Update README.md

README.md

@@ -7,77 +7,17 @@ base_model: teknium/OpenHermes-2.5-Mistral-7B
 model-index:
 - name: openhermes-mistral-cat_out
   results: []
+datasets:
+- xaviviro/oasst2_ca_gpt
+language:
+- ca
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
 
-[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
-# openhermes-mistral-cat_out
-
-This model is a fine-tuned version of [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) on the None dataset.
-It achieves the following results on the evaluation set:
-- Loss: 1.6083
-
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
-
-### Training hyperparameters
+# OpenHermes-2.5-Mistral-7B-Catala-LoRA 
 
-The following hyperparameters were used during training:
-- learning_rate: 0.0002
-- train_batch_size: 2
-- eval_batch_size: 2
-- seed: 42
-- gradient_accumulation_steps: 4
-- total_train_batch_size: 8
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: cosine
-- lr_scheduler_warmup_steps: 14
-- num_epochs: 2
-- mixed_precision_training: Native AMP
+This model is a fine-tuned version of [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) on the [xaviviro/oasst2_ca_gpt](https://huggingface.co/datasets/xaviviro/oasst2_ca_gpt) dataset.
 
-### Training results
-
-| Training Loss | Epoch | Step | Validation Loss |
-|:-------------:|:-----:|:----:|:---------------:|
-| 1.6981        | 1.01  | 61   | 1.6273          |
-| 1.6355        | 1.96  | 120  | 1.6083          |
-
-
-### Framework versions
-
-- Transformers 4.36.2
-- Pytorch 2.1.2+cu121
-- Datasets 2.15.0
-- Tokenizers 0.15.0
-## Training procedure
-
-
-The following `bitsandbytes` quantization config was used during training:
-- quant_method: bitsandbytes
-- load_in_8bit: False
-- load_in_4bit: True
-- llm_int8_threshold: 6.0
-- llm_int8_skip_modules: None
-- llm_int8_enable_fp32_cpu_offload: False
-- llm_int8_has_fp16_weight: False
-- bnb_4bit_quant_type: nf4
-- bnb_4bit_use_double_quant: True
-- bnb_4bit_compute_dtype: float16
-
-### Framework versions
-
-
-- PEFT 0.6.0
+[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)