TinyLlama_v1.1
TinyLlama v1.1 is a 1.1B-parameter language model built on Llama 2 architecture, trained on 2 trillion tokens. It is designed for memory-constrained environments and can run on commodity hardware. Three variants exist: general-purpose, math/code-optimized, and Chinese-optimized. Licensed under Apache 2.0 (permissive), ungated, and requires ~2.2 GB VRAM for inference at FP16.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | TinyLlama |
| Parameters | Unknown |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 38.1k |
| Likes | 113 |
| Last updated | 2024-06-07 |
| Source | TinyLlama/TinyLlama_v1.1 |
What TinyLlama_v1.1 is
TinyLlama v1.1 (1.1B params) uses Llama 2 architecture and tokenizer for compatibility with Llama ecosystem. Training spans three stages: (1) basic pretraining on SlimPajama (1.5T tokens), (2) continual pretraining with domain-specific corpora (Math&Code or Chinese variants), (3) cooldown phase with increased batch size. Model was trained on 4× A100-40G nodes with ~1.8M batch size, using cosine learning rate scheduling. Evaluation shows 53.63% average score on 7-benchmark suite (HellaSwag, OBQA, WinoGrande, ARC-c, ARC-e, BoolQ, PIQA), outperforming Pythia-1.0B (48.30%). Last updated 2024-06-07.
Run TinyLlama_v1.1 locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="TinyLlama/TinyLlama_v1.1")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
Inference at FP16: ~2.2 GB VRAM (1.1B params × 2 bytes). At INT8: ~1.1 GB. At GPTQ/AWQ quantization: 256–512 MB. Minimal CPU inference possible via llama.cpp (5–10 tokens/sec on modern CPU). Training: 4× A100-40G (used in original work); fine-tuning via LoRA requires 8–16 GB VRAM on single A100/RTX 4090. Batch inference on CPU or single V100/RTX 3090 feasible for <100 concurrent requests.
Llama 2 architecture supports LoRA/QLoRA. No official fine-tuning guide provided in card; refer to HuggingFace transformers documentation and TinyLlama GitHub. LoRA rank 8–16 with alpha 16 estimated at ~6–8 GB VRAM on RTX 3090. Instruction-tuning datasets (Alpaca, OpenAssistant) compatible. No mention of distributed fine-tuning setup; single-GPU tuning recommended.
When to avoid it — and what to weigh
- High language complexity or multilingual requirements — 53.63% benchmark average is significantly below 7B+ models. Expect reduced coherence on complex reasoning, long-context dependencies, and language nuance outside English/Chinese variants.
- Real-time latency-critical applications without optimization — 1.1B model is slower than distilled models (e.g., MobileBERT). Requires quantization and inference optimization (TGI, vLLM, llama.cpp) to meet <100ms SLA.
- Safety-critical or high-accuracy applications — No safety training, alignment, or jailbreak mitigations mentioned. Do not use for content moderation, child safety, or contexts requiring adversarial robustness without fine-tuning.
- Workflows requiring long context windows — Context length is unknown. Likely limited to standard 2K–4K tokens (Llama 2 base). Not suitable for long-document processing without retrieval augmentation.
License & commercial use
Apache License 2.0 (SPDX: apache-2.0). Permissive OSI-approved license. Permits commercial use, modification, and distribution with attribution and patent clause. No restrictions on commercial deployment.
Commercial use is permitted under Apache 2.0. No gating, no restrictions on training, fine-tuning, or serving in production. Can be freely integrated into commercial products and SaaS offerings. Ensure compliance with upstream data sources (SlimPajama, StarCoder, Proof Pile, Skypile); verify their licenses if modifying or redistributing training data.
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 |
Model has no mentioned safety training or alignment. Inherits potential risks from pretraining data (SlimPajama, StarCoder, etc.): may generate harmful, biased, or nonsensical content. No jailbreak mitigations. Recommend: (1) sandbox inference environments, (2) input validation/content filtering, (3) optional RLHF or instruction-tuning with safety datasets before production deployment. No known CVEs or supply-chain concerns; Apache 2.0 source is auditable.
Alternatives to consider
Phi-2 (2.7B, Microsoft)
Slightly larger, newer pretraining (1.4T tokens), better benchmarks on common sense tasks. Less cost-effective; requires more VRAM.
MiniLM / DistilBERT (110M–335M)
Smaller, encoder-only models for classification/embedding. Faster but unsuitable for open-ended text generation.
Mistral-7B quantized (AWQ/GPTQ)
Ship TinyLlama_v1.1 with senior software developers
TinyLlama v1.1 is production-ready for edge and private deployments. Start with quantized inference (llama.cpp, Ollama) or serve via vLLM/TGI. Review the GitHub repository and arXiv paper for setup and benchmarks.
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TinyLlama_v1.1 FAQ
Can I use TinyLlama v1.1 in a commercial product?
What GPU or CPU do I need?
Is context length documented?
How does it compare to larger open models?
Custom software development services
DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If TinyLlama_v1.1 is part of your open-source llms roadmap, our team can implement, customize, migrate, and maintain it.
Ready to deploy a lightweight LLM?
TinyLlama v1.1 is production-ready for edge and private deployments. Start with quantized inference (llama.cpp, Ollama) or serve via vLLM/TGI. Review the GitHub repository and arXiv paper for setup and benchmarks.