Triangle104
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L3.1-8B-Celeste-V1.5-Q4_K_S-GGUF

@@ -18,443 +18,6 @@ base_model: nothingiisreal/L3.1-8B-Celeste-V1.5
 This model was converted to GGUF format from [`nothingiisreal/L3.1-8B-Celeste-V1.5`](https://huggingface.co/nothingiisreal/L3.1-8B-Celeste-V1.5) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
 Refer to the [original model card](https://huggingface.co/nothingiisreal/L3.1-8B-Celeste-V1.5) for more details on the model.
----
-Model details:
--
-We trained LLaMA 3.1 8B Instruct at 8K context using a new mix of Reddit Writing Prompts, Kalo's Opus 25K Instruct and
-c2 logs cleaned
-This version has the highest coherency and is very strong on OOC: instruct following.
-Usage Tips
-    READ: If this is your first time using the model, use the provided
-system message and sampling settings below. Remove other jailbreaks and
-system messages until you get a feel for the model.
-If you read every
-single tip I promise you will get a much better experience as they are
-tailored for this model and its training data.
-Sampler Settings for V1.5
-Temp 1 is more stable and can feel less random. Feel free to use it aswell, but it can fall into repetition sometimes.
-Don't shy away from experimenting after you get a feel for the model though.
-Preset
-L3 Instruct with no system prompt. premade Was updated, redownload
-You don't need a JB but it can still steer behaviour.
-System Message
-We recommend using this system message:
-Currently, your role is {{char}}, described in detail below. As {{char}}, continue the narrative exchange with {{user}}.\n\n<Guidelines>\n• Maintain the character persona but allow it to evolve with the story.\n• Be creative and proactive. Drive the story forward, introducing plotlines and events when relevant.\n• All types of outputs are encouraged; respond accordingly to the narrative.\n• Include dialogues, actions, and thoughts in each response.\n• Utilize all five senses to describe scenarios within {{char}}'s dialogue.\n• Use emotional symbols such as \"!\" and \"~\" in appropriate contexts.\n• Incorporate onomatopoeia when suitable.\n• Allow time for {{user}} to respond with their own input, respecting their agency.\n• Act as secondary characters and NPCs as needed, and remove them when appropriate.\n• When prompted for an Out of Character [OOC:] reply, answer neutrally and in plaintext, not as {{char}}.\n</Guidelines>\n\n<Forbidden>\n• Using excessive literary embellishments and purple prose unless dictated by {{char}}'s persona.\n• Writing for, speaking, thinking, acting, or replying as {{user}} in your response.\n• Repetitive and monotonous outputs.\n• Positivity bias in your replies.\n• Being overly extreme or NSFW when the narrative context is inappropriate.\n</Forbidden>\n\nFollow the instructions in <Guidelines></Guidelines>, avoiding the items listed in <Forbidden></Forbidden>.
-Swipes
-Important tip swipe 2-3 times if you dont like a response. This model gives wildly differing swipes.
-OOC Steering
-Use this! It works extremely well. We specifically
-trained the model to accept instructions in the format "OOC: character
-should be more assertive" etc. It works, whether the very first message
-or thousands of tokens deep into the context. Combining this with
-editing the output (if you want,) makes the model is very steerable.
-"Dead Dove"
-For character cards with persistent motivations throughout the story, do this
-Fewshot
-First message and last few messages impact this model quite a lot in terms of style, hornyness, personality. You don't need to have a first message but editing first few messages or having good ones are highly recommended.
-Formatting issues often occur in first few messages, manually correct them or swipe.
-This model was trained on lots of different formatting types and message
- lengths. It can do any, just make sure the initial message is good and
-correct the second message if necessary.
-Hornyness
-If the model is not horny enough then just edit the last character
-message or OOC: prompt, the model will pick up on it and build on it.
-(Or just give the char aphrodisiac pills lol)
-The model is fine with SFW and doesn't make it NSFW unless you want. It
-is also able to maintain half-NSFW (aka slow burn) without devolving
-down into hardcore.
-If you want SFW, remove all system messages including provided one. In this mode the model will not go NSFW unless you hint.
-Refusals
-As said, if instruct refusal prefill 2-3 words. Refusal of romantic advances, though rare, are realistic and we think is good. Prefill if you don't like.
