TinyLlama-1.1B-Chat-v1.0-GPTQ
TinyLlama-1.1B-Chat-v1.0-GPTQ is a quantized 1.1B parameter conversational LLM optimized for resource-constrained inference. TheBloke provides multiple GPTQ quantization variants (4-bit and 8-bit) with different group sizes, allowing trade-offs between VRAM usage and output quality. Licensed under Apache 2.0 with no access gating, it is suitable for edge deployment, local inference, and cost-sensitive applications where speed and memory footprint take priority over state-of-the-art quality.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | TheBloke |
| Parameters | 1.1B |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 132.3k |
| Likes | 14 |
| Last updated | 2023-12-31 |
| Source | TheBloke/TinyLlama-1.1B-Chat-v1.0-GPTQ |
What TinyLlama-1.1B-Chat-v1.0-GPTQ is
This is a quantized derivative of TinyLlama's 1.1B chat model, provided in GPTQ format with multiple parameter configurations. Quantization uses AutoGPTQ or Transformers with calibration on VMware Open Instruct dataset (sequence length 2048). Variants range from 4-bit with group size 32 (0.82 GB, highest quality) to 8-bit with no group size (1.23 GB, lower VRAM). The model uses the Zephyr prompt template. Known compatible servers include text-generation-webui, KoboldAI, LoLLMS, and Hugging Face TGI. ExLlama compatibility available for select 4-bit variants. Last updated 2023-12-31.
Run TinyLlama-1.1B-Chat-v1.0-GPTQ locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="TheBloke/TinyLlama-1.1B-Chat-v1.0-GPTQ")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
Estimated: 4-bit models require ~2–3 GB VRAM for inference (batch size 1). 8-bit models require ~3–4 GB VRAM. Tested/compatible with NVidia GPUs on Linux/Windows; AMD on Linux. CPU-only inference possible via GGUF (slower). For fine-tuning: QLoRA feasible on 8–16 GB VRAM; full-parameter tuning impractical without significant VRAM headroom. Verify exact VRAM usage with your target deployment hardware before production.
Model card does not provide LoRA or QLoRA instructions. Fine-tuning feasibility: QLoRA likely viable on GPUs with ≥12 GB VRAM (requires external tooling, e.g., peft + bitsandbytes). Full fine-tuning not recommended due to small model size and quantization; better to use base unquantized model if adaptation needed. No official training scripts or adapter weights provided.
When to avoid it — and what to weigh
- Reasoning or coding tasks requiring strong semantic understanding — 1.1B models show limited capability on complex reasoning, mathematical problem-solving, and multi-step code generation. Larger models (7B+) are preferable for these domains.
- Production use cases demanding high output quality or safety guarantees — Quantization reduces precision and may amplify hallucination or factual errors. No safety audits or guardrails mentioned; production systems should add separate filtering or moderation layers.
- Long-context or document-intensive workflows — Quantization calibration tested only at 2048-token sequence length; context window size not specified. Not suitable if you need consistent quality on 8K+ token inputs.
- macOS deployment — GPTQ format supported only on Linux (NVidia/AMD) and Windows (NVidia). macOS users must use GGUF variants instead.
License & commercial use
Apache 2.0 license (OSI-approved). Permissive license allowing modification, distribution, and commercial use under standard ASL 2.0 terms (retain attribution and license notice; no warranty). No additional gating or restrictions on access (gated: false).
Apache 2.0 permits commercial use without royalties or special permission. However: (1) derivative/quantized models inherit the base model's license; review TinyLlama's original terms for any upstream constraints. (2) No explicit liability waiver or indemnity in Apache 2.0; production deployments should evaluate risk tolerance. (3) Model card contains no mention of data provenance restrictions (training datasets include open community sources); no guarantees on intellectual property clearance. Consult legal counsel for regulated industries (finance, healthcare, etc.).
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Stale |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Low |
| DEV.co fit | Good |
| Assessment confidence | High |
Quantization reduces model transparency (compressed weights harder to audit). No formal security audit or robustness testing mentioned. Standard LLM risks apply: potential for prompt injection, hallucination, and bias inherited from training data (Cerebras SlimPajama, BigCode, OpenAssistant). No guardrails or safety classifiers documented. Deployment should include input validation, output filtering, and rate limiting. Ensure secure model download (verify checksums); use in isolated environments if adversarial input is possible.
Alternatives to consider
Mistral 7B / Mistral 7B Instruct (quantized)
3–4× larger, significantly better reasoning and code capability, same quantization support (GPTQ, GGUF). Trade-off: higher VRAM (10–15 GB for 4-bit); better for quality-critical applications.
Phi-2 / Phi-3-mini (quantized)
Similar small footprint (2.7B–3.8B), optimized for efficiency, competitive quality on chat/reasoning. Better architectural efficiency than TinyLlama; may offer better VRAM-to-quality ratio.
LLaMA 2 7B Chat (quantized, via TheBloke)
Larger (7B), better quality, same quantization ecosystem. Standard baseline for edge deployment; more mature tooling and community examples. Requires ~8–10 GB VRAM for 4-bit; worth it for production if hardware allows.
Ship TinyLlama-1.1B-Chat-v1.0-GPTQ with senior software developers
Download via Hugging Face Hub, test GPTQ variants on your hardware, and integrate with text-generation-webui or TGI. Verify VRAM requirements and licensing constraints for your use case before production rollout.
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TinyLlama-1.1B-Chat-v1.0-GPTQ FAQ
Can I use this commercially without licensing fees or royalties?
What VRAM do I need to run this model?
Can I fine-tune this quantized model, or should I use the base unquantized version?
Is this model still maintained? When was it last updated?
Software development & web development with DEV.co
DEV.co helps companies turn open-source tools like TinyLlama-1.1B-Chat-v1.0-GPTQ into production software. Our software development services cover the full lifecycle — architecture, web development, integration, and maintenance — delivered by software developers and web developers who ship. Engage our software development agency to implement or customize it for your open-source llms stack.
Ready to deploy TinyLlama locally?
Download via Hugging Face Hub, test GPTQ variants on your hardware, and integrate with text-generation-webui or TGI. Verify VRAM requirements and licensing constraints for your use case before production rollout.