Name	Quant method	Size
stablelm-3b-4e1t.Q2_K.gguf	Q2_K	1.01GB
stablelm-3b-4e1t.IQ3_XS.gguf	IQ3_XS	1.11GB
stablelm-3b-4e1t.IQ3_S.gguf	IQ3_S	1.17GB
stablelm-3b-4e1t.Q3_K_S.gguf	Q3_K_S	1.17GB
stablelm-3b-4e1t.IQ3_M.gguf	IQ3_M	1.23GB
stablelm-3b-4e1t.Q3_K.gguf	Q3_K	1.3GB
stablelm-3b-4e1t.Q3_K_M.gguf	Q3_K_M	1.3GB
stablelm-3b-4e1t.Q3_K_L.gguf	Q3_K_L	1.4GB
stablelm-3b-4e1t.IQ4_XS.gguf	IQ4_XS	1.43GB
stablelm-3b-4e1t.Q4_0.gguf	Q4_0	1.5GB
stablelm-3b-4e1t.IQ4_NL.gguf	IQ4_NL	1.51GB
stablelm-3b-4e1t.Q4_K_S.gguf	Q4_K_S	1.51GB
stablelm-3b-4e1t.Q4_K.gguf	Q4_K	1.59GB
stablelm-3b-4e1t.Q4_K_M.gguf	Q4_K_M	1.59GB
stablelm-3b-4e1t.Q4_1.gguf	Q4_1	1.65GB
stablelm-3b-4e1t.Q5_0.gguf	Q5_0	1.81GB
stablelm-3b-4e1t.Q5_K_S.gguf	Q5_K_S	1.81GB
stablelm-3b-4e1t.Q5_K.gguf	Q5_K	1.86GB
stablelm-3b-4e1t.Q5_K_M.gguf	Q5_K_M	1.86GB
stablelm-3b-4e1t.Q5_1.gguf	Q5_1	1.96GB
stablelm-3b-4e1t.Q6_K.gguf	Q6_K	2.14GB
stablelm-3b-4e1t.Q8_0.gguf	Q8_0	2.77GB

Original model description:

language:

en license: cc-by-sa-4.0 tags:
causal-lm datasets:
tiiuae/falcon-refinedweb
togethercomputer/RedPajama-Data-1T
CarperAI/pilev2-dev
bigcode/starcoderdata
allenai/peS2o model-index:
name: stablelm-3b-4e1t results:
- task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics:
  - type: acc_norm value: 46.59 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=stabilityai/stablelm-3b-4e1t name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics:
  - type: acc_norm value: 75.94 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=stabilityai/stablelm-3b-4e1t name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics:
  - type: acc value: 45.23 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=stabilityai/stablelm-3b-4e1t name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics:
  - type: mc2 value: 37.2 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=stabilityai/stablelm-3b-4e1t name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics:
  - type: acc value: 71.19 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=stabilityai/stablelm-3b-4e1t name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics:
  - type: acc value: 3.34 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=stabilityai/stablelm-3b-4e1t name: Open LLM Leaderboard

`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:

python

from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-3b-4e1t")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-3b-4e1t",
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.75,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))

Run with Flash Attention 2 ⚡️

Click to expand

python

from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-3b-4e1t")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-3b-4e1t",
  torch_dtype="auto",
  attn_implementation="flash_attention_2",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device)
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.75,
  top_p=0.95,
  do_sample=True,
)
print(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

bibtex

@misc{StableLM-3B-4E1T,
      url={[https://huggingface.co/stabilityai/stablelm-3b-4e1t](https://huggingface.co/stabilityai/stablelm-3b-4e1t)},
      title={StableLM 3B 4E1T},
      author={Tow, Jonathan and Bellagente, Marco and Mahan, Dakota and Riquelme, Carlos}
}

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	46.58
AI2 Reasoning Challenge (25-Shot)	46.59
HellaSwag (10-Shot)	75.94
MMLU (5-Shot)	45.23
TruthfulQA (0-shot)	37.20
Winogrande (5-shot)	71.19
GSM8k (5-shot)	3.34

RichardErkhov/stabilityai_-_stablelm-3b-4e1t-gguf