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--- |
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base_model: Sao10K/L3-8B-Stheno-v3.3-32K |
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quantized_by: Lewdiculous |
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library_name: transformers |
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license: cc-by-nc-4.0 |
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inference: false |
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language: |
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- en |
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tags: |
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- roleplay |
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- llama3 |
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- sillytavern |
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--- |
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# #roleplay #sillytavern #llama3 |
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My GGUF-IQ-Imatrix quants for [**Sao10K/L3-8B-Stheno-v3.3-32K**](https://huggingface.co/Sao10K/L3-8B-Stheno-v3.3-32K). |
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**Sao10K** with Stheno **yet** again, now bigger and better than ever! <br> |
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I recommend checking his page for feedback and support. |
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> [!IMPORTANT] |
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> **Quantization process:** <br> |
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> Imatrix data was generated from the FP16-GGUF and conversions directly from the BF16-GGUF. <br> |
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> This is a bit more disk and compute intensive but hopefully avoids any losses during conversion. <br> |
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> To run this model, please use the [**latest version of KoboldCpp**](https://github.com/LostRuins/koboldcpp/releases/latest). <br> |
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> If you noticed any issues let me know in the discussions. |
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> [!NOTE] |
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> **General usage:** <br> |
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> For **8GB VRAM** GPUs, I recommend the **Q4_K_M-imat** (4.89 BPW) quant for up to 12288 context sizes. <br> |
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> |
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> **Presets:** <br> |
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> Some compatible SillyTavern presets can be found [**here (Virt's Roleplay Presets)**](https://huggingface.co/Virt-io/SillyTavern-Presets). <br> |
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> Check [**discussions such as this one**](https://huggingface.co/Virt-io/SillyTavern-Presets/discussions/5#664d6fb87c563d4d95151baa) for other recommendations and samplers. |
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<details> |
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<summary>⇲ Click here to expand/hide information – General chart with relative quant parformances.</summary> |
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> [!NOTE] |
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> **Recommended read:** <br> |
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> |
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> [**"Which GGUF is right for me? (Opinionated)" by Artefact2**](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) |
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> |
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> *Click the image to view full size.* |
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>  |
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</details> |
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> [!TIP] |
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> **Personal-support:** <br> |
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> I apologize for disrupting your experience. <br> |
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> Eventually I may be able to use a dedicated server for this, but for now hopefully these quants are helpful. <br> |
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> If you **want** and you are **able to**... <br> |
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> You can [**spare some change over here (Ko-fi)**](https://ko-fi.com/Lewdiculous). <br> |
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> |
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> **Author-support:** <br> |
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> You can support the author [**at their own page**](https://ko-fi.com/sao10k). |
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<details> |
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<summary>Original model card information.</summary> |
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## **Original card:** |
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Trained with compute from [Backyard.ai](https://backyard.ai/) | Thanks to them and @dynafire for helping me out. |
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--- |
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Training Details: |
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<br>Trained at 8K Context -> Expanded to 32K Context with PoSE training. |
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Dataset Modifications: |
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<br>\- Further Cleaned up Roleplaying Samples -> Quality Check |
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<br>\- Removed Low Quality Samples from Manual Check -> Increased Baseline Quality Floor |
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<br>\- More Creative Writing Samples -> 2x Samples |
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<br>\- Remade and Refined Detailed Instruct Data |
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Notes: |
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<br>\- Training run is much less aggressive than previous Stheno versions. |
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<br>\- This model works when tested in bf16 with the same configs as within the file. |
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<br>\- I do not know the effects quantisation has on it. |
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<br>\- Roleplays pretty well. Feels nice in my opinion. |
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<br>\- It has some issues on long context understanding and reasoning. Much better vs rope scaling normally though, so that is a plus. |
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<br>\- Reminder, this isn't a native 32K model. It has it's issues, but it's coherent and working well. |
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Sanity Check // Needle in a Haystack Results: |
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<br>\- This is not as complex as RULER or NIAN, but it's a basic evaluator. Some improper train examples had Haystack scores ranging from Red to Orange for most of the extended contexts. |
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Wandb Run: |
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--- |
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Relevant Axolotl Configurations: |
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<br>-> Taken from [winglian/Llama-3-8b-64k-PoSE](https://huggingface.co/winglian/Llama-3-8b-64k-PoSE) |
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<br>\- I tried to find my own configs, hours of tinkering but the one he used worked best, so I stuck to it. |
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<br>\- 2M Rope Theta had the best loss results during training compared to other values. |
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<br>\- Leaving it at 500K rope wasn't that much worse, but 4M and 8M Theta made the grad_norm values worsen even if loss drops fast. |
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<br>\- Mixing in Pretraining Data was a PITA. Made it a lot worse with formatting. |
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<br>\- Pretraining / Noise made it worse at Haystack too? It wasn't all Green, Mainly Oranges. |
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<br>\- Improper / Bad Rope Theta shows in Grad_Norm exploding to thousands. It'll drop to low values alright, but it's a scary fast drop even with gradient clipping. |
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``` |
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sequence_len: 8192 |
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use_pose: true |
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pose_max_context_len: 32768 |
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overrides_of_model_config: |
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rope_theta: 2000000.0 |
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max_position_embeddings: 32768 |
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# peft_use_dora: true |
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adapter: lora |
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peft_use_rslora: true |
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lora_model_dir: |
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lora_r: 256 |
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lora_alpha: 256 |
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lora_dropout: 0.1 |
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lora_target_linear: true |
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lora_target_modules: |
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- gate_proj |
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- down_proj |
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- up_proj |
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- q_proj |
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- v_proj |
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- k_proj |
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- o_proj |
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warmup_steps: 80 |
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gradient_accumulation_steps: 6 |
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micro_batch_size: 1 |
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num_epochs: 2 |
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optimizer: adamw_bnb_8bit |
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lr_scheduler: cosine_with_min_lr |
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learning_rate: 0.00004 |
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lr_scheduler_kwargs: |
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min_lr: 0.000004 |
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``` |
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</details> |
