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Model: tohur/natsumura-storytelling-rp-1.0-llama-3.1-8b
Source: Original Platform

README.md

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+---
+base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
+license: llama3.1
+datasets:
+- tohur/natsumura-rp-identity-sharegpt
+- tohur/ultrachat_uncensored_sharegpt
+- Nopm/Opus_WritingStruct
+- ResplendentAI/bluemoon
+- tohur/Internal-Knowledge-Map-sharegpt
+- felix-ha/tiny-stories
+- tdh87/Stories
+- tdh87/Just-stories
+- tdh87/Just-stories-2
+---
+# natsumura-storytelling-rp-1.0-llama-3.1-8b (fixed)
+  This is my Storytelling/RP model for my Natsumura series of 8b models. This model is finetuned on storytelling and roleplaying datasets so should be a great model 
+  to use for character chatbots in applications such as Sillytavern, Agnai, RisuAI and more. And should be a great model to use for fictional writing. Up to a 128k context.
+
+- **Developed by:** Tohur
+- **License:** llama3.1
+- **Finetuned from model :** meta-llama/Meta-Llama-3.1-8B-Instruct
+
+  This model is based on meta-llama/Meta-Llama-3.1-8B-Instruct, and is governed by [Llama 3.1 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE)
+  Natsumura is uncensored, which makes the model compliant.It will be highly compliant with any requests, even unethical ones. 
+  You are responsible for any content you create using this model. Please use it responsibly.
+
+# Quantized GGUF
+
+All GGUF models are available here: [natsumura-storytelling-rp-1.0-llama-3.1-8b-GGUF](https://huggingface.co/tohur/natsumura-storytelling-rp-1.0-llama-3.1-8b-GGUF)
+
+# use in ollama
+```
+ollama pull Tohur/natsumura-storytelling-rp-llama-3.1
+```
+
+# Datasets used:
+- tohur/natsumura-rp-identity-sharegpt
+- tohur/ultrachat_uncensored_sharegpt
+- Nopm/Opus_WritingStruct
+- ResplendentAI/bluemoon
+- tohur/Internal-Knowledge-Map-sharegpt
+- felix-ha/tiny-stories
+- tdh87/Stories
+- tdh87/Just-stories
+- tdh87/Just-stories-2
+
+The following parameters were used in [Llama Factory](https://github.com/hiyouga/LLaMA-Factory) during training:
+- per_device_train_batch_size=2
+- gradient_accumulation_steps=4
+- lr_scheduler_type="cosine"
+- logging_steps=10
+- warmup_ratio=0.1
+- save_steps=1000
+- learning_rate=2e-5
+- num_train_epochs=3.0
+- max_samples=500
+- max_grad_norm=1.0
+- quantization_bit=4
+- loraplus_lr_ratio=16.0
+- fp16=True
+  
+## Inference
+
+I use the following settings for inference:
+```
+"temperature": 1.0,
+"repetition_penalty": 1.05,
+"top_p": 0.95
+"top_k": 40
+"min_p": 0.05
+```
+
+# Prompt template: llama3
+
+```
+<|begin_of_text|><|start_header_id|>system<|end_header_id|>
+
+{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>
+
+{input}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
+
+{output}<|eot_id|>
+```