Update README.md

README.md

@@ -1,21 +1,53 @@
+
+
+
 ---
-library_name: transformers
-license: other
-base_model: IntervitensInc/Llama-3.1-Minitron-4B-Width-Base-chatml
-tags:
-- generated_from_trainer
-model-index:
-- name: outputs/out
-  results: []
+License: apache-2.0
+Language:
+  - En
+Pipeline_tag: text-generation
+Base_model: nvidia/Llama-3.1-Minitron-4 B-Width-Base
+Tags:
+  - Chat
 ---
 
-<!-- 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. -->
+![image/png]()
+A model made to continue off my previous work on anthracite-org/magnum-4 b, A small model made for creative writing / General assistant tasks, finetuned ontop of [Intervitens](link), this model is made to be more coherent and generally be better then the 4 B at both writing and assistant tasks. 
+
+## Prompting
+Model has been Instruct tuned with the ChatML formatting. A typical input would look like this:
+
+```py
+"""<|im_start|>system
+system prompt<|im_end|>
+<|im_start|>user
+Hi there!<|im_end|>
+<|im_start|>assistant
+Nice to meet you!<|im_end|>
+<|im_start|>user
+Can I ask a question?<|im_end|>
+<|im_start|>assistant
+"""
+```
+
+## Support
+
+To run inference on this model, you'll need to use Aphrodite, vLLM or EXL 2/tabbyAPI, as llama. Cpp hasn't yet merged the required pull request to fix the llama 3.1 rope_freqs issue with custom head dimensions. 
+
+However, you can work around this by quantizing the model yourself to create a functional GGUF file. Note that until [this PR](https://github.com/ggerganov/llama.cpp/pull/9141) is merged, the context will be limited to 8 k tokens.
+
+To create a working GGUF file, make the following adjustments:
+
+1. Remove the `"rope_scaling": {}` entry from `config.json`
+2. Change `"max_position_embeddings"` to `8192` in `config.json`
+
+These modifications should allow you to use the model with llama. Cpp, albeit with the mentioned context limitation.
+
+## Axolotl config
 
-[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
 <details><summary>See axolotl config</summary>
 
-axolotl version: `0.4.1`
+Axolotl version: `0.4.1`
 ```yaml
 base_model: IntervitensInc/Llama-3.1-Minitron-4B-Width-Base-chatml
 model_type: AutoModelForCausalLM
@@ -63,10 +95,10 @@ liger_rms_norm: true
 liger_swiglu: true
 liger_fused_linear_cross_entropy: true
 
-wandb_project: Ohashi4b
+wandb_project: 
 wandb_entity:
 wandb_watch:
-wandb_name: Ohashi4b
+wandb_name: 
 wandb_log_model:
 
 gradient_accumulation_steps: 32
@@ -107,66 +139,23 @@ fsdp_config:
 special_tokens:
   pad_token: <|finetune_right_pad_id|>
 
-
 ```
 
 </details><br>
 
-# outputs/out
-
-This model is a fine-tuned version of [IntervitensInc/Llama-3.1-Minitron-4B-Width-Base-chatml](https://huggingface.co/IntervitensInc/Llama-3.1-Minitron-4B-Width-Base-chatml) on the None dataset.
-It achieves the following results on the evaluation set:
-- Loss: 1.0118
-
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- learning_rate: 2e-05
-- train_batch_size: 1
-- eval_batch_size: 1
-- seed: 42
-- distributed_type: multi-GPU
-- num_devices: 2
-- gradient_accumulation_steps: 32
-- total_train_batch_size: 64
-- total_eval_batch_size: 2
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: cosine
-- lr_scheduler_warmup_steps: 6
-- num_epochs: 2
+## Credits
 
-### Training results
+- [anthracite-org/kalo-opus-instruct-22k-no-refusal](https://huggingface.co/datasets/anthracite-org/kalo-opus-instruct-22k-no-refusal)
+- [NewEden/Gryphe-3.5-16k-Subset](https://huggingface.co/datasets/NewEden/Gryphe-3.5-16k-Subset) 
+- [Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned](https://huggingface.co/datasets/Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned)
+- [lodrick-the-lafted/OpusStories](https://huggingface.co/datasets/lodrick-the-lafted/OpusStories)
 
-| Training Loss | Epoch  | Step | Validation Loss |
-|:-------------:|:------:|:----:|:---------------:|
-| 1.4992        | 0.0278 | 1    | 1.6105          |
-| 1.2866        | 0.25   | 9    | 1.2680          |
-| 1.1737        | 0.5    | 18   | 1.1396          |
-| 1.1355        | 0.75   | 27   | 1.0766          |
-| 1.1065        | 1.0    | 36   | 1.0408          |
-| 0.9673        | 1.2370 | 45   | 1.0272          |
-| 0.9526        | 1.4870 | 54   | 1.0167          |
-| 0.9653        | 1.7370 | 63   | 1.0126          |
-| 0.958         | 1.9870 | 72   | 1.0118          |
+I couldn't have made this model without the help of [Kubernetes_bad](https://huggingface.co/kubernetes-bad) and the support of [Lucy Knada](https://huggingface.co/lucyknada) 
 
+## Training
+The training was done for 2 epochs. We used  2 x [RTX 6000s](https://store.nvidia.com/en-us/nvidia-rtx/products/nvidia-rtx-6000-ada-generation/) GPUs graciously provided by [Kubernetes_Bad](https://huggingface.co/kubernetes-bad) for the full-parameter fine-tuning of the model. 
 
-### Framework versions
+[<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)
 
-- Transformers 4.45.0.dev0
-- Pytorch 2.4.0+cu121
-- Datasets 2.19.1
-- Tokenizers 0.19.1
+## Safety
+...