⚠️ ARCHIVED / LEGACY MODEL NOTICE
This repository is part of a legacy collection quantized around 2023. To manage storage quotas and maintain active community projects, some rarely used quantization formats (e.g., Q2_K, Q3_K, Q4_1, Q5_1) have been permanently removed.
Only the most popular and stable formats (Q4_0, Q4_K_M, Q5_K_M, Q6_K, and Q8_0) remain available.
💡 Looking for something modern?
If you are starting a new project, we highly recommend using newer architectures (like Llama 3, Mistral, or Qwen) provided by official maintainers or active community members (e.g., Bartowski, TheBloke legacy files, or official organization handles).
⚠️ This repository is no longer actively maintained. Existing files are provided "as is" for archival and legacy hardware purposes.
I'm constantly enhancing these model descriptions to provide you with the most relevant and comprehensive information
stablelm-3b-4e1t - GGUF
- Model creator: stabilityai
- Original model: stablelm-3b-4e1t
StableLM
This is a Model based on StableLM.
Stablelm is a familiy of Language Models by Stability AI.
Note:
Current (as of 2023-11-15) implementations of Llama.cpp only support GPU offloading up to 34 Layers with these StableLM Models.
The model will crash immediately if -ngl is larger than 34.
The model works fine however without any gpu acceleration.
Brief
This is the Base Model of Stable LM 3b by Stability AI.
It is licensed under Creative Commons
cc-by-sa-4.0, as should all derivatives
About GGUF format
gguf is the current file format used by the
ggml library.
A growing list of Software is using it and can therefore use this model.
The core project making use of the ggml library is the
llama.cpp project by Georgi Gerganov
Quantization variants
There is a bunch of quantized files available to cater to your specific needs. Here's how to choose the best option for you:
Legacy quants
Q4_0, Q4_1, Q5_0, Q5_1 and Q8 are legacy quantization types.
Nevertheless, they are fully supported, as there are several circumstances that cause certain model not to be compatible with the modern K-quants.
Note:
Now there's a new option to use K-quants even for previously 'incompatible' models, although this involves some fallback solution that makes them not real K-quants. More details can be found in affected model descriptions.
(This mainly refers to Falcon 7b and Starcoder models)
K-quants
K-quants are designed with the idea that different levels of quantization in specific parts of the model can optimize performance, file size, and memory load.
So, if possible, use K-quants.
With a Q6_K, you'll likely find it challenging to discern a quality difference from the original model - ask your model two times the same question and you may encounter bigger quality differences.
Original Model Card:
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:
1from transformers import AutoModelForCausalLM, AutoTokenizer
2tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-3b-4e1t")
3model = AutoModelForCausalLM.from_pretrained(
4 "stabilityai/stablelm-3b-4e1t",
5 trust_remote_code=True,
6 torch_dtype="auto",
7)
8model.cuda()
9inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to("cuda")
10tokens = model.generate(
11 **inputs,
12 max_new_tokens=64,
13 temperature=0.75,
14 top_p=0.95,
15 do_sample=True,
16)
17print(tokenizer.decode(tokens[0], skip_special_tokens=True))
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:
| Parameters | Hidden Size | Layers | Heads | Sequence Length |
|---|
| 2,795,443,200 | 2560 | 32 | 32 | 4096 |
- 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
GitHub repository - config.
Training Infrastructure
-
Hardware: StableLM-3B-4E1T was trained on the Stability AI cluster across 256 NVIDIA A100 40GB GPUs (AWS P4d instances). Training began on August 23, 2023, and took approximately 30 days to complete.
-
Software: We use a fork of
gpt-neox (
EleutherAI, 2021), train under 2D parallelism (Data and Tensor Parallel) with ZeRO-1 (
Rajbhandari et al., 2019), and rely on flash-attention as well as SwiGLU and Rotary Embedding kernels from FlashAttention-2 (
Dao et al., 2023)
Use and Limitations
Intended Use
The model is intended to be used as a foundational base model for application-specific fine-tuning. Developers must evaluate and fine-tune the model for safe performance in downstream applications.
Limitations and Bias
As a base model, this model may exhibit unreliable, unsafe, or other undesirable behaviors that must be corrected through evaluation and fine-tuning prior to deployment. The pre-training dataset may have contained offensive or inappropriate content, even after applying data cleansing filters, which can be reflected in the model-generated text. We recommend that users exercise caution when using these models in production systems. Do not use the models if they are unsuitable for your application, or for any applications that may cause deliberate or unintentional harm to others.
How to Cite
1@misc{StableLM-3B-4E1T,
2 url={[https://huggingface.co/stabilityai/stablelm-3b-4e1t](https://huggingface.co/stabilityai/stablelm-3b-4e1t)},
3 title={StableLM 3B 4E1T},
4 author={Tow, Jonathan and Bellagente, Marco and Mahan, Dakota and Riquelme, Carlos}
5}
End of original Model File
Please consider to support my work
Coming Soon: I'm in the process of launching a sponsorship/crowdfunding campaign for my work. I'm evaluating Kickstarter, Patreon, or the new GitHub Sponsors platform, and I am hoping for some support and contribution to the continued availability of these kind of models. Your support will enable me to provide even more valuable resources and maintain the models you rely on. Your patience and ongoing support are greatly appreciated as I work to make this page an even more valuable resource for the community.
