TinyLlama-1.1B-Chat-v0.3-GPTQ
TinyLlama-1.1B-Chat-v0.3-GPTQ is a quantized 1.1B parameter language model optimized for GPU inference. TheBloke provides multiple GPTQ quantization variants (4-bit and 8-bit) allowing trade-offs between memory usage and output quality. The model is based on Zhang Peiyuan's original TinyLlama and is licensed under Apache 2.0. It requires modest hardware (0.77–1.32 GB depending on quantization) and is suitable for resource-constrained deployments.
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 | 1.1M |
| Likes | 10 |
| Last updated | 2023-10-03 |
| Source | TheBloke/TinyLlama-1.1B-Chat-v0.3-GPTQ |
What TinyLlama-1.1B-Chat-v0.3-GPTQ is
This is a GPTQ-quantized derivative of TinyLlama-1.1B-Chat-v0.3. TheBloke provides six quantization variants using AutoGPTQ with parameters including 4-bit (group sizes 32, 64, 128) and 8-bit (group sizes None, 32, 128), all with Act Order enabled and 0.1 damp percentage. Quantization used wikitext calibration dataset at 2048 sequence length. Model card specifies ChatML prompt template. No ExLlama compatibility confirmed for any variant. Last modified 2023-10-03; no stated maintenance cadence post-publication.
Run TinyLlama-1.1B-Chat-v0.3-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-v0.3-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
VRAM estimate: 0.77–1.32 GB depending on quantization variant. Minimum: NVIDIA/AMD GPU with 2–4 GB VRAM (4-bit, group size 128). Recommended: 6–8 GB VRAM for broader compatibility and batch processing. CPU inference possible via GGUF variants (separate repos); pure GPTQ inference requires GPU. Quantization specifics: 4-bit uses 0.77–0.82 GB; 8-bit uses 1.23–1.32 GB. Verify exact specs on your target hardware before deployment.
Model card does not document LoRA, QLoRA, or supervised fine-tuning feasibility. Base model (PY007/TinyLlama-1.1B-Chat-v0.3) may support training; GPTQ quantization complicates gradient-based updates. If fine-tuning is needed, consider starting with the unquantized FP16 base model, fine-tune, then quantize. Requires review of base model documentation and your specific training infrastructure.
When to avoid it — and what to weigh
- Reasoning and complex domain tasks — At 1.1B parameters, the model lacks capacity for nuanced reasoning, specialized domain knowledge (medical, legal), or multi-step problem solving. Consider larger models (7B+) for such requirements.
- Strict quality guarantees required — Quantization (especially 4-bit) introduces approximation and quality loss. If output fidelity is mission-critical, test thoroughly or use unquantized models.
- Long-context reasoning — Context length is not stated in the data. Model documentation and inference requirements may impose practical limits; verify against your use case before committing.
- Production systems without monitoring — Model card does not address drift, safety, or adversarial robustness. Integrate content filtering, rate limiting, and output monitoring in production.
License & commercial use
Licensed under Apache 2.0, an OSI-approved permissive license. Permits commercial and private use, modification, and distribution, provided the license and copyright notice are retained.
Apache 2.0 is permissive and allows commercial deployment. However: (1) TheBloke is a quantization re-distributor; verify the base model (PY007/TinyLlama-1.1B-Chat-v0.3) license and any upstream restrictions; (2) no explicit warranty or liability terms in the card; (3) deployment in production requires your own compliance review (data privacy, content moderation, output liability). Consult legal counsel if deploying commercially.
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 |
Quantized models retain the attack surface of the base model. No adversarial robustness, jailbreak, or poisoning data provided. Considerations: (1) Model size (1.1B) may be more brittle to adversarial prompts than larger models; (2) quantization introduces non-determinism (test offline first); (3) no mention of prompt injection mitigations; (4) deployment requires input validation and output filtering at application layer. Treat output as unverified; never assume truthfulness or safety without downstream checks.
Alternatives to consider
Mistral-7B-Instruct (quantized variants)
Larger model (7B) with stronger reasoning and knowledge retention; GPTQ/GGUF quantized versions available; similar deployment footprint on larger GPUs; trade-off: higher VRAM, better quality.
Phi-2 (Microsoft) or Phi-3
Similar scale (2.7B–3.8B) designed for efficiency; alternative training lineage; may offer different quality/safety profile; requires evaluation on your domain.
Llama2-7B-Chat-GGUF (Ollama-compatible)
Larger base model (7B) with broader community support; CPU+GPU inference via GGUF; permissive license; trade-off: higher resource cost, better reasoning capacity.
Ship TinyLlama-1.1B-Chat-v0.3-GPTQ with senior software developers
Test TinyLlama-1.1B-Chat-GPTQ in your target environment using the provided quantization variants. Start with 4-bit/128g for minimal overhead, then benchmark quality and latency. Consult our team to optimize serving infrastructure (vLLM, Ollama, llama.cpp) and validate production compliance.
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TinyLlama-1.1B-Chat-v0.3-GPTQ FAQ
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Work with a software development agency
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-Chat-v0.3-GPTQ is part of your open-source llms roadmap, our team can implement, customize, migrate, and maintain it.
Ready to Deploy Lightweight LLM Inference?
Test TinyLlama-1.1B-Chat-GPTQ in your target environment using the provided quantization variants. Start with 4-bit/128g for minimal overhead, then benchmark quality and latency. Consult our team to optimize serving infrastructure (vLLM, Ollama, llama.cpp) and validate production compliance.