TinyLlama-1.1B-Chat-v1.0
TinyLlama-1.1B-Chat is a 1.1 billion parameter conversational LLM pretrained on 3 trillion tokens, designed for resource-constrained environments. It uses the Llama 2 architecture and tokenizer, making it compatible with existing Llama-based tools. The chat variant has been fine-tuned on synthetic dialogues and further aligned with preference learning, suitable for deployments where memory and compute are limited.
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 | 2.3M |
| Likes | 1.7k |
| Last updated | 2024-03-17 |
| Source | TinyLlama/TinyLlama-1.1B-Chat-v1.0 |
What TinyLlama-1.1B-Chat-v1.0 is
1.1B parameter decoder-only transformer matching Llama 2 architecture. Pretrained on 3T tokens from Cerebras SlimPajama, StarCoder, and chat data. Chat version fine-tuned on UltraChat synthetic dialogues, then DPO-aligned on UltraFeedback dataset (64k examples ranked by GPT-4). Requires transformers>=4.34. Supports bfloat16 inference with device_map="auto". No context length specified in provided data.
Run TinyLlama-1.1B-Chat-v1.0 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-Chat-v1.0")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: ~2.2–4.4 GB VRAM for bfloat16 inference (1.1B params × ~2–4 bytes per param). int8 quantization reduces to ~1.1–2.2 GB. Training: original pretraining used 16× A100-40G over 90 days. Fine-tuning on consumer GPUs (3090, 4090, or A10G) feasible with LoRA; full fine-tuning requires 20–40 GB VRAM. Verify with your batch size and precision.
LoRA and QLoRA strongly supported; training recipe follows HF Zephyr pattern. Card cites use of 🤗 TRL's DPOTrainer, indicating preference alignment is integrated workflow. Full fine-tuning feasible on high-end consumer GPUs (24–48 GB VRAM). SFT on smaller datasets (<10k examples) runs on 8GB+ with gradient checkpointing. Quantized training (int8/bfloat16 mixed) recommended for cost.
When to avoid it — and what to weigh
- High accuracy or specialized reasoning required — Model size constrains performance on complex reasoning, coding problems, and domain-specific tasks. Not suitable if baseline accuracy is critical.
- Long-context applications — Context length is not documented. If extended context windows are required, alternatives or explicit testing needed before deployment.
- Bleeding-edge capability requirements — Model was trained through Sept 2023 (training started 2023-09-01). Knowledge cutoff and improvements in newer frontier models not available.
- Ultra-low latency in production without optimization — While smaller than 7B models, requires quantization (int8/int4) and inference engines (vLLM, llama.cpp) for sub-100ms latencies on standard hardware.
License & commercial use
Apache 2.0 license (permissive OSI-compliant open-source license). Grants rights to use, modify, and distribute with minimal restrictions.
Apache 2.0 permits commercial use without explicit approval. No gating or restrictions noted (gated=false). However, review training data sources: UltraChat is synthetic/ChatGPT-generated (license compliance not stated), StarCoder data includes GPL code (verify downstream licensing risk if bundling), and UltraFeedback rankings use GPT-4 outputs (OpenAI terms of service may restrict commercial use of derivative outputs). Legal review recommended before production deployment.
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 trained on internet-scale data (SlimPajama, StarCoder) and synthetic dialogues; may encode biases, harmful patterns, or code vulnerabilities. No adversarial robustness testing documented. DPO alignment on GPT-4 rankings does not guarantee safety; red-team or prompt-injection testing required before sensitive applications. Training data filtering details unknown. Quantization/compression may alter model behavior; validate on representative prompts post-deployment.
Alternatives to consider
Phi-2 (Microsoft)
2.7B parameters, competitive performance on reasoning benchmarks, active development, but 2.4× larger footprint.
Mistral-7B
7B parameters, higher capability ceiling, permissive license, but requires more VRAM (14–28 GB) and does not fit tightly on edge devices.
Qwen-1.8B or Baichuan-1.3B
Comparable size and architecture, potentially better multilingual support, but less ecosystem integration and fewer HF deployments.
Ship TinyLlama-1.1B-Chat-v1.0 with senior software developers
TinyLlama-1.1B-Chat fits on-device and cost-optimized deployments. Review training data compliance, benchmark on your domain, and test with vLLM or llama.cpp before production rollout. Speak with our team to integrate into your inference pipeline.
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TinyLlama-1.1B-Chat-v1.0 FAQ
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What is the context window length?
Custom software development services
Need help beyond evaluating TinyLlama-1.1B-Chat-v1.0? 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 a Lightweight LLM?
TinyLlama-1.1B-Chat fits on-device and cost-optimized deployments. Review training data compliance, benchmark on your domain, and test with vLLM or llama.cpp before production rollout. Speak with our team to integrate into your inference pipeline.