Update README and CONTRIBUTING

CONTRIBUTING.md

@@ -40,7 +40,7 @@ git commit -m "Commit message"
 git push origin <BRANCH_NAME>
 ```
 
-7. Create a pull request to the `dev` branch
+7. Create a pull request to the `main` branch
 
 ## Pull Request Guidelines
 

README.md

@@ -20,7 +20,7 @@
 
 ___
 
-`xTuring` makes it simple, fast, and cost‑efficient to fine‑tune open‑source LLMs (e.g., GPT‑OSS, LLaMA/LLaMA 2, Falcon, GPT‑J, GPT‑2, OPT, Bloom, Cerebras, Galactica) on your own data — locally or in your private cloud.
+`xTuring` makes it simple, fast, and cost‑efficient to fine‑tune open‑source LLMs (e.g., GPT‑OSS, LLaMA/LLaMA 2, Qwen3, MiniMax M2, GPT‑J, GPT‑2, DistilGPT‑2, Mamba) on your own data — locally or in your private cloud.
 
 Why xTuring:
 - Simple API for data prep, training, and inference
@@ -49,8 +49,8 @@ from xturing.models import BaseModel
 # Load a toy instruction dataset (Alpaca format)
 dataset = InstructionDataset("./examples/models/llama/alpaca_data")
 
-# Start small for quick iterations (works on CPU)
-model = BaseModel.create("distilgpt2_lora")
+# Start with the lightweight Qwen 0.6B LoRA checkpoint
+model = BaseModel.create("qwen3_0_6b_lora")
 
 # Fine‑tune and then generate
 model.finetune(dataset=dataset)
@@ -124,7 +124,7 @@ from xturing.models import GenericLoraKbitModel
 dataset = InstructionDataset('../llama/alpaca_data')
 
 # Load the desired model for INT4 bit fine-tuning
-model = GenericLoraKbitModel('tiiuae/falcon-7b')
+model = GenericLoraKbitModel('mistralai/Mistral-7B-Instruct-v0.2')
 
 # Run the fine-tuning
 model.finetune(dataset)
@@ -155,7 +155,7 @@ from xturing.models import GenericLoraKbitModel
 dataset = InstructionDataset('../llama/alpaca_data')
 
 # Load the desired model for INT4 bit fine-tuning
-model = GenericLoraKbitModel('tiiuae/falcon-7b')
+model = GenericLoraKbitModel('mistralai/Mistral-7B-Instruct-v0.2')
 
 # Generate outputs on desired prompts
 outputs = model.generate(dataset = dataset, batch_size=10)
@@ -199,14 +199,14 @@ Playground().launch() ## launches localhost UI
 
 ## 📚 Tutorials
 - [Preparing your dataset](examples/datasets/preparing_your_dataset.py)
-- [Cerebras-GPT fine-tuning with LoRA and INT8](examples/models/cerebras/cerebras_lora_int8.ipynb)   [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1eKq3oF7dnK8KuIfsTE70Gvvniwr1O9D0?usp=sharing)
-- [Cerebras-GPT fine-tuning with LoRA](examples/models/cerebras/cerebras_lora.ipynb)   [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1VjqQhstm5pT4EjPjx4Je7b3W2X1V3vDo?usp=sharing)
 - [LLaMA fine-tuning with LoRA and INT8](examples/models/llama/llama_lora_int8.py)   [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1SQUXq1AMZPSLD4mk3A3swUIc6Y2dclme?usp=sharing)
 - [LLaMA fine-tuning with LoRA](examples/models/llama/llama_lora.py)
 - [LLaMA fine-tuning](examples/models/llama/llama.py)
 - [GPT-J fine-tuning with LoRA and INT8](examples/models/gptj/gptj_lora_int8.py)   [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1hB_8s1V9K4IzifmlmN2AovGEJzTB1c7e?usp=sharing)
 - [GPT-J fine-tuning with LoRA](examples/models/gptj/gptj_lora.py)
 - [GPT-2 fine-tuning with LoRA](examples/models/gpt2/gpt2_lora.py)   [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://drive.google.com/file/d/1Sh-ocNpKn9pS7jv6oBb_Q8DitFyj1avL/view?usp=sharing)
+- [Qwen3 LoRA fine-tuning](examples/models/qwen3/qwen3_lora_finetune.py)
+- [MiniMax M2 fine-tuning](examples/models/minimax_m2/minimax_m2_finetune.py)
 
 <br>
 
@@ -258,18 +258,15 @@ Below is a list of all the supported models via `BaseModel` class of `xTuring` a
 
 |  Model |  Key |
 | -- | -- |
-|Bloom | bloom|
-|Cerebras | cerebras|
 |DistilGPT-2 | distilgpt2|
-|Falcon-7B | falcon|
-|Galactica | galactica|
 |GPT-OSS (20B/120B) | gpt_oss_20b, gpt_oss_120b|
 |GPT-J | gptj|
 |GPT-2 | gpt2|
 |LLaMA | llama|
 |LLaMA2 | llama2|
-|MiniMaxM2 | minimax_m2|
-|OPT-1.3B | opt|
+|MiniMax M2 | minimax_m2|
+|Qwen3 0.6B | qwen3_0_6b|
+|Mamba | mamba|
 
 The above are the base variants. Use these templates for `LoRA`, `INT8`, and `INT8 + LoRA` versions:
 
@@ -283,10 +280,10 @@ To load a model’s __INT4 + LoRA__ version, use the `GenericLoraKbitModel` clas
 ```python
 model = GenericLoraKbitModel('<model_path>')
 ```
-Replace `<model_path>` with a local directory or a Hugging Face model like `facebook/opt-1.3b`.
+Replace `<model_path>` with a local directory or a Hugging Face model like `mistralai/Mistral-7B-Instruct-v0.2`.
 
 ## 📈 Roadmap
-- [x] Support for `LLaMA`, `GPT-J`, `GPT-2`, `OPT`, `Cerebras-GPT`, `Galactica` and `Bloom` models
+- [x] Support for `LLaMA`, `LLaMA 2`, `GPT-J`, `GPT-2`, and `GPT-OSS` models
 - [x] Dataset generation using self-instruction
 - [x] Low-precision LoRA fine-tuning and unsupervised fine-tuning
 - [x] INT8 low-precision fine-tuning support
@@ -295,7 +292,7 @@ Replace `<model_path>` with a local directory or a Hugging Face model like `face
 - [x] INT4 LLaMA LoRA fine-tuning demo
 - [x] INT4 LLaMA LoRA fine-tuning with INT4 generation
 - [x] Support for a `Generic model` wrapper
-- [x] Support for `Falcon-7B` model
+- [x] Support for `MiniMax M2`, `Qwen3 0.6B`, and `Mamba` models
 - [x] INT4 low-precision fine-tuning support
 - [x] Evaluation of LLM models
 - [ ] INT3, INT2, INT1 low-precision fine-tuning support