Quantization made by Richard Erkhov.
lmlab-mistral-1b-untrained - GGUF
- Model creator: https://huggingface.co/lmlab/
- Original model: https://huggingface.co/lmlab/lmlab-mistral-1b-untrained/
Original model description:
license: apache-2.0 language: - en pipeline_tag: text-generation
Sorry everyone this got sort of popular but it doesnt generate understandable text - I think there's a way to make this generate good results w/ relatively little compute I'll experiment a bit later
LMLab Mistral 1B Untrained
This is an untrained base model modified from Mistral-7B-Instruct. It has 1.13 billion parameters.
Untrained
This model is untrained. This means it will not generate comprehensible text.
Model Details
Model Description
- Developed by: LMLab
- License: Apache 2.0
- Parameters: 1.13 billion (1,134,596,096)
- Modified from model:
mistralai/Mistral-7B-v0.1
Model Architecture
LMLab Mistral 1B is a transformer model, with the following architecture choices:
- Grouped-Query Attention
- Sliding-Window Attention
- Byte-fallback BPE tokenizer
Usage
Use MistralForCausalLM
.
from transformers import MistralForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('lmlab/lmlab-mistral-1b-untrained')
model = MistralForCausalLM.from_pretrained('lmlab/lmlab-mistral-1b-untrained')
text = "Once upon a time"
encoded_input = tokenizer(text, return_tensors='pt')
output = model.generate(**encoded_input)
print(tokenizer.decode(output[0]))
Notice
This model does not have any moderation systems.