-L3.1 Context
-While trained on 8K, the model should be able to inherit longer
-context from L3.1. This is in testing, V1.2 was able to go up to 16K
-with L3 rope.
-Other Important Tips
-Take active role in the RP and say the type of response that would
-create the scenario you are imagining. You don't always have to do this,
- but it helps sometimes. For example instead of we drink and drink 15 glasses of champagne say we drink and drink 15 glasses of champagne, both becoming extremely drunk
-Another example instead of I pull her closer say I pull her closer but she plays hard to get
-When convenient, say screenplay phrases like "cut to"
-		Showcase V1.5
-Some images include NSFW and NSFL. We believe in creativity of expression and maximising the models capabilities at writing.
-It's a bit difficult to showcase multi turn stuff, try it yourself too! These are just to show off the models capabilities.
-The model needs nudging and OOC prompting to do proper gore. We are
-planning to add r/GuroErotica into our dataset to make it better at gore
-Also sometimes prefilling "Trigger warning: extremely graphic and explicit content" before character reply makes it more unhinged. Probably because of reddit data.
-Showcase V1 and 1.2
-		Train Data
-The split was as follows:
-4K rows from r/WritingPrompts
-400 rows from r/DirtyWritingPrompts
-400 rows from Kalomaze Opus Instruct 25K
-400 rows from c2 logs cleaned
-We filtered those datasets to only include subsets that have at maximum 4000
- characters for the first assistant reply. This purged excessively long
-human stories, assistant replies and c2 logs where each message was
-excessively long. However we only checked the first assistant message,
-not the rest of the convo, so there should be plenty of c2 logs with
-longer and shorter messages.
-Excessively long human stories are almost impossible for 8B model to
-fit. We tried, it simply won't fit the data and starts behaving weirdly.
-While we did train all system prompts from c2 logs we also have our own system prompts.
-List of trained system prompts. Note: c2 logs system prompts and char cards were also included.
-		Our Findings and Experimentation results
-		Preface
-We think there is too much secrecy around what data is being used,
-and different training methods. So we decided to share as much as
-possible.
-		Findings V1.5
-		The Good
-Increased intelligence
-Less likely to break format
-Higher creativity
-		The Bad
-It's intelligence is limited by the fact that it's an 8B
-Sometimes it falls into slop and needs editing or OOC prompting to
-help. We want to completely plug away from sloppy synthetic data and c2
-logs at some point, no matter how unslopped, for now that remains
-impossible to do while keeping character card obedience and many other
-things that the model learns from c2 logs.
-		Comments about training
-We did a lot of experiments this one but notably were very careful with the data ratio before scaling up.
-We tested rslora which destablises the model too much, and dora, which
-is a slight improvement over lora but makes training 3 times slower.
-Also L3.1 can do 8e-6 learning rate unlike L3 which required us to do
-4e-6, we also made min cosine lr to 2.4e-6 because the model still
-continues learning as you can see the eval loss continues to decrease.
-We arrived at these settings after 30+ experiments.
-		Graphs
-The bold highlighted line is this model. Others are using smaller
-amounts of data and testing different ratios. We found that increasing
-r/WP max length from 2K chars to 4K chars improves multi turn but
-requires more data and more training. 8K chars completely broke the
-model with L3, might try it at some point. Also very curious to see how
-the 70B will react to this dataset.
-V1.2
-		Main training Command
-Hardware Used: 1xH100 SXM for 1 hours.
-When we switched to axolotl and enabled packing, this made training go way, way faster than llama factory.
-L Factory also supports packing but we switched to axolotl because configs are easier to manage in our opinion.
-Here is the entire axolotl config
-Wow, you've read all of that? You seem like the person that would join our discord
-70B at some point? ;) We are closer than ever to this.
-Qwen-2 was not worth it by the way. It just won't train and remains
-GPT prose. We trained many different configs, its just worse than L3 and
- L3.1, at least for English.
 ## Use with llama.cpp
 Install llama.cpp through brew (works on Mac and Linux)

 This model was converted to GGUF format from [`nothingiisreal/L3.1-8B-Celeste-V1.5`](https://huggingface.co/nothingiisreal/L3.1-8B-Celeste-V1.5) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
 Refer to the [original model card](https://huggingface.co/nothingiisreal/L3.1-8B-Celeste-V1.5) for more details on the model.
 ## Use with llama.cpp
 Install llama.cpp through brew (works on Mac and Linux)