diff --git a/PARAMETERS.md b/PARAMETERS.md
deleted file mode 100644
index 94d6379897..0000000000
--- a/PARAMETERS.md
+++ /dev/null
@@ -1,87 +0,0 @@
-## LoraConfig Parameters
-
-Adjusting the `LoraConfig` parameters allows you to balance model performance and computational efficiency in Low-Rank Adaptation (LoRA). Here’s a concise breakdown of key parameters:
-
-**r**
-- **Description**: Rank of the low-rank decomposition for factorizing weight matrices.
-- **Impact**:
-  - **Higher**: Retains more information, increases computational load.
-  - **Lower**: Fewer parameters, more efficient training, potential performance drop if too small.
-
-
-**lora_alpha**
-- **Description**: Scaling factor for the low-rank matrices' contribution.
-- **Impact**:
-  - **Higher**: Increases influence, speeds up convergence, risks instability or overfitting.
-  - **Lower**: Subtler effect, may require more training steps.
-
-**lora_dropout**
-- **Description**: Probability of zeroing out elements in low-rank matrices for regularization.
-- **Impact**:
-  - **Higher**: More regularization, prevents overfitting, may slow training and degrade performance.
-  - **Lower**: Less regularization, may speed up training, risks overfitting.
-
-**loftq_config**
-- **Description**: Configuration for LoftQ, a quantization method for the backbone weights and initialization of LoRA layers.
-- **Impact**:
-  - **Not None**: If specified, LoftQ will quantize the backbone weights and initialize the LoRA layers. It requires setting `init_lora_weights='loftq'`.
-  - **None**: LoftQ quantization is not applied.
-  - **Note**: Do not pass an already quantized model when using LoftQ as LoftQ handles the quantization process itself.
-
-
-**use_rslora**
-- **Description**: Enables Rank-Stabilized LoRA (RSLora).
-- **Impact**:
-  - **True**: Uses Rank-Stabilized LoRA, setting the adapter scaling factor to `lora_alpha/math.sqrt(r)`, which has been proven to work better as per the [Rank-Stabilized LoRA paper](https://doi.org/10.48550/arXiv.2312.03732).
-  - **False**: Uses the original default scaling factor `lora_alpha/r`.
-
-**gradient_accumulation_steps**
-- **Default**: 1
-- **Description**: The number of steps to accumulate gradients before performing a backpropagation update.
-- **Impact**: 
-  - **Higher**: Accumulate gradients over multiple steps, effectively increasing the batch size without requiring additional memory. This can improve training stability and convergence, especially with large models and limited hardware.
-  - **Lower**: Faster updates but may require more memory per step and can be less stable.
-
-**weight_decay**
-- **Default**: 0.01
-- **Description**: Regularization technique that applies a small penalty to the weights during training.
-- **Impact**:
-  - **Non-zero Value (e.g., 0.01)**: Adds a penalty proportional to the magnitude of the weights to the loss function, helping to prevent overfitting by discouraging large weights.
-  - **Zero**: No weight decay is applied, which can lead to overfitting, especially in large models or with small datasets.
-
-**learning_rate**
-- **Default**: 2e-4
-- **Description**: The rate at which the model updates its parameters during training.
-- **Impact**:
-  - **Higher**: Faster convergence but risks overshooting optimal parameters and causing instability in training.
-  - **Lower**: More stable and precise updates but may slow down convergence, requiring more training steps to achieve good performance.
-
-## Target Modules 
-
-**q_proj (query projection)**
-- **Description**: Part of the attention mechanism in transformer models, responsible for projecting the input into the query space.
-- **Impact**: Transforms the input into query vectors that are used to compute attention scores.
-
-**k_proj (key projection)**
-- **Description**: Projects the input into the key space in the attention mechanism.
-- **Impact**: Produces key vectors that are compared with query vectors to determine attention weights.
-
-**v_proj (value projection)**
-- **Description**: Projects the input into the value space in the attention mechanism.
-- **Impact**: Produces value vectors that are weighted by the attention scores and combined to form the output.
-
-**o_proj (output projection)**
-- **Description**: Projects the output of the attention mechanism back into the original space.
-- **Impact**: Transforms the combined weighted value vectors back to the input dimension, integrating attention results into the model.
-
-**gate_proj (gate projection)**
-- **Description**: Typically used in gated mechanisms within neural networks, such as gating units in gated recurrent units (GRUs) or other gating mechanisms.
-- **Impact**: Controls the flow of information through the gate, allowing selective information passage based on learned weights.
-
-**up_proj (up projection)**
-- **Description**: Used for up-projection, typically increasing the dimensionality of the input.
-- **Impact**: Expands the input to a higher-dimensional space, often used in feedforward layers or when transitioning between different layers with differing dimensionalities.
-
-**down_proj (down projection)**
-- **Description**: Used for down-projection, typically reducing the dimensionality of the input.
-- **Impact**: Compresses the input to a lower-dimensional space, useful for reducing computational complexity and controlling the model size.
diff --git a/README.md b/README.md
index 2c50f45763..534079ed49 100644
--- a/README.md
+++ b/README.md
@@ -35,7 +35,7 @@ All notebooks are **beginner friendly**! Add your dataset, click "Run All", and
 - Run [Llama 3 conversational notebook](https://colab.research.google.com/drive/1XamvWYinY6FOSX9GLvnqSjjsNflxdhNc?usp=sharing) and [Mistral 7B v3 ChatML](https://colab.research.google.com/drive/15F1xyn8497_dUbxZP4zWmPZ3PJx1Oymv?usp=sharing)
 - This [text completion notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing) is for continued pretraining / raw text
 - This [continued pretraining notebook](https://colab.research.google.com/drive/1tEd1FrOXWMnCU9UIvdYhs61tkxdMuKZu?usp=sharing) is for learning another language
-
+- Click [here](https://github.com/unslothai/unsloth/wiki) for detailed documentation for Unsloth.
 
