Text Generation
Transformers
Safetensors
gemma2
llm
ai
artificial intelligence
large language model
math
coding
science
daily tasks
conversational
text-generation-inference
Instructions to use frameai/LoxaPro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use frameai/LoxaPro with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="frameai/LoxaPro") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("frameai/LoxaPro") model = AutoModelForCausalLM.from_pretrained("frameai/LoxaPro", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use frameai/LoxaPro with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "frameai/LoxaPro" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "frameai/LoxaPro", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/frameai/LoxaPro
- SGLang
How to use frameai/LoxaPro with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "frameai/LoxaPro" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "frameai/LoxaPro", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "frameai/LoxaPro" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "frameai/LoxaPro", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use frameai/LoxaPro with Docker Model Runner:
docker model run hf.co/frameai/LoxaPro
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tags:
- text-generation
- llm
- ai
- artificial intelligence
- large language model
- math
- coding
- science
- daily tasks
license: apache-2.0
library_name: transformers
---
# Loxa Pro
## Model Description
Loxa Pro is a high-quality Large Language Model (LLM) designed to excel in a wide range of tasks, including:
* **Mathematics:** Solving mathematical problems, performing calculations, and providing mathematical explanations.
* **Coding:** Generating code snippets, debugging code, and answering programming-related questions.
* **General Questions:** Answering a wide variety of general knowledge questions accurately and comprehensively.
* **Science Questions:** Providing information and explanations on various scientific topics.
* **Daily Tasks:** Assisting with everyday tasks, such as writing emails, setting reminders, generating to-do lists, and more.
Loxa Pro employs the **CA (Combine Architectures)** method, which enables it to effectively address diverse queries and tasks. This model surpasses its predecessors, Loxa-4B and Loxa-3B, in terms of accuracy and performance.
## Key Features
* **High Accuracy:** Loxa Pro demonstrates superior accuracy compared to Loxa-4B and Loxa-3B, providing more reliable and precise responses.
* **Broad Capabilities:** Handles a diverse range of tasks, from complex mathematical problems and coding challenges to general knowledge and everyday tasks.
* **Optimized for Efficiency:** The model is well-optimized to run efficiently even on smaller GPUs, making it accessible for users with limited computational resources.
* **CA (Combine Architectures) Method:** Leverages the CA method to effectively combine different architectural strengths, enhancing overall performance.
## Intended Use
Loxa Pro is intended for a wide range of applications, including:
* **Research:** As a tool for research in natural language processing, artificial intelligence, and related fields.
* **Education:** As an educational aid for students and educators in various subjects.
* **Development:** As a component in building intelligent applications, chatbots, and virtual assistants.
* **Personal Use:** As a versatile tool to assist with daily tasks, answer questions, and provide information.
## Limitations
* **Potential Biases:** Like all LLMs, Loxa Pro may reflect biases present in its training data.
* **Factual Accuracy:** While highly accurate, the model may occasionally generate incorrect or misleading information. It is always recommended to verify information from multiple sources.
* **Resource Requirements:** Although optimized, the model still requires a certain level of computational resources to run effectively.
## How to Use
**Example with `transformers.pipeline`:**
```python
from transformers import pipeline
messages = [
{"role": "user", "content": "Write softmax formula in math style for me"},
]
pipe = pipeline("text-generation", model="explorewithai/LoxaPro", device_map = "cuda")
pipe(messages)
``` |