Model Card for Model ID
Walsh_Instruct-1.7b
Model Details
- Model Dimension: 2048
- Hidden Layers: 32
- Attention Heads: 32
- Feedforward Dimension: 8192
- Feedforward Network Type: Conventional MLP with GeLU activation
- Vocabulary Size: 32000
- Max Sequence Length: 16K (14-bit absolute positional encoding via Walsh matrix)
- Weight Initialization: DeepNet, https://arxiv.org/abs/2203.00555
- Pretraining Datasets: RedPajama-Data-1T, mostly "books" and some Wikipedia.
Model Description
This is an instruction tuned fork of my "dinalt/walsh-1-7b" model... mostly for fun.
Hadamard-Walsh 1.7B is an experimental model using a new positional encoder. The encoder represents absolute positions by using a combination of rows from the Hadamard-Walsh matrix (https://en.wikipedia.org/wiki/Hadamard_code). Each row corresponds to a binary digit is the positional code, where the presence of a row codes for a 1 and the absence, a zero. While training, the base offset in the sequence is randomly chosen for each batch. The result is that the model is very proficient at sequences much longer than those seen in training.
Aside from the unsual positional encoder, the most interesting aspect of this model is the application of DITTO training:
Learning to Break the Loop: Analyzing and Mitigating Repetitions for Neural Text Generation https://arxiv.org/abs/2206.02369
As described in the paper, the procedure is very effective at eliminating sentence level repition. As described in the paper, it also reduces perplexity slightly.
I will see about posting the code for running the training and generating a DITTO dataset later, althogh the "ditto-loss" function is already in the model implementation.
- Developed by: Jason dinAlt
- Model type: Causal language model. Instruction following. Text generation.
Model Sources [optional]
- Repository: https://huggingface.co/dinalt/walsh-1-7b
Uses
This is a toy instruciton following model. It's occasionally reliable at following directions.
Direct Use
[More Information Needed]
Bias, Risks, and Limitations
This is an uncensored instruction following model. No attempt has been made to make the model "safe." It may offend your sensibilities. It will likely provide inaccurate information. Use at your own risk. Whatever you do, don't put it in charge of the global defense grid!
How to Get Started with the Model
The easiest way to get started with the model is to use text-generation-webui, which needs to be started with the "--trust-remote-code" flag.
https://github.com/oobabooga/text-generation-webui
It appears to work best with the "Big O" and "Simple-1" generation presets.
Prompt Format
As an instruction model, the model has been trained to use the ChatML instruction format:
<|im_start|>system
Provide some context and/or instructions to the model.
<|im_end|>
<|im_start|>user
The user’s message goes here
<|im_end|>
<|im_start|>assistant
For details, see: https://github.com/MicrosoftDocs/azure-docs/blob/main/articles/ai-services/openai/includes/chat-markup-language.md#chatml
Loading:
The model implementation is all my own, so you will need to use "trust_remote_code" to load the model.
from transformers import (
AutoTokenizer,
AutoModelForCausalLM,
)
model_id = "dinalt/walsh-1-7b"
model = AutoModelForCausalLM.from_pretrained(
model_id,
trust_remote_code=True,
# flash_attention_2 requires bfloat16 or float16
torch_dtype=torch.bfloat16,
# One of ["flash_attention_2", "sdpa", "eager"]
attn_implementation="flash_attention_2",
)
tokenizer = AutoTokenizer.from_pretrained(model_id)
For batch instruction generation, see my example code here: https://discuss.huggingface.co/t/implimentation-of-stopping-criteria-list/20040/16?u=dinalt
Training Details
Training Data
[More Information Needed]
Training Procedure
Preprocessing [optional]
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Training Hyperparameters
- Training regime: [More Information Needed]
Speeds, Sizes, Times [optional]
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Evaluation
Testing Data, Factors & Metrics
Testing Data
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Factors
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Metrics
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Results
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Summary
Model Examination [optional]
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Environmental Impact
It keeps my house warm in the winter...
Technical Specifications [optional]
Model Architecture and Objective
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Compute Infrastructure
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Hardware
6 x RTX4090
Software
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Glossary [optional]
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More Information [optional]
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Model Card Authors [optional]
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Model Card Contact
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