- samir-fama/SamirGPT-v1
+- abacusai/Slerp-CM-mist-dpo
+- EmbeddedLLM/Mistral-7B-Merge-14-v0.2
+---
+
+# LLM_MERGE_CC2
+
+LLM_MERGE_CC2 est une fusion des modèles suivants créée par ManoloPueblo utilisant [mergekit](https://github.com/cg123/mergekit):
+* [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
+* [samir-fama/SamirGPT-v1](https://huggingface.co/samir-fama/SamirGPT-v1)
+* [abacusai/Slerp-CM-mist-dpo](https://huggingface.co/abacusai/Slerp-CM-mist-dpo)
+* [EmbeddedLLM/Mistral-7B-Merge-14-v0.2](https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0.2)
+
+## 🧩 Configuration de la fusion
+```yaml
+merge_method: ties
+base_model: mistralai/Mistral-7B-v0.1
+models:
+  - model: mistralai/Mistral-7B-v0.1
+    # No parameters necessary for base model
+  - model: samir-fama/SamirGPT-v1
+    parameters:
+      density: 0.53
+      weight: 0.4
+  - model: abacusai/Slerp-CM-mist-dpo
+    parameters:
+      density: 0.53
+      weight: 0.3
+  - model: EmbeddedLLM/Mistral-7B-Merge-14-v0.2
+    parameters:
+      density: 0.53
+      weight: 0.3
+merge_method: dare_ties
+base_model: mistralai/Mistral-7B-v0.1
+parameters:
+  int8_mask: true
+dtype: bfloat16
+```
+
+## Description
+LLM_MERGE_CC2 est un modèle de langage créé par la fusion de trois variantes Mistral :
+1. Mistral-7B-v0.1 - Le modèle de base Mistral (modèle de référence)
+2. mistral-ft-optimized-1218 - Version optimisée par OpenPipe (poids: 0.5, densité: 0.5)
+3. NeuralHermes-2.5-Mistral-7B - Version améliorée par MLabonne (poids: 0.3, densité: 0.5)
+
+Cette fusion utilise la méthode "ties" avec normalisation et une précision float16 pour combiner les forces des trois modèles.
+
+## Architecture
+Le modèle conserve l'architecture de base de Mistral-7B tout en incorporant les améliorations des trois versions à travers une fusion pondérée. La méthode "ties" permet une fusion plus sophistiquée des poids des modèles.
+
+## Paramètres de fusion
+- Méthode de fusion : ties
+- Normalisation : activée
+- Type de données : float16
+- Densités et poids :
+  * OpenPipe/mistral-ft-optimized-1218 : densité 0.5, poids 0.5
+  * NeuralHermes-2.5-Mistral-7B : densité 0.5, poids 0.3
+
+## Utilisation
+Ce modèle peut être utilisé avec la bibliothèque transformers de Hugging Face :
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("ManoloPueblo/LLM_MERGE_CC2")
+model = AutoModelForCausalLM.from_pretrained("ManoloPueblo/LLM_MERGE_CC2")
+```
+
+## Modèles fusionnés
+1. [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) - Modèle de base
+2. [mistral-ft-optimized-1218](https://huggingface.co/OpenPipe/mistral-ft-optimized-1218) - Version optimisée
+3. [NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B) - Version améliorée
+
+## Limitations
+Comme pour tout modèle de langage, les utilisateurs doivent être conscients des biais potentiels et des limitations inhérentes aux modèles sources. Les performances peuvent varier selon les cas d'utilisation.

config.json

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+{
+  "_name_or_path": "mistralai/Mistral-7B-v0.1",
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 32768,
+  "model_type": "mistral",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 10000.0,
+  "sliding_window": 4096,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.46.2",
+  "use_cache": true,
+  "vocab_size": 32000
+}

mergekit_config.yml

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+
+
+models:
+  - model: mistralai/Mistral-7B-v0.1
+    # No parameters necessary for base model
+  - model: samir-fama/SamirGPT-v1
+    parameters:
+      density: 0.53
+      weight: 0.4
+  - model: abacusai/Slerp-CM-mist-dpo
+    parameters:
+      density: 0.53
+      weight: 0.3
+  - model: EmbeddedLLM/Mistral-7B-Merge-14-v0.2
+    parameters:
+      density: 0.53
+      weight: 0.3
+merge_method: dare_ties
+base_model: mistralai/Mistral-7B-v0.1
+parameters:
+  int8_mask: true
+dtype: bfloat16

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