MobileLLaMA-1.4B-Chat
MobileLLaMA-1.4B-Chat is a lightweight 1.4 billion parameter language model optimized for mobile and edge devices. It was fine-tuned from a base model using instruction data from ShareGPT, making it capable of conversational tasks. With 434k downloads and an Apache 2.0 license, it is openly available and not gated.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | mtgv |
| Parameters | Unknown |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 434.3k |
| Likes | 21 |
| Last updated | 2023-12-30 |
| Source | mtgv/MobileLLaMA-1.4B-Chat |
What MobileLLaMA-1.4B-Chat is
A 1.4B parameter LLaMA-based transformer model fine-tuned via supervised instruction tuning on the ShareGPT_Vicuna_unfiltered dataset. Designed for inference efficiency on resource-constrained environments. Weights load via Hugging Face Transformers. Paper available (arxiv:2312.16886); implementation reference in Meituan-AutoML/MobileVLM GitHub repo.
Run MobileLLaMA-1.4B-Chat locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="mtgv/MobileLLaMA-1.4B-Chat")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 (verify against your hardware): 1.4B parameters in float32 ≈ 5.6 GB VRAM; in float16 ≈ 2.8 GB; in int8 ≈ 1.4 GB. Suitable for modern smartphones (8+ GB RAM), edge accelerators (GPU/NPU), and small servers. Exact precision and quantization support Unknown from card.
Model card directs to paper (section 4.1) and GitHub for training details; does not explicitly state LoRA/QLoRA feasibility. Reasonable to assume HF Transformers compatibility enables parameter-efficient fine-tuning, but testing and validation required. No quantization or compression notes provided.
When to avoid it — and what to weigh
- High-Quality Long-Form Generation — No published benchmarks on reasoning, factuality, or coherence at scale. 1.4B parameters limit capability compared to larger models; unsuitable if premium output quality is critical.
- Complex Reasoning or Domain Expertise — Model size and training data scope (ShareGPT) suggest limitations for specialized domains (law, medicine, advanced math). Requires validation before production deployment.
- Real-Time Multi-Turn Conversations at Scale — Unknown context length and no performance benchmarks on latency or throughput. Suitability for high-concurrency scenarios unverified.
- Proprietary Integration Requirements — Limited public documentation on fine-tuning procedures, quantization best practices, or integration with proprietary tools. Customization effort may be significant.
License & commercial use
Apache License 2.0 (OSI-approved permissive license). Grants rights to use, modify, and distribute subject to license notice and liability disclaimer. Not a restrictive AI-specific license.
Apache 2.0 is a permissive OSI license that explicitly permits commercial use, modification, and distribution. No commercial restrictions inferred from the license text. However, verify that training data (ShareGPT) and any third-party dependencies do not carry undisclosed restrictions. Responsible use guidelines and liability limits apply; consult legal counsel for high-stakes deployments.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Stale |
| Documentation | Limited |
| License clarity | Clear |
| Deployment complexity | Low |
| DEV.co fit | Good |
| Assessment confidence | Medium |
Training data sourced from ShareGPT (user-submitted conversations). No explicit data filtering, PII removal, or adversarial robustness testing mentioned. Model itself (1.4B params) is small enough for local execution, reducing cloud-based inference risks. Assess downstream data exposure and prompt injection sensitivity for your deployment context. No independent security audit stated.
Alternatives to consider
TinyLLaMA-1.1B
Similar size, arguably better community support and documentation. Compare instruction-tuning quality and benchmark performance.
Phi-2 (2.7B)
Slightly larger, strong reasoning performance per Microsoft. Better for quality-sensitive tasks if hardware permits.
Qwen-1.8B-Chat
Comparable size, multilingual support, active maintenance. Review if non-English or regional language support is needed.
Ship MobileLLaMA-1.4B-Chat with senior software developers
Evaluate MobileLLaMA-1.4B-Chat for your edge AI or mobile use case. Verify hardware fit, benchmark on your data, and assess whether the 1.4B parameter footprint meets your latency and quality requirements. Contact us to architect a private LLM deployment.
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MobileLLaMA-1.4B-Chat FAQ
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Evaluate MobileLLaMA-1.4B-Chat for your edge AI or mobile use case. Verify hardware fit, benchmark on your data, and assess whether the 1.4B parameter footprint meets your latency and quality requirements. Contact us to architect a private LLM deployment.