Update README.md

README.md

@@ -11,9 +11,11 @@ tags:
 - merge
 - Yi
 ---
-# Yi 34B Merge v7
+# Yi 34B Merge v8
 
-A merge of several Yi 34B 200K models using the new DARE Ties method via mergekit. The goal is to create a merge model that excels at 32K+ context performance. 
+A merge of several Yi 34B 200K models using the new DARE Ties method via mergekit, quantized with exllamav2 on ~300K tokens of a sci-fi story, a fantasy story, and a vicuna chat for optimal long context storywriting performance.
+
+See the main model card: https://huggingface.co/brucethemoose/Yi-34B-200K-DARE-megamerge-v8
 
 ## Prompt template: Orca-Vicuna
 ```
@@ -24,135 +26,25 @@ ASSISTANT:
 It might recognize ChatML, and possibly Alpaca-like formats. Raw prompting as described here is also effective: https://old.reddit.com/r/LocalLLaMA/comments/18zqy4s/the_secret_to_writing_quality_stories_with_llms/
 
 
-
 ## Running
-Being a Yi model, try running a lower temperature with 0.02-0.06 MinP, a little repetition penalty, maybe mirostat with a low tau, and no other samplers. Yi tends to run "hot" by default, and it really needs a low temperature + MinP to cull the huge vocabulary.
-
-24GB GPUs can efficiently run Yi-34B-200K models at **45K-90K context** with exllamav2, and performant UIs like [exui](https://github.com/turboderp/exui). I go into more detail in this [post](https://old.reddit.com/r/LocalLLaMA/comments/1896igc/how_i_run_34b_models_at_75k_context_on_24gb_fast/). 16GB GPUs can still run the high context with aggressive quantization.
-
-I recommend exl2 quantizations profiled on data similar to the desired task. It is especially sensitive to the quantization data at low bpw. I've uploaded my own fiction-oriented quantizations here: https://huggingface.co/collections/brucethemoose/most-recent-merge-65742644ca03b6c514afa204
-
-To load/train this in full-context backends like transformers, you *must* change `max_position_embeddings` in config.json to a lower value than 200,000, otherwise you will OOM! I do not recommend running high context without context-efficient backends like exllamav2 or unsloth.
-
-
-## Testing Notes
-
-See: https://huggingface.co/brucethemoose/Yi-34B-200K-DARE-merge-v5#testing-notes
-
-A "4k" merge model was created to try and extend the context of SUS Chat and DPO-bagel before adding them to the merge: https://huggingface.co/brucethemoose/SUS-Bagel-200K-DARE-Test
 
-In addition, the weight gradients are biased towards Vicuna-format models in the first few layers to try and "emphasize" the Orca-Vicuna prompt template. How sucessful this is remains to be seen. 
+24GB GPUs can run 4bpw Yi-34B-200K models at **45K context** with exllamav2, and performant UIs like [exui](https://github.com/turboderp/exui). I go into more detail in this [post](https://old.reddit.com/r/LocalLLaMA/comments/1896igc/how_i_run_34b_models_at_75k_context_on_24gb_fast/)
 
 
-### Merge Method
+Being a Yi model, try running a lower temperature with 0.05+ MinP, a little repetition penalty, maybe mirostat with a low tau, and no other samplers. Yi tends to run "hot" by default, and it really needs a low temperature +  MinP to cull the huge vocabulary.
 
-This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using /home/alpha/Storage/Models/Raw/chargoddard_Yi-34B-200K-Llama as a base.
-
-### Models Merged
-
-The following models were included in the merge:
-* https://huggingface.co/kyujinpy/PlatYi-34B-200k-Q-FastChat
-* https://huggingface.co/jondurbin/bagel-34b-v0.2
-* https://huggingface.co/NousResearch/Nous-Capybara-34B
-* https://huggingface.co/migtissera/Tess-M-Creative-v1.0
-* https://huggingface.co/brucethemoose/SUS-Bagel-200K-DARE-Test
-* https://huggingface.co/Mihaiii/Pallas-0.5
-* https://huggingface.co/bhenrym14/airoboros-3_1-yi-34b-200k
-* https://huggingface.co/adamo1139/Yi-34B-200K-AEZAKMI-v2
-* https://huggingface.co/migtissera/Tess-34B-v1.4
-* https://huggingface.co/SUSTech/SUS-Chat-34B
-* https://huggingface.co/jondurbin/bagel-dpo-34b-v0.2
-* https://huggingface.co/chargoddard/Yi-34B-200K-Llama
-* https://huggingface.co/chargoddard/Yi-34B-Llama
-
-
-### Configuration
-
-The following YAML configuration was used to produce this model:
-
-```yaml
-models:
-  - model: /home/alpha/Storage/Models/Raw/chargoddard_Yi-34B-200K-Llama
-    # No parameters necessary for base model
-  - model: /home/alpha/Storage/Models/Raw/migtissera_Tess-34B-v1.4
-    parameters:
-      weight: [0.23, 0.125, 0.125, 0.125, 0.125, 0.125]
-      density: 0.59
-  - model: /home/alpha/Models/Raw/Mihaiii_Pallas-0.5
-    parameters:
-      weight: [0.23, 0.125, 0.125, 0.125, 0.125, 0.125]
-      density: 0.59
-  - model: /home/alpha//Storage/Models/Raw/bhenrym14_airoboros-3_1-yi-34b-200k
-    parameters:
-      weight: [0.02, 0.106, 0.106, 0.106, 0.106, 0.106]
-      density: 0.59
-  - model: /home/alpha/Storage/Models/Raw/jondurbin_bagel-34b-v0.2
-    #Only the SFT in the main merge since the DPO version seems to have no long context ability at all
-    parameters:
-      weight: [0.02, 0.100, 0.100, 0.100, 0.100, 0.100]
-      density: 0.4
-  - model: /home/alpha/Storage/Models/Raw/kyujinpy_PlatYi-34B-200k-Q-FastChat
-    parameters:
-      weight: [0.02, 0.100, 0.100, 0.100, 0.100, 0.100]
-      density: 0.59
-  #- model: /home/alpha/Storage/Models/Raw/ehartford_dolphin-2.2-yi-34b-200k
-  #  Dolphin 200K seems to be funky according to multiple leaderboards and perplexity tests?
-  #  parameters:
-  #    weight: 0.15
-  #    density: 0.6
-  - model: /home/alpha/Models/Raw/adamo1139_Yi-34B-200K-AEZAKMI-v2
-    parameters:
-      weight: [0.02, 0.110, 0.110, 0.110, 0.110, 0.110]
-      density: 0.59
-  - model: /home/alpha/Storage/Models/Raw/Nous-Capybara-34B
-    parameters:
-      weight:  [0.22, 0.126, 0.126, 0.126, 0.126, 0.126]
-      density: 0.59
-  - model: /home/alpha/Storage/Models/Raw/4kmerge
-    parameters:
-      weight: [0.02,  0.108, 0.108, 0.108, 0.108, 0.108]
-      density: 0.5
-  - model: /home/alpha/Models/Raw/migtissera_Tess-M-Creative-v1.0
-    parameters:
-      weight: [0.22, 0.100, 0.100, 0.100, 0.100, 0.10]
-      density: 0.59
-merge_method: dare_ties
-tokenizer_source: union
-base_model: /home/alpha/Storage/Models/Raw/chargoddard_Yi-34B-200K-Llama
-parameters:
-  int8_mask: true
-dtype: bfloat16
+## Quantization Commands
 
+First pass:
 ```
+python /home/alpha/AI/exllamav2/convert.py --in_dir /home/alpha/FastModels/v8/v8 -o /home/alpha/FastModels/scratch -om /home/alpha/FastModels/v8meas.json --cal_dataset /home/alpha/Documents/stories.parquet -ml 32768 -mr 8 -ss 4096 -b 4.0 -hb 6 -nr
 
