Text Classification
Transformers
Safetensors
English
llama
text-generation
text-embeddings-inference
Instructions to use netcat420/MFANNv0.11 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use netcat420/MFANNv0.11 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="netcat420/MFANNv0.11")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("netcat420/MFANNv0.11") model = AutoModelForCausalLM.from_pretrained("netcat420/MFANNv0.11", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 796 Bytes
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library_name: transformers
license: llama3
datasets:
- netcat420/MFANN
language:
- en
pipeline_tag: text-classification
---
MFANN 8b version 0.11 32-bit

fine-tuned on the MFANN dataset as of 5/22/24 as it is an ever expanding dataset. these are the full 32-bit unquantized weights.
SYSTEM PROMPT:
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are a helpful, respectful and honest assistant. Always answer as helpfully as possible.
If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
<|eot_id|> |