Quant for 6.5

README.md

@@ -11,63 +11,88 @@ tags:
 - mergekit
 - merge
 license: apache-2.0
-quantized_by: bartowski
-pipeline_tag: text-generation
 ---
 
-## Exllama v2 Quantizations of Excalibur-7b
 
-Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.15">turboderp's ExLlamaV2 v0.0.15</a> for quantization.
+# Excalibur-7b
 
-<b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b>
+<img src="https://i.imgur.com/viIO4WT.png" width="550"/>
 
-Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
+<i>Image generated with Envoid's [Model9](https://huggingface.co/Envoid/model9) SDXL model </i>
 
-Original model: https://huggingface.co/InferenceIllusionist/Excalibur-7b
+GGUFs can be found [here](https://huggingface.co/InferenceIllusionist/Excalibur-7b-GGUF)
 
-| Branch | Bits | lm_head bits | VRAM (4k) | VRAM (16k) | VRAM (32k) | Description |
-| ----- | ---- | ------- | ------ | ------ | ------ | ------------ |
-| [8_0](https://huggingface.co/bartowski/Excalibur-7b-exl2/tree/8_0) | 8.0 | 8.0 | 8.4 GB | 9.8 GB | 11.8 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. |
-| [6_5](https://huggingface.co/bartowski/Excalibur-7b-exl2/tree/6_5) | 6.5 | 8.0 | 7.2 GB | 8.6 GB | 10.6 GB | Very similar to 8.0, good tradeoff of size vs performance, **recommended**. |
-| [5_0](https://huggingface.co/bartowski/Excalibur-7b-exl2/tree/5_0) | 5.0 | 6.0 | 6.0 GB | 7.4 GB |  9.4 GB | Slightly lower quality vs 6.5, but usable on 8GB cards. |
-| [4_25](https://huggingface.co/bartowski/Excalibur-7b-exl2/tree/4_25) | 4.25 | 6.0 | 5.3 GB | 6.7 GB | 8.7 GB | GPTQ equivalent bits per weight, slightly higher quality. |
-| [3_5](https://huggingface.co/bartowski/Excalibur-7b-exl2/tree/3_5) | 3.5 | 6.0 | 4.7 GB | 6.1 GB | 8.1 GB | Lower quality, only use if you have to. |
+### Performance Comparison
+| Name | Avg. | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
+| ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- |
+| <b>Excalibur-7b</b> | <u><b>73.6</b></u> | <u><b>69.71</b></u> | <u><b>87.56</b></u> | <u><b>65.66</b></u> | <u><b>67.24</b></u> | <u><b>82.79</b></u> | <u><b>68.61</b></u> |
+| Magic-Dolphin-7b | 67.48 | 65.78 | 85.61 | 64.64 | 58.01 | 79.64 | 51.18 |
+| merlinite-7b | 64 | 63.65 | 84.52 | 64.91 | 50.15 | 79.72 | 41.09 |
+[* Open LLM Leaderboard Dataset](https://huggingface.co/datasets/open-llm-leaderboard/details_InferenceIllusionist__Excalibur-7B)
 
-## Download instructions
+### Methodology
+[Magic-Dolphin-7b](https://huggingface.co/InferenceIllusionist/Magic-Dolphin-7b) was an unexpected surprise. Profoundly satisfied with it as a first attempt. For this follow-up I wanted to target the MMLU benchmark specifically.
+The challenge this time was placing more weight on Merlinite-7b as an unknown quantity that hasn't been in the spotlight despite its novel LAB tuning method.
 
-With git:
+<b>Excalibur-7b</b> builds on past success and is the culmination of several learnings:
+* Measuring KL-divergences for new quantization types brought a deeper understanding of benchmarking and assessing model performance
+* This signifcantly sped up the testing process by using MMLU as a base, narrowing down over 10 candidate linear merges to 1: merliniteX-blockB1
+* Reaching the limitations of linear merging necessitated a pivot to reviewing the viability of SLERP, DARE-TIES, and Passthrough methods
+* Thus a competing candidate merge pool was tested between different merge algorithms. Once more the list was narrowed from 10 candidates to 1: merliniteX-blockF2
+* merliniteX-blockF2 (SLERP of Magic-Dolphin-7B and jaskier-7b-dpo in unorthadox proportions) was originally planned for release earlier this week
+* Instead -blockB1 and -blockF2 were merged and the results were placed head to head in a final round of tests. Ultimately a more conventional execution of SLERP showed the best results for the final step.
 