-The following config was used for the "4kmerge" model:
+```
 
-```yaml
-models:
-  - model: /home/alpha/Models/Raw/chargoddard_Yi-34B-Llama
-  # No parameters necessary for base model
-  - model: /home/alpha/Storage/Models/Raw/chargoddard_Yi-34B-200K-Llama
-    parameters:
-      weight: 0.5
-      density: 1
-  - model: /home/alpha/Models/Raw/SUSTech_SUS-Chat-34B
-    parameters:
-      weight: 0.2
-      density: 0.12
-  - model: /home/alpha/Models/Raw/jondurbin_bagel-dpo-34b-v0.2
-    parameters:
-      weight: 0.2
-      density: 0.15
-  - model: /home/alpha/Models/Raw/jondurbin_bagel-34b-v0.2
-    parameters:
-      weight: 0.1
-      density: 0.12
-merge_method: dare_ties
-tokenizer_source: union
-base_model: /home/alpha/Models/Raw/chargoddard_Yi-34B-Llama
-parameters:
-  int8_mask: true
-dtype: bfloat16
+Second pass:
 ```
+python /home/alpha/AI/exllamav2/convert.py --in_dir /home/alpha/FastModels/v8/v8 -o /home/alpha/FastModels/scratch -m /home/alpha/FastModels/v8meas.json --cal_dataset /home/alpha/Documents/stories.parquet -l 12288 -r 26 -ml 32768 -mr 8 -ss 4096 -b 4.0 -hb 6 -cf /home/alpha/FastModels/v8-exl2-4bpw-fiction -nr
+```
+