stablelm-4e1t

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What stablelm-4e1t is

StableLM-3B-4E1T Model Description StableLM-3B-4E1T is a 3 billion parameter decoder-only language model pre-trained on 1 trillion tokens of diverse English and code datasets for 4 epochs. Usage Get started generating text with StableLM-3B-4E1T by using the following code snippet: Run with Flash Attention 2 ⚡️ Model Details Developed by: Stability AI Model type: StableLM-3B-4E1T models are auto-regressive language models based on the transformer decoder architecture. Language(s): English Library: GPT-NeoX License: Model checkpoints are licensed under the Creative Commons license (CC BY-SA-4.0). Under this license, you must give credit to Stability AI, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the Stability AI endorses you or your use. Contact: For questions and comments about the model, please email lm@stability.ai Model Architecture The model is a decoder-only transformer similar to the LLaMA (Touvron et al., 2023) architecture with the following modifications: Position Embeddings: Rotary Position Embeddings (Su et al., 2021) applied to the first 25% of head embedding dimensions for improved throughput following Black et al. (2022). Normalization: LayerNorm (Ba et al., 2016) with learned bias terms as opposed to RMSNorm (Zhang & Sennrich, 2019). Tokenizer: GPT-NeoX (Black et al., 2022). Training For complete dataset and training details, please see the StableLM-3B-4E1T Technical Report. Training Dataset The dataset is comprised of a filtered mixture of open-source large-scale datasets available on the HuggingFace Hub: Falcon RefinedWeb extract (Penedo et al., 2023), RedPajama-Data (Together Computer., 2023) and The Pile (Gao et al., 2020) both without the Books3 subset, and StarCoder (Li et al., 2023). Given the large amount of web data, we recommend fine-tuning the base StableLM-3B-4E1T for your downstream tasks. Training Procedure The model is pre-trained on the aforementioned datasets in bfloat16 precision, optimized with AdamW, and trained using the NeoX tokenizer with a vocabulary size of 50,257. We outline the complete hyperparameters choices in the project's

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

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Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Benchmarks

Reported results

As published on the model card — the maker's own numbers, not measured by AxForge.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys stablelm-4e1t for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (stablelm-4e1t below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/chat/completions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"stablelm-4e1t","messages":[{"role":"user","content":"Hello"}]}'

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