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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.model.bak filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: other
+license_name: yi-license
+license_link: https://huggingface.co/01-ai/Yi-34B/blob/main/LICENSE
+language:
+- en
+library_name: transformers
+base_model: []
+tags:
+- mergekit
+- merge
+- Yi
+---
+# Yi 34B Merge v7
+
+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. 
+
+## Prompt template: Orca-Vicuna
+```
+SYSTEM: {system_message}
+USER: {prompt}
+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. 
+
+
+### Merge Method
+
+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
+
+```
+
+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
+```
+
+

config.json

@@ -0,0 +1,29 @@
+{
+  "_name_or_path": "/home/alpha/Storage/Models/Raw/chargoddard_Yi-34B-200K-Llama",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 7168,
+  "initializer_range": 0.02,
+  "intermediate_size": 20480,
+  "max_position_embeddings": 200000,
+  "model_type": "llama",
+  "num_attention_heads": 56,
+  "num_hidden_layers": 60,
+  "num_key_value_heads": 8,
+  "pad_token_id": 0,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 5000000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.36.2",
+  "use_cache": true,
+  "vocab_size": 64002
+}

mergekit_config.yml

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+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

model.safetensors.index.json

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