Update README.md

README.md

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 pipeline_tag: question-answering
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+<img src="https://huggingface.co/EpistemeAI/Fireball-Mistral-Nemo-Base-2407-v1-DPO2/resolve/main/fireball.JPG" width="200"/>
+
+
+# Fireball-12B-v1.3a Philosophers 
+This model is super fine-tune with philosophy in science, math, epistemology, to provide high quality responses(from first fine-tune) than Llama-3.1-8B and Google Gemma 2 9B.
+Super fine tuned with various datasets.
+
+# Benchmark
+
+Example from Fireball-12B
+<img src="https://huggingface.co/EpistemeAI/Fireball-12B/resolve/main/benchmark2.jpg"/>
+
+V1.3a benchmark will show later this quarter.
+
+## Training Dataset 
+Fine tuned with various datasets.
+
+
+# Model Card for Fireball-12B-v1.3a Philosophers
+
+The Heavy fine-tuned 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)**
+
+# Guardrail/Moderation guide: 
+For guardrailing and moderating prompts against indirect/direct prompt injections and jailbreaking, please follow the SentinelShield AI GitHub repository:
+[SentinelShield AI](https://github.com/tomtyiu/SentinelShieldAI)
+
+
+#### Demo
+
+After installing `mistral_inference`, a `mistral-demo` CLI command should be available in your environment.
+
+### Prompt instructions - Alpaca style prompt(recommended):
+
+```py
+f"""Below is an instruction that describes a task. \
+    Write a response that appropriately completes the request.
+    ### Instruction:
+    {x['instruction']}
+    ### Input:
+    {x['input']}
+    ### Response:
+    """
+```
+
+### Transformers
+
+> [!IMPORTANT]
+> NOTE: Until a new release has been made, you need to install transformers from source:
+> ```sh
+> pip install mistral_inference
+> pip install mistral-demo
+> 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 = "EpistemeAI2/Fireball-12B-v1.13a-philosophers"
+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))
+```
+## Accelerator mode: 
+```py
+pip install accelerate #GPU A100/L4
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from accelerate import Accelerator
+# Initialize the accelerator
+accelerator = Accelerator()
+# Define the model ID
+model_id = "EpistemeAI2/Fireball-12B-v1.13a-philosophers"
+# Load the tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+# Load the model and prepare it for distributed setup using accelerate
+model = AutoModelForCausalLM.from_pretrained(model_id)
+# Move the model to the appropriate device using accelerate
+model, = accelerator.prepare(model)
+# Prepare inputs
+inputs = tokenizer("Hello my name is", return_tensors="pt").to(accelerator.device)
+# Generate outputs with the model
+outputs = model.generate(**inputs, max_new_tokens=20)
+# Decode and print the outputs
+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
+`EpistemeAI/Fireball-12B-v1.2` is a pretrained base model and therefore does not have any moderation mechanisms. Go to Guardrail/Moderation guide section for moderation guide
+
+
+
 # Uploaded  model
 
 - **Developed by:** EpistemeAI2