this is a Safetensors sharded version of the original model detailed below
Bittensor Language Model (BTLM-3B-8k-base) is a 3 billion parameter language model with an 8k context length trained on 627B tokens of SlimPajama. BTLM-3B-8k-base sets a new standard for 3B parameter models, outperforming models trained on hundreds of billions more tokens and achieving comparable performance to open 7B parameter models. BTLM-3B-8k-base can also be quantized to 4-bit to fit in devices with as little as 3GB of memory. The model is made available with an Apache 2.0 license for commercial use.
BTLM-3B-8k was trained with a similar architecture to CerebrasGPT with the addition of SwiGLU nonlinearity, ALiBi position embeddings, and maximal update parameterization (muP). The model was trained for 1 epoch of SlimPajama-627B. 75% of training was performed with 2k sequence length. The final 25% of training was performed at 8k sequence length to enable long sequence applications
- Licensed for commercial use (Apache 2.0).
- State of the art 3B parameter model.
- Provides 7B model performance in a 3B model via performance enhancements from ALiBi, SwiGLU, maximal update parameterization (muP) and the the extensively deduplicated and cleaned SlimPajama-627B dataset.
- Fits in devices with as little as 3GB of memory when quantized to 4-bit.
- One of few 3B models that supports 8k sequence length thanks to ALiBi.
- Requires 71% fewer training FLOPs, has 58% smaller memory footprint for inference than comparable 7B models.
Note: Transformers does not support muP for all models, so BTLM-3B-8k-base requires a custom model class. This causes a situation where users must either (1) enable
trust_remote_code=True when loading the model or (2) acknowledge the warning about code execution upon loading the model.
from transformers import AutoTokenizer, AutoModelForCausalLM # Load the tokenizer and model tokenizer = AutoTokenizer.from_pretrained("cerebras/btlm-3b-8k-base") model = AutoModelForCausalLM.from_pretrained("cerebras/btlm-3b-8k-base", trust_remote_code=True, torch_dtype="auto") # Set the prompt for generating text prompt = "Albert Einstein was known for " # Tokenize the prompt and convert to PyTorch tensors inputs = tokenizer(prompt, return_tensors="pt") # Generate text using the model outputs = model.generate( **inputs, num_beams=5, max_new_tokens=50, early_stopping=True, no_repeat_ngram_size=2 ) # Convert the generated token IDs back to text generated_text = tokenizer.batch_decode(outputs, skip_special_tokens=True) # Print the generated text print(generated_text)
from transformers import AutoTokenizer, AutoModelForCausalLM from transformers import pipeline # Load the tokenizer and model tokenizer = AutoTokenizer.from_pretrained("cerebras/btlm-3b-8k-base") model = AutoModelForCausalLM.from_pretrained("cerebras/btlm-3b-8k-base", trust_remote_code=True, torch_dtype="auto") # Set the prompt for text generation prompt = """Isaac Newton was a """ # Create a text generation pipeline pipe = pipeline("text-generation", model=model, tokenizer=tokenizer) # Generate text using the pipeline generated_text = pipe( prompt, max_length=50, do_sample=False, no_repeat_ngram_size=2) # Print the generated text print(generated_text['generated_text'])
To enable long sequence applications, we use ALiBi position embeddings and trained on 470B tokens at the context length of 2,048 followed by 157B of tokens trained at 8,192 context length. To assess BTLM’s long sequence capability, we evaluate it on SlimPajama test set with 32,768 context length and plot loss at each token position. Although ALiBi allows extrapolation in theory, 2,048 context length training alone does not extrapolate well in practice. Thankfully variable sequence length training allows for substantially improved extrapolation. BTLM-3B extrapolates well up to 10k context length but the performance degrades slightly beyond this.
- Developed by: Cerebras Systems and Opentensor with generous support from G42 Cloud and IIAI
- License: Apache 2.0
- Model type: Decoder-only Language Model
- Architecture: GPT-2 style architecture with SwiGLU, ALiBi, and muP
- Data set: SlimPajama-627B
- Tokenizer: Byte Pair Encoding
- Vocabulary Size: 50257
- Sequence Length: 8192
- Optimizer: AdamW
- Positional Encoding: ALiBi
- Language: English
- Learn more: BTLM-3B-8k blog
- Paper: Coming soon
from transformers import AutoModelForCausalLM import torch model = AutoModelForCausalLM.from_pretrained("cerebras/btlm-3b-8k-base", trust_remote_code=True) # Get the parameter groups for the muP optimizer param_groups = model.get_mup_param_groups(lr=1e-3, weight_decay=0.1) # Set up the optimizer using AdamW with muP parameters optimizer = torch.optim.AdamW( param_groups, betas=(0.9, 0.95), eps=1e-8 )
Ensure the following muP parameters are passed in your config, otherwise your model will default to standard parameterization
The primary intended use is to further research into large language models. BTLM-3B-8k-base can be used as a foundation model for NLP, applications, ethics, and alignment research. We release these models with a fully permissive Apache license for the community to use freely.
You may fine-tune and adapt BTLM-3B-8k-base model via either Cerebras Model Studio or third-party libraries. Further safety-related testing and mitigations should be applied before using the BTLM-3B-8k-base in production downstream applications.
BTLM-3B-8k-base was trained on SlimPajama, with primarily English language, and is not recommended for machine translation tasks. BTLM-3B-8k-base has not been tuned for instruction-following or chat-based use cases.
Prompt: What is a manometer?" Generation: "A manometric device is an instrument that measures the pressure of a fluid."
Prompt: "What Robert Oppenheimer is famous for?" Generation: “Robert Opperman was a physicist who was the director of the Manhattan Project, which was responsible for the development of nuclear weapons.”
Prompt: "What was Albert Einstein known for?" Generation: “Albert Einstein was a German-born theoretical physicist who developed the theory of relativity, one of the two pillars of modern physics (alongside quantum mechanics).”
Prompt: “Who is Isaac Newton?” Generation: “Isaac Newton was an English physicist, mathematician, astronomer, alchemist, and theologian. He is widely recognized as one of the most influential scientists of all time and as a key figure in the scientific revolution.”
Prompt: "Where does the blood get pumped to after it leaves the right ventricle?" Generation: “The right atrium receives the deoxygenated blood from the pulmonary artery. The right side of the heart is the only side that receives de-oxygened...”
Prompt: "Peter lost his wallet after attending a local concert. To help him, Bob gave David 10 dollars. David then passed it on to Peter. Now what is the total amount of money Peter has?" Generation: “A. $10”
- Human life: The outputs from this model may or may not align with human values. The risk needs to be thoroughly investigated before deploying this model in a production environment where it can directly impact human life.
- Risks and harms: There may be distributional bias in the RedPajama dataset that can manifest in various forms in the downstream model deployment. There are other risks associated with large language models such as amplifying stereotypes, memorizing training data, or revealing private or secure information.
We are thankful to all Cerebras engineers that made this work possible.
We would like to acknowledge the generous support of G42 Cloud and the Inception Institute of Artificial Intelligence for providing compute time on Condor Galaxy 1.
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