Update README.md

README.md

@@ -9,5 +9,114 @@ library_name: transformers
 tags:
 - text-generation-inference
 ---
-
 ![8.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/vebaBsL6MsLveGCH3y1ig.png)
+
+Blaze.1-27B-Reflection is a Gemma 2-based 27B parameter model. Gemma is a family of lightweight, state-of-the-art open models from Google, built using the same research and technology behind the Gemini models. These models are text-to-text, decoder-only large language models available in English, with open weights for both pre-trained and instruction-tuned variants. Gemma models are well-suited for a variety of text generation tasks, including question answering, summarization, and reasoning. Blaze.1-27B-Reflection is fine-tuned on self-reflection and behavioral data, using synthetic datasets for long-chain-of-thought reasoning from models such as DeepSeek and QwQ.
+
+# **Quickstart Chat Template**
+
+Below we share some code snippets on how to get quickly started with running the model. First, install the Transformers library with:
+```sh
+pip install -U transformers
+```
+
+Then, copy the snippet from the section that is relevant for your usecase.
+
+# **Running with the `pipeline` API**
+
+```python
+import torch
+from transformers import pipeline
+
+pipe = pipeline(
+    "text-generation",
+    model="prithivMLmods/Blaze.1-27B-Reflection",
+    model_kwargs={"torch_dtype": torch.bfloat16},
+    device="cuda",  # replace with "mps" to run on a Mac device
+)
+
+messages = [
+    {"role": "user", "content": "Who are you? Please, answer in pirate-speak."},
+]
+
+outputs = pipe(messages, max_new_tokens=256)
+assistant_response = outputs[0]["generated_text"][-1]["content"].strip()
+print(assistant_response)
+# Ahoy, matey! I be Gemma, a digital scallywag, a language-slingin' parrot of the digital seas. I be here to help ye with yer wordy woes, answer yer questions, and spin ye yarns of the digital world.  So, what be yer pleasure, eh? 🦜
+```
+
+# **Running the model on a single / multi GPU**
+
+```python
+# pip install accelerate
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/Blaze.1-27B-Reflection")
+model = AutoModelForCausalLM.from_pretrained(
+    "prithivMLmods/Blaze.1-27B-Reflection",
+    device_map="auto",
+    torch_dtype=torch.bfloat16,
+)
+
+input_text = "Write me a poem about Machine Learning."
+input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
+
+outputs = model.generate(**input_ids, max_new_tokens=32)
+print(tokenizer.decode(outputs[0]))
+```
+
+You can ensure the correct chat template is applied by using `tokenizer.apply_chat_template` as follows:
+```python
+messages = [
+    {"role": "user", "content": "Write me a poem about Machine Learning."},
+]
+input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt", return_dict=True).to("cuda")
+
+outputs = model.generate(**input_ids, max_new_tokens=256)
+print(tokenizer.decode(outputs[0]))
+```
+
+<a name="precisions"></a>
+
+# **Running the model on a GPU using different precisions**
+
+The native weights of this model were exported in `bfloat16` precision.
+
+You can also use `float32` if you skip the dtype, but no precision increase will occur (model weights will just be upcasted to `float32`). See examples below.
+
+* _Upcasting to `torch.float32`_
+
+```python
+# pip install accelerate
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/Blaze.1-27B-Reflection")
+model = AutoModelForCausalLM.from_pretrained(
+    "prithivMLmods/Blaze.1-27B-Reflection",
+    device_map="auto",
+)
+
+input_text = "Write me a poem about Machine Learning."
+input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
+
+outputs = model.generate(**input_ids, max_new_tokens=32)
+print(tokenizer.decode(outputs[0]))
+```
+
+# **Intended Use**  
+Blaze.1-27B-Reflection is designed for advanced reasoning tasks that require long-chain-of-thought processing, self-reflection, and behavioral analysis. Its primary applications include:  
+
+1. **Question Answering**: The model excels in providing detailed, step-by-step answers to complex queries.  
+2. **Summarization**: It can generate concise summaries of large text inputs, maintaining key information and logical flow.  
+3. **Reasoning and Decision Support**: With its fine-tuning on self-reflection data, it can assist in tasks that require thoughtful analysis, such as legal reasoning, policy development, and strategic planning.  
+4. **Conversational AI**: Due to its instruction-tuned nature, it performs well in interactive dialogue systems, offering coherent and context-aware responses.  
+5. **Creative Writing**: The model can be employed in generating high-quality content for creative tasks, including storytelling and content ideation.
+
+# **Limitations**  
+1. **Language and Domain Constraints**: While the model is effective in English, it may perform poorly with non-English inputs or domain-specific jargon outside its training scope.  
+2. **Context Retention Issues**: In very long conversations or documents, the model may lose track of earlier context, leading to incomplete or off-topic responses.  
+3. **Over-reliance on Synthetic Data**: Since Blaze.1-27B-Reflection is fine-tuned on synthetic datasets, it may exhibit biases or inconsistencies when faced with real-world, nuanced scenarios.  
+4. **Circular Reasoning**: The model may occasionally enter recursive reasoning loops, generating verbose responses without reaching a clear conclusion.  
+5. **Computational Demand**: As a 27B parameter model, it requires substantial computational resources for both inference and fine-tuning, which may limit its accessibility for users with limited hardware.  
+6. **Hallucinations**: Like most large language models, it may confidently generate incorrect information, especially when asked about facts or events outside its training data.