mlfoundations-dev/oh-dcft-v3.1-claude-3-5-sonnet-20241022

Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:8BQuant:FP8Ctx Length:32kPublished:Jan 14, 2025License:llama3.1Architecture:Transformer0.0K Warm

The mlfoundations-dev/oh-dcft-v3.1-claude-3-5-sonnet-20241022 model is an 8 billion parameter language model, fine-tuned from Meta-Llama-3.1-8B. This model has been specifically fine-tuned on the mlfoundations-dev/oh-dcft-v3.1-claude-3-5-sonnet-20241022 dataset, indicating a specialization derived from its training data. With a context length of 32768 tokens, it is designed for tasks benefiting from extensive contextual understanding. Its primary differentiation lies in its fine-tuning process, which aims to adapt the base Llama 3.1 architecture for specific applications related to its training dataset.

Loading preview...

Model Overview

The mlfoundations-dev/oh-dcft-v3.1-claude-3-5-sonnet-20241022 is an 8 billion parameter language model, fine-tuned from the meta-llama/Meta-Llama-3.1-8B base architecture. This model leverages a substantial context window of 32,768 tokens, enabling it to process and generate responses based on extensive input.

Key Capabilities

  • Fine-tuned Performance: The model has undergone specific fine-tuning on the mlfoundations-dev/oh-dcft-v3.1-claude-3-5-sonnet-20241022 dataset, suggesting optimized performance for tasks aligned with this data.
  • Llama 3.1 Base: Benefits from the robust architecture and pre-training of the Meta-Llama-3.1-8B model.
  • Extended Context: Supports a 32k token context length, suitable for applications requiring deep contextual understanding or processing long documents.

Training Details

The model was trained with a learning rate of 5e-06 over 3 epochs, utilizing a total batch size of 512 across 8 GPUs. The training process achieved a final validation loss of 0.4631, indicating effective learning from the fine-tuning dataset.

Potential Use Cases

  • Specialized Text Generation: Ideal for generating text in domains represented by its fine-tuning dataset.
  • Context-Rich Applications: Suitable for tasks like summarization, question answering, or content creation where long input sequences are common.

Popular Sampler Settings

Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.

temperature
top_p
top_k
frequency_penalty
presence_penalty
repetition_penalty
min_p