Update README.md

README.md

@@ -9,6 +9,10 @@ tags:
 - unsloth
 - mistral
 - trl
+- rp
+- gguf
+- experimental
+- long-context
 ---
 
 # Uploaded  model
@@ -17,6 +21,61 @@ tags:
 - **License:** apache-2.0
 - **Finetuned from model :** unsloth/Mistral-Nemo-Instruct-2407
 
+Standard disclaimer: This is me teaching myself the basics of fine-tuning, with notes extensively borrowed from https://huggingface.co/nothingiisreal/MN-12B-Celeste-V1.9
+
+New for v6:
+- Slightly different source mix. Down to 8,000 records of mostly-human convos and stories, curated by me, trained in ChatML.
+- The stories have been edited to remove author's notes, and the RP chats tweaked to remove many ministrations.
+- Different learning rate and back to Celeste's scaling factor setup (but Celeste trained on -base, this is -instruct).
+- Now with added eval! I worked out how to get eval stats (and wandb) set up, so now I can see my failures in graphical form.
+
+And of course yay Unsloth for letting this all train on a single A100 with variable (wildly variable) context length. 
+
+It was trained with the following settings:
+
+```
+
+model = FastLanguageModel.get_peft_model(
+    model,
+    r = 256,
+    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
+                      "gate_proj", "up_proj", "down_proj",],
+    lora_alpha = 128,  #   128 / sqrt(256) gives a scaling factor of 8
+    lora_dropout = 0.1, # Supports any, but = 0 is optimized
+    bias = "none",    # Supports any, but = "none" is optimized
+    # [NEW] "unsloth" uses 30% less VRAM, fits 2x larger batch sizes!
+    use_gradient_checkpointing = "unsloth", # True or "unsloth" for very long context
+    random_state = 3407,
+    use_rslora = True,  # setting the adapter scaling factor to lora_alpha/math.sqrt(r) instead of lora_alpha/r
+    loftq_config = None, # And LoftQ
+)
+
+lr_scheduler_kwargs = {
+    'min_lr': 0.0000024  # Adjust this value as needed
+}
+
+        per_device_train_batch_size = 2,
+        per_device_eval_batch_size = 2, # defaults to 8!
+        gradient_accumulation_steps = 4,
+        eval_accumulation_steps = 4,
+        prediction_loss_only = True, # When performing evaluation and generating predictions, only returns the loss.
+        warmup_steps = 50,
+        num_train_epochs = 2, # For longer training runs! 12 hrs/epoch?
+        learning_rate = 1e-5, # 8e-5 used by Celeste, 0.0001 is from the paper, halving it. tried 5e-5, now 1e-5.
+        fp16 = not is_bfloat16_supported(),
+        bf16 = is_bfloat16_supported(),
+        fp16_full_eval = True, # stops eval from trying to use fp32
+        eval_strategy = "steps", # 'no', 'steps', 'epoch'. Don't use this without an eval dataset etc
+        eval_steps = 100, # is eval_strat is set to 'steps', do every N steps.
+        logging_steps = 5, # so eval and logging happen on the same schedule
+        optim = "adamw_8bit", # 
+        weight_decay = 0, # up from 0
+        lr_scheduler_type = "cosine_with_min_lr", # linear, cosine, cosine_with_min_lr, default linear
+        lr_scheduler_kwargs = lr_scheduler_kwargs, # needed for cosine_with_min_lr
+        seed = 3407,
+
+```
+
 This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
 
 [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)