Update README.md

README.md

@@ -25,104 +25,19 @@ Back from the dead! Hoping to make something cool to share with everyone! Introd
 [exl2](https://huggingface.co/lucyknada/Epiculous_Crimson_Dawn-V0.1-exl2) / [gguf](https://huggingface.co/mradermacher/Crimson_Dawn-V0.1-GGUF)
 
 ## Prompting
-Crimson Dawn was trained with the Mistral Instruct template, therefore it should be prompted in the same way that you would prompt any other mistral model.
+Crimson Dawn was trained with the Mistral Instruct template, therefore it should be prompted in a similar way that you would prompt any other mistral based model.
 
 ```
 "<s>[INST] Prompt goes here [/INST]<\s>"
 ```
+### Context and Instruct
+[Mistral-Custom-Context.json](https://files.catbox.moe/l9w0ry.json)
+[Mistral-Custom-Instruct.json](https://files.catbox.moe/9xiiwb.json)
+
 
 ### Current Top Sampler Settings
-```json
-{
-    "temp": 1.25,
-    "temperature_last": true,
-    "top_p": 1,
-    "top_k": -1,
-    "top_a": 0,
-    "tfs": 1,
-    "epsilon_cutoff": 0,
-    "eta_cutoff": 0,
-    "typical_p": 1,
-    "min_p": 0.3,
-    "rep_pen": 1,
-    "rep_pen_range": 0,
-    "rep_pen_decay": 0,
-    "rep_pen_slope": 1,
-    "no_repeat_ngram_size": 0,
-    "penalty_alpha": 0,
-    "num_beams": 1,
-    "length_penalty": 1,
-    "min_length": 0,
-    "encoder_rep_pen": 1,
-    "freq_pen": 0,
-    "presence_pen": 0,
-    "skew": 0,
-    "do_sample": true,
-    "early_stopping": false,
-    "dynatemp": false,
-    "min_temp": 0,
-    "max_temp": 2,
-    "dynatemp_exponent": 1,
-    "smoothing_factor": 0,
-    "smoothing_curve": 1,
-    "dry_allowed_length": 2,
-    "dry_multiplier": 0,
-    "dry_base": 1.75,
-    "dry_sequence_breakers": "[\"\\n\", \":\", \"\\\"\", \"*\"]",
-    "dry_penalty_last_n": 0,
-    "add_bos_token": true,
-    "ban_eos_token": false,
-    "skip_special_tokens": true,
-    "mirostat_mode": 0,
-    "mirostat_tau": 5,
-    "mirostat_eta": 0.1,
-    "guidance_scale": 1,
-    "negative_prompt": "",
-    "grammar_string": "",
-    "json_schema": {},
-    "banned_tokens": "",
-    "sampler_priority": [
-        "temperature",
-        "dynamic_temperature",
-        "quadratic_sampling",
-        "top_k",
-        "top_p",
-        "typical_p",
-        "epsilon_cutoff",
-        "eta_cutoff",
-        "tfs",
-        "top_a",
-        "min_p",
-        "mirostat"
-    ],
-    "samplers": [
-        "top_k",
-        "tfs_z",
-        "typical_p",
-        "top_p",
-        "min_p",
-        "temperature"
-    ],
-    "ignore_eos_token": false,
-    "spaces_between_special_tokens": true,
-    "speculative_ngram": false,
-    "sampler_order": [
-        5,
-        6,
-        0,
-        1,
-        2,
-        3,
-        4
-    ],
-    "logit_bias": [],
-    "ignore_eos_token_aphrodite": false,
-    "spaces_between_special_tokens_aphrodite": true,
-    "rep_pen_size": 0,
-    "genamt": 1024,
-    "max_length": 16384
-}
-```
+[Crimson_Dawn-Magnum-Style](https://files.catbox.moe/lc59dn.json)
+[Crimson_Dawn-Nitral-Special](https://files.catbox.moe/8xjxht.json)
 
 ## Training
 Training was done twice over 2 epochs each on two 2x [NVIDIA A6000 GPUs](https://www.nvidia.com/en-us/design-visualization/rtx-a6000/) using LoRA. A two-phased approach was used in which the base model was trained 2 epochs on RP data, the LoRA was then applied to base. Finally, the new modified base was trained 2 epochs on instruct, and the new instruct LoRA was applied to the modified base, resulting in what you see here.