 ## 🦥 Unsloth.ai News
 - 📣 NEW! Continued Pretraining [notebook](https://colab.research.google.com/drive/1tEd1FrOXWMnCU9UIvdYhs61tkxdMuKZu?usp=sharing) for other languages like Korean!
@@ -76,7 +76,7 @@ model = FastLanguageModel.get_peft_model(
 
 
 ## 🥇 Performance Benchmarking
-- For the full list of **reproducable** benchmarking tables, [go to our website](https://unsloth.ai/blog/mistral-benchmark#Benchmark%20tables)
+- For the full list of **reproducible** benchmarking tables, [go to our website](https://unsloth.ai/blog/mistral-benchmark#Benchmark%20tables)
 
 | 1 A100 40GB  | 🤗Hugging Face | Flash Attention | 🦥Unsloth Open Source | 🦥[Unsloth Pro](https://unsloth.ai/pricing) |
 |--------------|--------------|-----------------|---------------------|-----------------|
@@ -100,14 +100,16 @@ model = FastLanguageModel.get_peft_model(
 ### Conda Installation
 Select either `pytorch-cuda=11.8` for CUDA 11.8 or `pytorch-cuda=12.1` for CUDA 12.1. If you have `mamba`, use `mamba` instead of `conda` for faster solving. See this [Github issue](https://github.com/unslothai/unsloth/issues/73) for help on debugging Conda installs.
 ```bash
-conda create --name unsloth_env python=3.10
+conda create --name unsloth_env \
+    python=3.10 \
+    pytorch-cuda=<11.8/12.1> \
+    pytorch cudatoolkit xformers -c pytorch -c nvidia -c xformers \
+    -y
 conda activate unsloth_env
 
-conda install pytorch-cuda=<12.1/11.8> pytorch cudatoolkit xformers -c pytorch -c nvidia -c xformers
-
 pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
 
-pip install --no-deps trl peft accelerate bitsandbytes
+pip install --no-deps "trl<0.9.0" peft accelerate bitsandbytes
 ```
 
 ### Pip Installation
@@ -162,7 +164,7 @@ pip install --no-deps packaging ninja einops flash-attn xformers trl peft accele
 
 # Pre Ampere RTX 2080, T4, GTX 1080 GPUs:
 pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
-pip install --no-deps xformers trl peft accelerate bitsandbytes
+pip install --no-deps xformers "trl<0.9.0" peft accelerate bitsandbytes
 ```
 7. For Pytorch 2.3.0: Use the `"ampere"` path for newer RTX 30xx GPUs or higher.
 ```bash
@@ -257,7 +259,7 @@ trainer.train()
 # (1) Saving to GGUF / merging to 16bit for vLLM
 # (2) Continued training from a saved LoRA adapter
 # (3) Adding an evaluation loop / OOMs
-# (4) Cutomized chat templates
+# (4) Customized chat templates
 ```
 
 <a name="DPO"></a>
diff --git a/unsloth/models/mistral.py b/unsloth/models/mistral.py
index fc2e1a9fb0..ff2e909fb9 100644
--- a/unsloth/models/mistral.py
+++ b/unsloth/models/mistral.py
@@ -512,7 +512,7 @@ def from_pretrained(
         if "n_total_devices >" not in inner_training_loop:
             raise RuntimeError(
                 "Our OSS was designed for people with few GPU resources to level the playing field.\n"
-                "The OSS Apache 2 license only supports four GPUs - please obtain a commercial license from our website.\n"
+                "The OSS Apache 2 license only supports one GPU - please obtain a commercial license.\n"
                 "We're a 2 person team, so we still have to fund our development costs - thanks!\n"
                 "If you don't, please consider at least sponsoring us through Ko-fi! Appreciate it!",
             )
@@ -521,6 +521,7 @@ def from_pretrained(
             "is_sagemaker_mp_enabled()",
             "False",
         )
+        exec(inner_training_loop, globals())
         Trainer._inner_training_loop = _fast_inner_training_loop
 
         # Save max_seq_length
@@ -560,6 +561,7 @@ def from_pretrained(
 
         # Add save modules
         patch_saving_functions(model)
+        Trainer._inner_training_loop = _fast_inner_training_loop
 
         # Save tokenizer for inference purposes
         tokenizer.padding_side = "left" # Force inference
diff --git a/unsloth/models/qwen2.py b/unsloth/models/qwen2.py
index 115bf3e090..47327280b9 100644
--- a/unsloth/models/qwen2.py
+++ b/unsloth/models/qwen2.py
@@ -12,9 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from .llama import *
-import os
-from ._utils import __version__
+from .mistral import *
 
 from transformers.models.qwen2.modeling_qwen2 import (
     Qwen2Attention,
@@ -34,7 +32,7 @@
 pass
 
 
-class FastQwen2Model(FastLlamaModel):
+class FastQwen2Model(FastMistralModel):
 
     @staticmethod
     def pre_patch():
@@ -72,7 +70,7 @@ def from_pretrained(
         trust_remote_code = False,
         **kwargs,
     ):
-        return FastLlamaModel.from_pretrained(
+        return FastMistralModel.from_pretrained(
             model_name     = model_name,
             max_seq_length = max_seq_length,
             dtype          = dtype,