TinyLlama-1.1B-intermediate-step-1431k-3T
TinyLlama-1.1B is a 1.1 billion parameter language model trained on 3 trillion tokens, designed to match Llama 2's architecture while fitting into memory-constrained environments. It achieves competitive performance on standard benchmarks for its size and can be deployed on consumer hardware. The model is open-source under Apache 2.0, making it suitable for self-hosted and embedded AI applications.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | TinyLlama |
| Parameters | 1.1B |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 67.3k |
| Likes | 192 |
| Last updated | 2024-09-27 |
| Source | TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T |
What TinyLlama-1.1B-intermediate-step-1431k-3T is
A 1.1B-parameter causal language model using Llama 2 architecture and tokenizer, trained on 3 trillion tokens sourced from Cerebras SlimPajama and BigCode StarCoder datasets. Last checkpoint modified September 2024. Supports standard transformers pipeline with PyTorch and SafeTensors format. No gating. Context length not publicly stated. Evaluated on Open LLM Leaderboard with average score of 36.42 (HellaSwag: 60.31, MMLU: 26.04, TruthfulQA: 37.32, Winogrande: 59.51). Significantly weaker on mathematical reasoning (GSM8k: 1.44).
Run TinyLlama-1.1B-intermediate-step-1431k-3T locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T")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: FP32 inference requires ~4.4 GB VRAM; FP16 ~2.2 GB VRAM; 8-bit quantization ~1.1 GB VRAM; 4-bit quantization ~0.5–0.7 GB VRAM. Suitable for single consumer GPUs (RTX 3060 or better), CPU inference with quantization, or edge accelerators. Batch inference and fine-tuning will increase memory demands. Verify with actual deployment stack.
Llama 2 architecture enables LoRA and QLoRA fine-tuning with minimal compute. No official fine-tuning guide in card excerpt. Standard transformer fine-tuning libraries (HuggingFace Transformers, PEFT, bitsandbytes) are compatible. Small parameter count makes full fine-tuning feasible on single GPU with modest VRAM. Requires review of GitHub repository for training recipes and data format expectations.
When to avoid it — and what to weigh
- Complex reasoning or math-heavy tasks — GSM8k score of 1.44 indicates severe limitations on mathematical reasoning. Not suitable for applications requiring symbolic logic, advanced quantitative analysis, or multi-step problem solving.
- High semantic precision required — MMLU score of 26.04 suggests weak performance on knowledge-intensive tasks and factual accuracy. Avoid for applications where hallucination or incorrectness creates liability (legal, medical advice, safety-critical systems).
- Production systems without fallback capacity — Limited evaluation on specialized domains or proprietary tasks. Lack of domain-specific fine-tuning baseline data means performance in your use case is Unknown. Requires validation before deployment.
- Real-time latency-sensitive applications with modest hardware — While compact, inference speed depends heavily on quantization and serving setup. Unknown context length and no published latency benchmarks make performance guarantees difficult.
License & commercial use
Apache License 2.0 (OSI-compliant permissive license). Requires attribution in source and binary distributions. No patent claims granted or revoked. No liability limitations for the licensor.
Apache 2.0 is a permissive OSI license that explicitly permits commercial use, modification, and distribution, provided the license and copyright notice are retained. No royalties, usage restrictions, or approval gates. However, verify that your intended use case does not conflict with any third-party obligations imposed by the training data sources (Cerebras SlimPajama and BigCode StarCoder). Data attribution and licensing of those datasets is your responsibility to audit.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Low |
| DEV.co fit | Strong |
| Assessment confidence | High |
No security audit, adversarial robustness, or jailbreak evaluation provided. Model trained on public web data (SlimPajama, StarCoder); inherent risk of encoding biases, toxic content, or sensitive information. Apache 2.0 license provides no warranty. For sensitive use cases (user-facing, safety-critical), conduct adversarial testing, content filtering evaluation, and consider output monitoring. No exploit details or vulnerability disclosure provided.
Alternatives to consider
Phi-2 (2.7B, Microsoft)
Slightly larger (2.7B), reported stronger reasoning performance, also Apache 2.0, aimed at efficiency. Better MMLU/GSM8k scores if available benchmarks are reliable.
MobileLLM (0.5–1.2B, Apple)
Explicitly optimized for mobile/edge inference. If targeting mobile deployment, may offer better on-device performance per parameter; newer training techniques.
Pythia-1.0B (Eleuther AI)
Similar size, well-established benchmark baseline (shown in TinyLlama eval table). Stronger community resources and research backing; good for comparison and reproducibility.
Ship TinyLlama-1.1B-intermediate-step-1431k-3T with senior software developers
TinyLlama-1.1B offers a compelling balance of efficiency and capability for self-hosted and edge AI. Test it on your hardware and benchmark against your domain data. Use LoRA fine-tuning to customize for your specific task. Contact Devco to architect a production deployment strategy.
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TinyLlama-1.1B-intermediate-step-1431k-3T FAQ
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Software developers & web developers for hire
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-1.1B-intermediate-step-1431k-3T is part of your open-source llms roadmap, our team can implement, customize, migrate, and maintain it.
Evaluate TinyLlama for Your Use Case
TinyLlama-1.1B offers a compelling balance of efficiency and capability for self-hosted and edge AI. Test it on your hardware and benchmark against your domain data. Use LoRA fine-tuning to customize for your specific task. Contact Devco to architect a production deployment strategy.