README.md

---
license: gemma
language:
- en
base_model:
- google/gemma-2-27b-it
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
- Gemma
---
![8.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/vebaBsL6MsLveGCH3y1ig.png)

Blaze.1-27B-Preview is a Gemma 2-based, 27-billion-parameter model. Gemma is a family of lightweight, state-of-the-art open models from Google, built using the same research and technology that powers 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 was fine-tuned on long-chain-of-thought reasoning synthetic datasets derived from models such as DeepSeek, Qwen, and OpenAI’s GPT-4.

# **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-Preview",
    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-Preview")
model = AutoModelForCausalLM.from_pretrained(
    "prithivMLmods/Blaze.1-27B-Preview",
    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-Preview")
model = AutoModelForCausalLM.from_pretrained(
    "prithivMLmods/Blaze.1-27B-Preview",
    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-Preview is designed for advanced text generation tasks requiring logical reasoning, complex problem-solving, and long-form content generation. Its primary use cases include:  

1. **Question Answering**: Generating detailed, accurate answers to a wide range of questions across various domains.  
2. **Summarization**: Condensing long texts into concise summaries while preserving key information and context.  
3. **Reasoning Tasks**: Performing multi-step reasoning, particularly in mathematical, logical, and conditional scenarios.  
4. **Instruction Following**: Responding to user prompts with coherent and relevant outputs, based on fine-tuned instruction-following capabilities.  
5. **Conversational AI**: Supporting virtual assistants and chatbots for both casual and professional applications.  
6. **Multi-Model Comparison**: Benefiting researchers by providing outputs tuned with diverse datasets such as DeepSeek, Qwen, and GPT-4, allowing comparative insights across different reasoning paradigms.


# **Limitations**  
1. **Reasoning Bias**: Despite its training on synthetic datasets, the model may exhibit biases in reasoning, especially when encountering unfamiliar problem types.  
2. **Hallucinations**: Like other large language models, Blaze.1-27B may generate inaccurate or fabricated information, particularly when dealing with facts or events not covered during training.  
3. **Dependency on Prompt Quality**: The quality of the model’s output heavily relies on the clarity and specificity of the input prompt. Poorly framed prompts may lead to irrelevant or incomplete responses.  
4. **Long Context Handling**: While it is designed for long-chain reasoning, performance may degrade with excessively long inputs or contexts, resulting in loss of coherence or incomplete reasoning.  
5. **Resource Requirements**: Due to its large size (27 billion parameters), it requires substantial computational resources for both inference and fine-tuning, limiting its accessibility for users without high-performance hardware.  
6. **Language Support**: Although it excels in English, its capabilities in other languages may be limited, and unexpected issues may arise when processing multilingual or code-mixed inputs.