crest-e2-clash-e2-faint

README.md

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+---
+base_model:
+- mistralai/Mixtral-8x7B-v0.1
+library_name: transformers
+tags:
+- mergekit
+- merge
+
+---
+# uphill-instruct-crest-e2-clash-e2-lime-faint-try1
+
+This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
+
+## Merge Details
+### Merge Method
+
+This model was merged using the [DARE TIES](https://arxiv.org/abs/2311.03099) merge method using [mistralai/Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) as a base.
+
+### Models Merged
+
+The following models were included in the merge:
+* ./Mixtral-8x7B-Yes-Instruct-LimaRP
+* ./uphill-instruct-crest-e2-nolime
+* ./uphill-pure-clash-0.2-e2
+
+### Configuration
+
+The following YAML configuration was used to produce this model:
+
+```yaml
+# Faint tecnnique, crest-e2 clash-e1
+#
+# review:
+# - Instruction-following: 
+# - Swerve: 
+# - Word choice: 
+# - Rhythm, cadence: 
+# - Notes: 
+#     - 
+#
+# - Design: 
+#   The idea here is to cut crush -- formerly the very cornerstone
+#   of our merges -- completely out. it's very good for word choice
+#   but crest is, too. The only problem is I seem to remember that
+#   crest is overfit. So, we make it faint.
+#
+# Note: nearly two years later I'm trying to bring Mixtral
+# back from the dead. There are multiple reasons:
+# 1. Mistral-Small is kind of crap and smells like slop.
+#    Hell, even the comprehension felt weak but maybe that's
+#    just how I tried to sample it.
+# 2. Llama3 hasn't been interesting and is definitely crammed
+#    with slop.
+# 3. Mixtral is probably the least synthetic-trained sounding
+#    of all the OG models. Even when I tried the Quen shit
+#    it seemed to be just openai. Mixtral is still sloppy.
+#
+# So, the pieces that are ours are uphill: non-instruct lora
+# being applied to the instruct rawdog without an intermediate
+# step.
+#
+# Obviously we're using pure elemental antisoc loras, hush's shit
+# but not her merge because the merges aren't "uphill", as in, 
+# a lora made with "mixtral non-instruct" applied straight to
+# the instruct with loraize.
+#
+# The notion, which came to me in the middle of the night, is
+# to have the hush loras be only barely present layer-wise but
+# weighted heavily. Likewise with LimaRP, send uphill from 
+# doctor-shotgun's qlora straight into mixtral-instruct
+#
+# My hypothesis is that we should get really fucking close to
+# pure-ass mixtral-instruct in terms of attention, but that
+# we're weighting really hard not to write like it. I have no
+# idea if that's how it works--I'm a fucking caveman.
+#
+# What I'm given to understand, and I'm way out of my depth,
+# is that the antisoc layers won't have blotched the instruct
+# as badly as they usually do, but when they're triggered they
+# are dominant. It's entirely possible I've got no idea what
+# I'm saying.
+
+# Model descriptions:
+#  - crush: poetry; we have all checkpoints
+#  - crest: fic; we only have e2 for this
+#  - clash: novels (I think); we have all checkpoints for 0.2
+models:
+  # I wonder what happens if we just hurl this out the window
+  #  - model: mistralai/Mixtral-8x7B-Instruct-v0.1
+  #    parameters:
+  #      density: 0.9
+  #      weight: 0.55
+  #
+  # crest is fic
+  - model: ./uphill-instruct-crest-e2-nolime
+    # i found lima in this, I need to cook another
+    parameters:
+      density: 0.4
+      weight: 0.3
+  # This is actually an uphill lima but I didn't name it that way.
+  - model: ./Mixtral-8x7B-Yes-Instruct-LimaRP
+    parameters:
+      # Still just a breath of layers from the thing
+      density: 0.2
+      # I am gimping its weight compared to hush tunes because limarp has too
+      # much ai-slop and amateur-smut cliche slop. Honestly, if there were
+      # something better than limarp I'd try to train it myself but I don't
+      # know if there is.
+      weight: 0.1
+  # Pure uphill clash at e2. Also more weight.
+  - model: ./uphill-pure-clash-0.2-e2
+    parameters:
+      density: 0.5
+      weight: 0.6
+# della sucked ass so dare_ties it is
+merge_method: dare_ties
+# I know all of these look like instruct but the lora
+# is actually not so we go to the base base
+base_model: mistralai/Mixtral-8x7B-v0.1
+parameters:
+  normalize: true
+  int8_mask: true
+dtype: bfloat16
+
+```

