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Llama-SmolTalk-3.2-1B-Instruct-GGUF

Text Generation
Transformers
GGUF
English
Llama
Llama-CPP
SmolTalk
ollama
bin
Inference Endpoints
conversational
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Llama-SmolTalk-3.2-1B-Instruct-GGUF / README.md
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metadata
license: creativeml-openrail-m
datasets:
  - HuggingFaceTB/smoltalk
language:
  - en
base_model:
  - meta-llama/Llama-3.2-1B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
  - Llama
  - Llama-CPP
  - SmolTalk
  - ollama
  - bin

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QuantFactory/Llama-SmolTalk-3.2-1B-Instruct-GGUF

This is quantized version of prithivMLmods/Llama-SmolTalk-3.2-1B-Instruct created using llama.cpp

Original Model Card

Updated Files for Model Uploads 🤗

File Name [ Updated Files ] Size Description Upload Status
.gitattributes 1.57 kB Git attributes configuration file Uploaded
README.md 42 Bytes Initial README Uploaded
config.json 1.03 kB Configuration file Uploaded
generation_config.json 248 Bytes Configuration for text generation Uploaded
pytorch_model.bin 2.47 GB PyTorch model weights Uploaded (LFS)
special_tokens_map.json 477 Bytes Special token mappings Uploaded
tokenizer.json 17.2 MB Tokenizer configuration Uploaded (LFS)
tokenizer_config.json 57.4 kB Additional tokenizer settings Uploaded
Model Type Size Context Length Link
GGUF 1B - 🤗 Llama-SmolTalk-3.2-1B-Instruct-GGUF

The Llama-SmolTalk-3.2-1B-Instruct model is a lightweight, instruction-tuned model designed for efficient text generation and conversational AI tasks. With a 1B parameter architecture, this model strikes a balance between performance and resource efficiency, making it ideal for applications requiring concise, contextually relevant outputs. The model has been fine-tuned to deliver robust instruction-following capabilities, catering to both structured and open-ended queries.

Key Features:

  1. Instruction-Tuned Performance: Optimized to understand and execute user-provided instructions across diverse domains.
  2. Lightweight Architecture: With just 1 billion parameters, the model provides efficient computation and storage without compromising output quality.
  3. Versatile Use Cases: Suitable for tasks like content generation, conversational interfaces, and basic problem-solving.

Intended Applications:

  • Conversational AI: Engage users with dynamic and contextually aware dialogue.
  • Content Generation: Produce summaries, explanations, or other creative text outputs efficiently.
  • Instruction Execution: Follow user commands to generate precise and relevant responses.

Technical Details:

The model leverages PyTorch for training and inference, with a tokenizer optimized for seamless text input processing. It comes with essential configuration files, including config.json, generation_config.json, and tokenization files (tokenizer.json and special_tokens_map.json). The primary weights are stored in a PyTorch binary format (pytorch_model.bin), ensuring easy integration with existing workflows.

Model Type: GGUF
Size: 1B Parameters

The Llama-SmolTalk-3.2-1B-Instruct model is an excellent choice for lightweight text generation tasks, offering a blend of efficiency and effectiveness for a wide range of applications.