Revert changes - uploaded to wrong repo

README.md

@@ -15,9 +15,11 @@ base_model: alpindale/Mistral-7B-v0.2-hf
 ---
 # Model Card: Hercules-4.0-Mistral-v0.2-7B
 
+![image/png](https://tse3.mm.bing.net/th/id/OIG1.vnrl3xpEcypR3McLW63q?pid=ImgGn)
 
 ## Model Description
-Hercules-4.0-Yi-34B is a fine-tuned language model derived from Yi-34B language model. It is specifically designed to excel in instruction following, function calls, and conversational interactions across various scientific and technical domains. The dataset used for fine-tuning, also named Hercules-v4.0, expands upon the diverse capabilities of OpenHermes-2.5 with contributions from numerous curated datasets. This fine-tuning has hercules-v4.0 with enhanced abilities in:
+
+Hercules-4.0-Mistral-v0.2-7B is a fine-tuned language model derived from Mistralai/Mistral-7B-v0.2. It is specifically designed to excel in instruction following, function calls, and conversational interactions across various scientific and technical domains. The dataset used for fine-tuning, also named Hercules-v4.0, expands upon the diverse capabilities of OpenHermes-2.5 with contributions from numerous curated datasets. This fine-tuning has hercules-v4.0 with enhanced abilities in:
 
 - Complex Instruction Following: Understanding and accurately executing multi-step instructions, even those involving specialized terminology.
 - Function Calling: Seamlessly interpreting and executing function calls, providing appropriate input and output values.
@@ -33,7 +35,7 @@ AWQ: https://huggingface.co/solidrust/Hercules-4.0-Mistral-v0.2-7B-AWQ
 
 ## Intended Uses & Potential Bias
 
-Hercules-4.0-Yi-34B is well-suited to the following applications:
+Hercules-4.0-Mistral-v.02-7B is well-suited to the following applications:
 
 - Specialized Chatbots: Creating knowledgeable chatbots and conversational agents in scientific and technical fields.
 - Instructional Assistants: Supporting users with educational and step-by-step guidance in various disciplines.
@@ -44,21 +46,52 @@ Hercules-4.0-Yi-34B is well-suited to the following applications:
 ## Limitations and Risks
 
 - Toxicity: The dataset contains toxic or harmful examples.
-- Hallucinations and Factual Errors: Like other language models, Hercules-4.0-Yi-34B may generate incorrect or misleading information, especially in specialized domains where it lacks sufficient expertise.
+- Hallucinations and Factual Errors: Like other language models, Hercules-4.0-Mistral-v0.2-7B may generate incorrect or misleading information, especially in specialized domains where it lacks sufficient expertise.
 - Potential for Misuse: The ability to engage in technical conversations and execute function calls could be misused for malicious purposes.
 
 ## Training Procedure
 
 - This model was trained on 8 kaggle TPUs, using torch xla SPMD for high MXU efficiency. There was no expense on my end (meaning you can reproduce this too!)
-- a learning rate of 1e-4 with an end factor of 0.001 was used. 
+- A learning rate of 5e-06 with the Adam optimizer. A linear scheduler was used, with an end factor of 0.1. A low learning rate was used to prevent exploding gradients.
 - No mixed precision was used, with the default dtype being bfloat16.
 - A total batch size of 64 was used.
-- Trained on 75,000 examples of Hercules-v4.0
-- I used LoRA to freeze about 97% of the model parameters. Quantization was not used. 
-- This model was trained on OpenAI's ChatML prompt format. Because this model has function calling capabilities, the prompt format is slightly different, here's what it would look like: ```<|im_start|>system\n{message}<|im_end|>\n<|im_start|>user\n{user message}<|im_end|>\n<|im_start|>call\n{function call message}<|im_end|>\n<|im_start|>function\n{function response message}<|im_end|>\n<|im_start|>assistant\n{assistant message}<|endoftext|>```
+- Trained on 700,000 examples of Hercules-v4.0
+- No model parameters were frozen and no quantization was used.
+- This model was trained on OpenAI's ChatML prompt format. Because this model has function calling capabilities, the prompt format is slightly different, here's what it would look like: ```<|im_start|>system\n{message}<|im_end|>\n<|im_start|>user\n{user message}<|im_end|>\n<|im_start|>call\n{function call message}<|im_end|>\n<|im_start|>function\n{function response message}<|im_end|>\n<|im_start|>assistant\n{assistant message}</s>```
 
 This model was fine-tuned using my TPU-Alignment repository. https://github.com/Locutusque/TPU-Alignment
 
 ## Evaluation
 
-Coming Soon
+|              Tasks              |Version|Filter|n-shot| Metric |Value |   |Stderr|
+|---------------------------------|-------|------|-----:|--------|-----:|---|-----:|
+|agieval_nous                     |N/A    |none  |     0|acc_norm|0.3578|±  |0.0093|
+|                                 |       |none  |     0|acc     |0.3598|±  |0.0093|
+| - agieval_aqua_rat              |      1|none  |     0|acc     |0.2362|±  |0.0267|
+|                                 |       |none  |     0|acc_norm|0.2362|±  |0.0267|
+| - agieval_logiqa_en             |      1|none  |     0|acc     |0.3026|±  |0.0180|
+|                                 |       |none  |     0|acc_norm|0.3456|±  |0.0187|
+| - agieval_lsat_ar               |      1|none  |     0|acc     |0.2565|±  |0.0289|
+|                                 |       |none  |     0|acc_norm|0.2522|±  |0.0287|
+| - agieval_lsat_lr               |      1|none  |     0|acc     |0.3588|±  |0.0213|
+|                                 |       |none  |     0|acc_norm|0.3490|±  |0.0211|
+| - agieval_lsat_rc               |      1|none  |     0|acc     |0.4981|±  |0.0305|
+|                                 |       |none  |     0|acc_norm|0.4461|±  |0.0304|
+| - agieval_sat_en                |      1|none  |     0|acc     |0.6553|±  |0.0332|
+|                                 |       |none  |     0|acc_norm|0.6408|±  |0.0335|
+| - agieval_sat_en_without_passage|      1|none  |     0|acc     |0.3883|±  |0.0340|
+|                                 |       |none  |     0|acc_norm|0.3738|±  |0.0338|
+| - agieval_sat_math              |      1|none  |     0|acc     |0.3091|±  |0.0312|
+|                                 |       |none  |     0|acc_norm|0.2773|±  |0.0302|
+
+|   Groups   |Version|Filter|n-shot| Metric |Value |   |Stderr|
+|------------|-------|------|-----:|--------|-----:|---|-----:|
+|agieval_nous|N/A    |none  |     0|acc_norm|0.3578|±  |0.0093|
+|            |       |none  |     0|acc     |0.3598|±  |0.0093|
+|mmlu_flan_cot_fewshot                   |N/A    |get-answer|     0|exact_match|0.5787|±  |0.0120|
+| - mmlu_flan_cot_fewshot_humanities     |N/A    |get-answer|     0|exact_match|0.5174|±  |0.0209|
+| - mmlu_flan_cot_fewshot_other          |N/A    |get-answer|     0|exact_match|0.6833|±  |0.0249|
+| - mmlu_flan_cot_fewshot_social_sciences|N/A    |get-answer|     0|exact_match|0.6528|±  |0.0248|
+| - mmlu_flan_cot_fewshot_stem           |N/A    |get-answer|     0|exact_match|0.4925|±  |0.0266|
+|ai2_arc                                 |N/A    |none      |     0|acc        |0.6936|±  |0.0073|
+|                                        |       |none      |     0|acc_norm   |0.6984|±  |0.0074|