config.json

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+{
+  "architectures": [
+    "MixtralForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "head_dim": null,
+  "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": 8,
+  "output_router_logits": false,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 1000000.0,
+  "router_aux_loss_coef": 0.02,
+  "router_jitter_noise": 0.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.52.4",
+  "use_cache": true,
+  "vocab_size": 32000
+}

mergekit_config.yml

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+# Faint tecnnique, crest-e2 clash-e1
+#
+# review:
+# - Instruction-following: 
+# - Swerve: 
+# - Word choice: 
+# - Rhythm, cadence: 
+# - Notes: 
+#     - 
+#
+# - Design: 
+#   The idea here is to cut crush -- formerly the very cornerstone
+#   of our merges -- completely out. it's very good for word choice
+#   but crest is, too. The only problem is I seem to remember that
+#   crest is overfit. So, we make it faint.
+#
+# Note: nearly two years later I'm trying to bring Mixtral
+# back from the dead. There are multiple reasons:
+# 1. Mistral-Small is kind of crap and smells like slop.
+#    Hell, even the comprehension felt weak but maybe that's
+#    just how I tried to sample it.
+# 2. Llama3 hasn't been interesting and is definitely crammed
+#    with slop.
+# 3. Mixtral is probably the least synthetic-trained sounding
+#    of all the OG models. Even when I tried the Quen shit
+#    it seemed to be just openai. Mixtral is still sloppy.
+#
+# So, the pieces that are ours are uphill: non-instruct lora
+# being applied to the instruct rawdog without an intermediate
+# step.
+#
+# Obviously we're using pure elemental antisoc loras, hush's shit
+# but not her merge because the merges aren't "uphill", as in, 
+# a lora made with "mixtral non-instruct" applied straight to
+# the instruct with loraize.
+#
+# The notion, which came to me in the middle of the night, is
+# to have the hush loras be only barely present layer-wise but
+# weighted heavily. Likewise with LimaRP, send uphill from 
+# doctor-shotgun's qlora straight into mixtral-instruct
+#
+# My hypothesis is that we should get really fucking close to
+# pure-ass mixtral-instruct in terms of attention, but that
+# we're weighting really hard not to write like it. I have no
+# idea if that's how it works--I'm a fucking caveman.
+#
+# What I'm given to understand, and I'm way out of my depth,
+# is that the antisoc layers won't have blotched the instruct
+# as badly as they usually do, but when they're triggered they
+# are dominant. It's entirely possible I've got no idea what
+# I'm saying.
+
+# Model descriptions:
+#  - crush: poetry; we have all checkpoints
+#  - crest: fic; we only have e2 for this
+#  - clash: novels (I think); we have all checkpoints for 0.2
+models:
+  # I wonder what happens if we just hurl this out the window
+  #  - model: mistralai/Mixtral-8x7B-Instruct-v0.1
+  #    parameters:
+  #      density: 0.9
+  #      weight: 0.55
+  #
+  # crest is fic
+  - model: ./uphill-instruct-crest-e2-nolime
+    # i found lima in this, I need to cook another
+    parameters:
+      density: 0.4
+      weight: 0.3
+  # This is actually an uphill lima but I didn't name it that way.
+  - model: ./Mixtral-8x7B-Yes-Instruct-LimaRP
+    parameters:
+      # Still just a breath of layers from the thing
+      density: 0.2
+      # I am gimping its weight compared to hush tunes because limarp has too
+      # much ai-slop and amateur-smut cliche slop. Honestly, if there were
+      # something better than limarp I'd try to train it myself but I don't
+      # know if there is.
+      weight: 0.1
+  # Pure uphill clash at e2. Also more weight.
+  - model: ./uphill-pure-clash-0.2-e2
+    parameters:
+      density: 0.5
+      weight: 0.6
+# della sucked ass so dare_ties it is
+merge_method: dare_ties
+# I know all of these look like instruct but the lora
+# is actually not so we go to the base base
+base_model: mistralai/Mixtral-8x7B-v0.1
+parameters:
+  normalize: true
+  int8_mask: true
+dtype: bfloat16

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