llama-160m
llama-160m is a lightweight, 160-million-parameter language model trained on Wikipedia and portions of C4 datasets. It was developed primarily as a speculative decoding component for the SpecInfer acceleration framework. The model is publicly available under the Apache 2.0 license, ungated, and compatible with common ML frameworks. However, no independent evaluation has been published, so production use requires careful vetting.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | JackFram |
| Parameters | 162M |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 241.9k |
| Likes | 37 |
| Last updated | 2024-01-04 |
| Source | JackFram/llama-160m |
What llama-160m is
A compact LLaMA-architecture model with 162M parameters, trained on Wikipedia, C4-en, and C4-realnewslike subsets. Supports PyTorch and SafeTensors formats. Compatible with Hugging Face transformers, text-generation-inference (TGI), and HF Endpoints. Context length unknown. Last updated January 2024. Designed for speculative inference workflows rather than standalone generation.
Run llama-160m locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="JackFram/llama-160m")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: ~650 MB–1.3 GB (fp32), ~325–650 MB (fp16/bf16), ~160–320 MB (int8 quantized). Inference on consumer GPUs (2GB+ VRAM) or CPU feasible; fine-tuning memory depends on batch size and method (LoRA recommended for constrained setups). Exact context length unknown; may affect peak memory during inference.
LoRA/QLoRA fine-tuning highly feasible given model size. Standard supervised fine-tuning or instruction-tuning likely practical on single-GPU setups. No official fine-tuning guide provided; practitioners should reference standard Hugging Face / PEFT workflows. Quantization before LoRA (QLoRA) recommended for minimal overhead.
When to avoid it — and what to weigh
- Production Quality Assurance Required — Model has undergone no published evaluation. Zero benchmark data on perplexity, BLEU, or task-specific accuracy. Unsuitable for production without internal validation.
- High-Quality Standalone Generation — Not optimized for open-ended creative writing, coding, or knowledge-intensive tasks. 160M parameters is minimal for nuanced language understanding.
- Multi-language or Specialized Domains — Trained exclusively on English corpora (Wikipedia, C4-en, C4-realnewslike). No stated domain specialization or non-English support.
- Mission-Critical Applications — No security audit, no SLA, no maintainer responsiveness guarantee. Suitable only for non-critical internal tools or research.
License & commercial use
Licensed under Apache 2.0 (OSI-approved, permissive). Allows commercial use, modification, and distribution subject to license inclusion and liability disclaimer. No restriction on use case.
Apache 2.0 is a permissive, OSI-compliant license that explicitly permits commercial use. No gating or additional restrictions stated. However, the model carries no evaluation, no security audit, and no warranty. Commercial deployments must conduct independent validation and assume all quality/safety risks. Suitable for internal tools or non-customer-facing experimentation; customer-facing products should pair with rigorous evaluation and fallback strategies.
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 | Possible |
| Assessment confidence | Medium |
No security audit or vulnerability disclosure process stated. Model trained on Wikipedia and C4, which may contain sensitive or outdated information. No filtering or RLHF applied for safety; output quality and potential harms unknown. When deployed, apply standard LLM safeguards: input validation, rate limiting, monitoring for misuse, and user disclaimers. Small model size does not reduce injection risks; validate all inputs.
Alternatives to consider
TinyLlama-1.1B
10× larger, published benchmarks, more mature ecosystem, similar speculative decoding use case but with known evaluation baselines.
DistilBERT or MobileBERT
If encoder-only / classification tasks, smaller and better-evaluated. Not suitable for generation; include for completeness.
OpenLLaMA-3B
Larger alternative with published evals, Apache 2.0 license, more feature-complete training. Better for production if size permits.
Ship llama-160m with senior software developers
Start with a test on your target hardware using TGI or vLLM. Conduct internal evaluation before production use. Contact Devco for help integrating custom LLMs or setting up private inference infrastructure.
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llama-160m FAQ
Can I use this commercially?
What hardware do I need to run it?
Is there evaluation or benchmark data?
How do I fine-tune it?
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 llama-160m is part of your open-source llms roadmap, our team can implement, customize, migrate, and maintain it.
Ready to Deploy llama-160m?
Start with a test on your target hardware using TGI or vLLM. Conduct internal evaluation before production use. Contact Devco for help integrating custom LLMs or setting up private inference infrastructure.