2024-05-28 update

README.md

@@ -12,7 +12,7 @@ _"The only difference between Science and screwing around is writing it down."_
 
 # The LLM Creativity benchmark
 
-_Last benchmark update: 15 May 2024_
+_Last benchmark update: 28 May 2024_
 
 The goal of this benchmark is to evaluate the ability of Large Language Models to be used
 as an **uncensored creative writing assistant**. Human evaluation of the results is done manually,
@@ -37,24 +37,42 @@ The questions can be split half-half in 2 possible ways:
 - **Second best _large_ model**: [CohereForAI/c4ai-command-r-plus](https://huggingface.co/CohereForAI/c4ai-command-r-plus). Very close to the above choice, but 4 times slower! On my m2 max with 38 GPU cores, I get an inference speed of **3.88 tok/s** with q5_km. However it gives different results from WizardLM, and it can definitely be worth using.
 - **Best _medium_ model**: [sophosympatheia/Midnight-Miqu-70B-v1.5](https://huggingface.co/sophosympatheia/Midnight-Miqu-70B-v1.5)
 - **Best _small_ model**: [CohereForAI/c4ai-command-r-v01](https://huggingface.co/CohereForAI/c4ai-command-r-v01)
-- **Best _tiny_ model**: [froggeric/WestLake-10.7b-v2](https://huggingface.co/froggeric/WestLake-10.7B-v2-GGUF)
+- **Best _tiny_ model**: [daybreak-kunoichi-2dpo-7b](https://huggingface.co/crestf411/daybreak-kunoichi-2dpo-7b) and [froggeric/WestLake-10.7b-v2](https://huggingface.co/froggeric/WestLake-10.7B-v2-GGUF)
 
 # Results
 
-![benchmark-results.png](https://cdn-uploads.huggingface.co/production/uploads/65a681d3da9f6df1410562e9/cduDXpf8Ur1nHDTV4jq-G.png)
+![benchmark-results.png](https://cdn-uploads.huggingface.co/production/uploads/65a681d3da9f6df1410562e9/RCkFma06SsgBadRXx0mJe.png)
 
 # Remarks about some of the models
 
 
 [WizardLM-2-8x22B](https://huggingface.co/alpindale/WizardLM-2-8x22B)\
+Even though the score is close to the iq4_xs version, **the _q4_km_ quant definitely feels smarter and writes better text than the
+_iq4_xs_ quant**. Unfortunately with my 96GB of RAM, once I go over 8k context size, it fails. Best to use it (for me), is until 8k,
+and then switch to the iq4_xs version which can accomodate a much larger context size.
 I used the imatrix quantisation from [mradermacher](https://huggingface.co/mradermacher/WizardLM-2-8x22B-i1-GGUF)\
 Fast inference! Great quality writing, that feels a lot different from most other models.
 Unrushed, less repetitions. Good at following instructions.
 Non creative writing tasks are also better, with more details and useful additional information.
 This is a huge improvement over the original **Mixtral-8x22B**.
 My new favourite model.\
+Inference speed: **11.22 tok/s** (q4_km on m2 max with 38 gpu cores)
 Inference speed: **11.81 tok/s** (iq4_xs on m2 max with 38 gpu cores)
 
+[daybreak-kunoichi-2dpo-7b](https://huggingface.co/crestf411/daybreak-kunoichi-2dpo-7b)
+Absolutely no guard rails! No refusal, no censorship. Good writing, but very hardcore.
+
+[jukofyork/Dark-Miqu-70B](https://huggingface.co/jukofyork/Dark-Miqu-70B)
+Can write long and detailed narratives, but often continues writing slightly beyond the requested stop point.
+It has some slight difficulties at following instructions. But the biggest problem by far is it is marred by
+too many spelling and grammar mistakes.
+
+[dreamgen/opus-v1-34b](https://huggingface.co/dreamgen/opus-v1-34b)
+Writes complete nonsense: no logic, absurd plots. Poor writing style. Lots of canned expressions used again and again.
+
+**Previously:**
+
+
 [llmixer/BigWeave-v16-103b](https://huggingface.co/llmixer/BigWeave-v16-103b)\
 A miqu self-merge, which is the winner of the BigWeave experiments. I was hoping for an improvement over the
 existing _traditional_ 103B and 120B self-merges, but although it comes close, it is still not as good.
@@ -83,10 +101,6 @@ Experiments in trying to get a better self-merge of miqu-1, by using @jukofyork
 More info about the _attenuation_ is available in this [discussion](https://huggingface.co/wolfram/miqu-1-120b/discussions/4).
 So far no better results.
 
-
-**Previously:**
-
-
 [CohereForAI/c4ai-command-r-plus](https://huggingface.co/CohereForAI/c4ai-command-r-plus)\
 A big step up for open LLM models. Has a tendency to work best by giving it the beginning of an answer
 for completion. To get the best of it, I recommend getting familiar with the