-```shell
-git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/Excalibur-7b-exl2 Excalibur-7b-exl2-6_5
-```
 
-With huggingface hub (credit to TheBloke for instructions):
 
-```shell
-pip3 install huggingface-hub
-```
+# Sample Question
 
-To download the `main` (only useful if you only care about measurement.json) branch to a folder called `Excalibur-7b-exl2`:
+<img src="https://i.imgur.com/fdFYIhv.jpeg" width="550"/>
 
-```shell
-mkdir Excalibur-7b-exl2
-huggingface-cli download bartowski/Excalibur-7b-exl2 --local-dir Excalibur-7b-exl2 --local-dir-use-symlinks False
-```
+# Bonus Question - Vision Capabilities
 
-To download from a different branch, add the `--revision` parameter:
+<b>Requires additional [mistral-7b-mmproj-v1.5-Q4_1.gguf](https://huggingface.co/koboldcpp/mmproj/tree/main) file for vision functionality</b>
+<img src="https://i.imgur.com/4wbUrjf.jpeg" width="550"/>
 
-Linux:
 
-```shell
-mkdir Excalibur-7b-exl2-6_5
-huggingface-cli download bartowski/Excalibur-7b-exl2 --revision 6_5 --local-dir Excalibur-7b-exl2-6_5 --local-dir-use-symlinks False
-```
 
-Windows (which apparently doesn't like _ in folders sometimes?):
 
-```shell
-mkdir Excalibur-7b-exl2-6.5
-huggingface-cli download bartowski/Excalibur-7b-exl2 --revision 6_5 --local-dir Excalibur-7b-exl2-6.5 --local-dir-use-symlinks False
-```
+This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
 
-Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
+## Merge Details
+### Merge Method
+
+This model was merged using the SLERP merge method.
+
+### Models Merged
+
+The following models were included in the merge:
+* models/merliniteX-blockB1
+* models/merliniteX-blockF2
+
+### Configuration
+
+The following YAML configuration was used to produce this model:
+
+```yaml
+slices:
+  - sources:
+      - model: models/merliniteX-blockF2
+        layer_range: [0, 32]
+      - model: models/merliniteX-blockB1
+        layer_range: [0, 32]
+# or, the equivalent models: syntax:
+# models:
+#   - model: psmathur/orca_mini_v3_13b
+#   - model: garage-bAInd/Platypus2-13B
+merge_method: slerp
+base_model: models/merliniteX-blockF2
+parameters:
+  t:
+    - filter: self_attn
+      value: [1, 0.7, 0.3, 0.5, 0]
+    - filter: mlp
+      value: [0, 0.3, 0.7, 0.5, 1]
+    - value: 0.5 # fallback for rest of tensors
+dtype: float16
+
+```

config.json

@@ -0,0 +1,26 @@
+{
+  "_name_or_path": "models/merliniteX-blockF2",
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "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": "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": "float16",
+  "transformers_version": "4.37.1",
+  "use_cache": true,
+  "vocab_size": 32000
+}

mergekit_config.yml

@@ -0,0 +1,20 @@
+slices:
+  - sources:
+      - model: models/merliniteX-blockF2
+        layer_range: [0, 32]
+      - model: models/merliniteX-blockB1
+        layer_range: [0, 32]
+# or, the equivalent models: syntax:
+# models:
+#   - model: psmathur/orca_mini_v3_13b
+#   - model: garage-bAInd/Platypus2-13B
+merge_method: slerp
+base_model: models/merliniteX-blockF2
+parameters:
+  t:
+    - filter: self_attn
+      value: [1, 0.7, 0.3, 0.5, 0]
+    - filter: mlp
+      value: [0, 0.3, 0.7, 0.5, 1]
+    - value: 0.5 # fallback for rest of tensors
+dtype: float16

model.safetensors.index.json

@@ -0,0 +1 @@
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