stablelm-3b-4e1t
StableLM-3B-4E1T is a 2.8B parameter, open-source language model from Stability AI trained on 1 trillion tokens across diverse English and code datasets. It uses a standard transformer decoder architecture with rotary position embeddings and LayerNorm. The model is ungated, permissively licensed (CC BY-SA 4.0), and designed as a base model for fine-tuning rather than production use out-of-the-box. It supports standard text generation via HuggingFace transformers and can optionally run with Flash Attention 2 for improved inference throughput.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | stabilityai |
| Parameters | 2.8B |
| Context window | Unknown |
| License | cc-by-sa-4.0 — Requires review (not clearly OSI) |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 93.3k |
| Likes | 314 |
| Last updated | 2024-03-07 |
| Source | stabilityai/stablelm-3b-4e1t |
What stablelm-3b-4e1t is
Decoder-only transformer with 2.8B parameters, 32 layers, 2560 hidden size, 32 attention heads, and 4096 token context window. Uses GPT-NeoX tokenizer (50.2K vocab), bfloat16 training precision, and AdamW optimizer. Training leveraged 256 NVIDIA A100 GPUs over ~30 days (August–September 2023). Trained on filtered mixture of Falcon RefinedWeb, RedPajama-Data, The Pile (excl. Books3), and StarCoder. Position embeddings apply rotary embeddings to 25% of head dimensions. Architecture closely follows LLaMA with noted exceptions (LayerNorm vs RMSNorm, rotary PE partial application).
Run stablelm-3b-4e1t locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="stabilityai/stablelm-3b-4e1t")out = pipe("Explain retrieval-augmented generation in one sentence.", max_new_tokens=128)print(out[0]["generated_text"])Swap in vLLM or Ollama for production-grade serving. DEV.co can stand up the inference stack.
How you'd run it
A typical self-hosted path — open weights, an inference server, your application.
DEV.co builds each layer — from GPU infrastructure to the application.
Best use cases
Running & fine-tuning it
ESTIMATE: ~11–12 GB VRAM (bfloat16 full precision). With quantization (int8): ~6 GB; int4 (GGUF/llama.cpp): ~2–3 GB. For fine-tuning with LoRA on consumer GPUs (e.g., RTX 3090), typically 16–24 GB recommended. Inference batch size and max tokens will vary by available memory.
Model card and architecture are amenable to LoRA/QLoRA fine-tuning. Standard PyTorch/HuggingFace Training framework support. Parameter count (2.8B) makes single-GPU fine-tuning feasible with gradient checkpointing and low-rank adaptation. No specific fine-tuning instructions or hyperparameters provided in card; refer to GitHub config and StableLM technical report for training details. Flash Attention 2 support available for training acceleration.
When to avoid it — and what to weigh
- Production deployment without fine-tuning or safeguards — Model card explicitly states it is a base model with potential for unreliable, unsafe, or undesirable outputs. Pre-training data may contain offensive content. Requires evaluation and alignment before production use.
- Complex reasoning or mathematical problem-solving — Open LLM Leaderboard shows weak GSM8k score (3.34/5-shot), indicating poor quantitative reasoning. Not suitable for math-heavy or formal logic applications without significant fine-tuning.
- Very long-context or document-heavy applications — 4096 token context window is limited for multi-document retrieval or very long-form synthesis. Alternatives with 8K+ context windows may be more suitable for RAG or summarization at scale.
- Applications requiring certifiable safety or compliance guarantees — As a base model trained on web data, it cannot provide formal safety, bias, or regulatory compliance assurances. Requires substantial post-training evaluation and safety work.
License & commercial use
Licensed under Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0). This is an OSI-compatible, permissive open-source license. Model and derived works must attribute Stability AI, link to the license, and indicate modifications. Redistribution of the model is permitted under the same license terms.
CC BY-SA 4.0 permits commercial use. You may use, modify, and redistribute the model in commercial products and services provided you: (1) attribute Stability AI and link to the CC BY-SA 4.0 license; (2) indicate any changes made; (3) apply the same license (ShareAlike clause) to derived models or significant modifications. The ShareAlike requirement may impose obligations on downstream users if you redistribute modified versions. For mission-critical or highly integrated use cases, legal review of attribution and ShareAlike compliance is prudent.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Moderate |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Low |
| DEV.co fit | Strong |
| Assessment confidence | High |
Base model, no formal security audit stated. Pre-training on web-scale data (RefinedWeb, RedPajama, Pile) introduces potential exposure to adversarial, copyrighted, or sensitive content. No formal defense against prompt injection, jailbreaking, or adversarial inputs documented. Intended as base model only; fine-tuning and evaluation on sensitive tasks required before deployment. No mention of differential privacy, data sanitization depth, or membership inference resistance. Requires post-training safety evaluation (RLHF, red-teaming) for production.
Alternatives to consider
Phi-2 (Microsoft, 2.7B)
Similar parameter count, stronger math/reasoning benchmark performance (GSM8k). May suit quantitative workloads better; check license (MIT-like permissive). Smaller context window (2048).
Mistral-7B (Mistral AI, 7B)
Larger model (~2.5x), stronger general benchmarks (46%+ average on Open LLM). Apache 2.0 license. Requires more VRAM (~14–16 GB), but significantly better performance on reasoning and instruction-following.
TinyLlama-1.1B (Open)
Smaller footprint (1.1B), fits on edge/mobile devices. Trade-off: weaker capability. Useful if extreme resource constraints override quality needs. CC BY-SA licensed.
Ship stablelm-3b-4e1t with senior software developers
Assess your fine-tuning and infrastructure needs. StableLM-3B-4E1T is ideal for on-device or self-hosted deployments. Review the model card, benchmark results, and safety considerations. Contact Devco to discuss custom application development, RAG integration, or quantization strategies for your use case.
Talk to DEV.coRelated open-source tools
Surfaced by semantic similarity across the DEV.co open-source index.
Related on DEV.co
Explore the category and the services that help you build with it.
stablelm-3b-4e1t FAQ
Can I use StableLM-3B-4E1T commercially?
What GPU/hardware do I need to run this model?
Why is the GSM8k benchmark score so low (3.34)?
Is this model safe to deploy in production without changes?
Software developers & web developers for hire
Need help beyond evaluating stablelm-3b-4e1t? DEV.co is a software development agency offering software development services and web development for teams of every size. Our software developers and web developers build custom software, web applications, APIs, and open-source llms integrations — and maintain them long-term.
Ready to Deploy StableLM-3B-4E1T?
Assess your fine-tuning and infrastructure needs. StableLM-3B-4E1T is ideal for on-device or self-hosted deployments. Review the model card, benchmark results, and safety considerations. Contact Devco to discuss custom application development, RAG integration, or quantization strategies for your use case.