Update README.md

README.md

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+<img src="https://huggingface.co/EpistemeAI/Fireball-Mistral-Nemo-Base-2407-v1-DPO2/resolve/main/fireball.JPG" width="200"/>
+
+
+# Fireball-Mistral-Nemo-Base-2407-V2
+This model is super fine-tune to provide better coding and better response(from first fine-tune) than Llama-3.1-8B and Google Gemma 2 9B. 
+Further fine tuned with ORPO method with dataset 
+- reciperesearch/dolphin-sft-v0.1-preference
+
+# Benchmark
+- TBD
+
+## Training Dataset 
+Supervised fine-tuning with dataset: 
+- candenizkocak/code-alpaca-297k
+- yahma/alpaca-cleaned
+
+
+
 # Uploaded  model
 
 - **Developed by:** EpistemeAI
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 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)
+
+
+
+
+
+
+
+# Model Card for Mistral-Nemo-Base-2407
+
+The Mistral-Nemo-Base-2407 Large Language Model (LLM) is a pretrained generative text model of 12B parameters trained jointly by Mistral AI and NVIDIA, it significantly outperforms existing models smaller or similar in size.
+
+For more details about this model please refer to our release [blog post](https://mistral.ai/news/mistral-nemo/).
+
+## Key features
+- Released under the **Apache 2 License**
+- Pre-trained and instructed versions
+- Trained with a **128k context window**
+- Trained on a large proportion of **multilingual and code data**
+- Drop-in replacement of Mistral 7B
+
+## Model Architecture
+Mistral Nemo is a transformer model, with the following architecture choices:
+- **Layers:** 40
+- **Dim:** 5,120
+- **Head dim:** 128
+- **Hidden dim:** 14,436
+- **Activation Function:** SwiGLU
+- **Number of heads:** 32
+- **Number of kv-heads:** 8 (GQA)
+- **Vocabulary size:** 2**17 ~= 128k
+- **Rotary embeddings (theta = 1M)**
+
+#### Demo
+
+After installing `mistral_inference`, a `mistral-demo` CLI command should be available in your environment.
+
+```
+mistral-demo $HOME/mistral_models/Nemo-v0.1
+```
+
+### Transformers
+
+> [!IMPORTANT]
+> NOTE: Until a new release has been made, you need to install transformers from source:
+> ```sh
+> pip install git+https://github.com/huggingface/transformers.git
+> ```
+
+If you want to use Hugging Face `transformers` to generate text, you can do something like this.
+
+```py
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model_id = "EpistemeAI/Fireball-Mistral-Nemo-Base-2407-sft-v2.1"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+inputs = tokenizer("Hello my name is", return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=20)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+> [!TIP]
+> Unlike previous Mistral models, Mistral Nemo requires smaller temperatures. We recommend to use a temperature of 0.3.
+
+## Note
+
+`Mistral-Nemo-Base-2407` is a pretrained base model and therefore does not have any moderation mechanisms.
+
+
+### Citation for yahma/alpaca-cleaned dataset
+```
+@misc{alpaca,
+  author = {Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto },
+  title = {Stanford Alpaca: An Instruction-following LLaMA model},
+  year = {2023},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}},
+}
+```