llm.create_chat_completion(
messages = "No input example has been defined for this model task."
)Uploaded model
- Developed by: iFaz
- License: apache-2.0
- Finetuned from model : unsloth/Llama-3.2-3B-Instruct-bnb-4bit
Model Card: unsloth/Llama-3.2-3B-Instruct-bnb-4bit
Overview
This is a fine-tuned version of the unsloth/Llama-3.2-3B-Instruct-bnb-4bit model, optimized for instruction-following tasks. The model leverages the efficiency of 4-bit quantization, making it lightweight and resource-efficient while maintaining high-quality outputs. It is particularly suited for text generation tasks in English, with applications ranging from conversational AI to natural language understanding tasks.
Key Features
- Base Model:
unsloth/Llama-3.2-3B - Quantization: Utilizes 4-bit precision, enabling deployment on resource-constrained systems while maintaining performance.
- Language: English-focused, with robust generalization capabilities across diverse text-generation tasks.
- Fine-Tuning: Enhanced for instruction-following tasks to generate coherent and contextually relevant responses.
- Versatile Applications: Ideal for text generation, summarization, dialogue systems, and other natural language processing (NLP) tasks.
Model Details
- Developer: iFaz
- License: Apache 2.0 (permitting commercial and research use)
- Tags:
- Text generation inference
- Transformers
- Unsloth
- LLaMA
- TRL (Transformers Reinforcement Learning)
Usage
This model is designed for use in text-generation pipelines and can be easily integrated with the Hugging Face Transformers library. Its optimized architecture allows for inference on low-resource hardware, making it an excellent choice for applications that require efficient and scalable NLP solutions.
Example Code:
from transformers import AutoTokenizer, AutoModelForCausalLM
# Load the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("iFaz/llama32_3B_en_emo_v3")
model = AutoModelForCausalLM.from_pretrained("iFaz/llama32_3B_en_emo_v3")
# Generate text
input_text = "Explain the benefits of AI in education."
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Performance
The fine-tuned model demonstrates strong performance on instruction-based tasks, providing detailed and contextually accurate responses. The 4-bit quantization enhances its speed and reduces memory consumption, enabling usage on devices with limited computational resources.
Applications
- Conversational AI: Develop chatbots and virtual assistants with coherent, context-aware dialogue generation.
- Text Summarization: Extract concise summaries from lengthy texts for improved readability.
- Creative Writing: Assist in generating stories, articles, or creative content.
- Education: Enhance e-learning platforms with interactive and adaptive learning tools.
Limitations and Considerations
- Language Limitation: Currently optimized for English. Performance on other languages may be suboptimal.
- Domain-Specific Knowledge: While the model performs well on general tasks, it may require additional fine-tuning for domain-specific applications.
About the Developer
This model was developed and fine-tuned by iFaz, leveraging the capabilities of the unsloth/Llama-3.2-3B architecture to create an efficient and high-performance NLP tool.
Acknowledgments
The model builds upon the unsloth/Llama-3.2-3B framework and incorporates advancements in quantization techniques. Special thanks to the Hugging Face community for providing tools and resources to support NLP development.
License
The model is distributed under the Apache 2.0 License, allowing for both research and commercial use. For more details, refer to the license documentation.
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Model tree for iFaz/llama32_3B_en_emo_v3
Base model
meta-llama/Llama-3.2-3B-Instruct
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="iFaz/llama32_3B_en_emo_v3", filename="unsloth.Q4_K_M.gguf", )