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README.md

@@ -1,62 +1,25 @@
 ---
 license: cc-by-nc-2.0
-library_name: transformers
-tags:
-- mixtral
-pipeline_tag: text-generation
 ---
-## Exllama v2 Quantizations of laserxtral
-
-Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.11">turboderp's ExLlamaV2 v0.0.11</a> for quantization.
-
-# The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)
-
-Join Our Discord! https://discord.gg/vT3sktQ3zb 
-
-Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
-
-Conversion was done using the default calibration dataset.
-
-Default arguments used.
-
-Original model: https://huggingface.co/cognitivecomputations/laserxtral
-
-<a href="https://huggingface.co/cognitivecomputations/laserxtral-exl2/tree/6_5">6.5 bits per weight</a>
-
-<a href="https://huggingface.co/cognitivecomputations/laserxtral-exl2/tree/4">4 bits per weight</a>
-
-<a href="https://huggingface.co/cognitivecomputations/laserxtral-exl2/tree/3">3 bits per weight</a>
-
-<a href="https://huggingface.co/cognitivecomputations/laserxtral-exl2/tree/2">2 bits per weight</a>
-
-Credit to Bartowski for help and model card formatting
-
-![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/655dc641accde1bbc8b41aec/iToMZFTp1DuXnpw9oJ61y.jpeg)
-
-## Original Model Card Below
-
-![image/webp](https://cdn-uploads.huggingface.co/production/uploads/646e57a5cb6ea6e6b6df1ad4/BtnWsqZnaG1I6aa-Ldkfz.webp)
 
 by David, Fernando and Eric
 
-Sponsored by: [VAGO Solutions](https://vago-solutions.de)
-
-Join our Discord! https://discord.gg/vT3sktQ3zb 
-
 An experimentation regarding 'lasering' each expert to denoise and enhance model capabilities.
 
 This model has half size in comparison to the Mixtral 8x7b Instruct. And it basically has the same level of performance (we are working to get a better MMLU score).
 
+Used models (all lasered using laserRMT, except for the base model):
+
+
+*mlabonne/Marcoro14-7B-slerp (base)
+
+*cognitivecomputations/dolphin-2.6-mistral-7b-dpo
 
-# Laserxtral - 4x7b (all, except for base, lasered using laserRMT)
+*beowolx/CodeNinja-1.0-OpenChat-7B
 
-This model is a Mixture of Experts (MoE) made with [mergekit](https://github.com/cg123/mergekit) (mixtral branch). It uses the following base models:
- * [cognitivecomputations/dolphin-2.6-mistral-7b-dpo](https://huggingface.co/cognitivecomputations/dolphin-2.6-mistral-7b-dpo)
- * [mlabonne/Marcoro14-7B-slerp (base)](https://huggingface.co/mlabonne/Marcoro14-7B-slerp)
- * [beowolx/CodeNinja-1.0-OpenChat-7B](https://huggingface.co/beowolx/CodeNinja-1.0-OpenChat-7B)
- * [Q-bert/MetaMath-Cybertron-Starling](https://huggingface.co/Q-bert/MetaMath-Cybertron-Starling)
- * [WizardLM/WizardMath-7B-V1.1](https://huggingface.co/WizardLM/WizardMath-7B-V1.1)
+*Q-bert/MetaMath-Cybertron-Starling
 
+*WizardLM/WizardMath-7B-V1.1
 
 It follows the implementation of laserRMT @ https://github.com/cognitivecomputations/laserRMT
 
@@ -64,4 +27,4 @@ Here, we are controlling layers checking which ones have lower signal to noise r
 
 We intend to be the first of a family of experimentations being carried out @ Cognitive Computations.
 
-In this experiment we have observed very high truthfulness and high reasoning capabilities.
+In this experiment we have observed very high truthfulness and high reasoning capabilities.

config.json

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+{
+  "_name_or_path": "mlabonne/Marcoro14-7B-slerp",
+  "architectures": [
+    "MixtralForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 32768,
+  "model_type": "mixtral",
+  "num_attention_heads": 32,
+  "num_experts_per_tok": 2,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "num_local_experts": 4,
+  "output_router_logits": false,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 10000.0,
+  "router_aux_loss_coef": 0.001,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.36.2",
+  "use_cache": true,
+  "vocab_size": 32000
+}

huggingface-metadata.txt

@@ -0,0 +1,10 @@
+url: https://huggingface.co/cognitivecomputations/laserxtral
+branch: main
+download date: 2024-01-15 13:49:54
+sha256sum:
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+    dadfd56d766715c61d2ef780a525ab43b8e6da4de6865bda3d95fdef5e134055 tokenizer.model

model.safetensors.index.json

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