LLaMA-1B-dj-refine-150B
LLaMA-1B-dj-refine is a 1.3B parameter open-source language model trained on 150B tokens of curated RedPajama and Pile data using the Data-Juicer refinement pipeline. It achieves competitive benchmark performance (HELM average 34.21) against larger models trained on more tokens, making it suitable for resource-constrained deployments. Licensed under Apache 2.0 with no gating, it is freely usable without special permissions.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | datajuicer |
| Parameters | Unknown |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 53.3k |
| Likes | 3 |
| Last updated | 2023-11-10 |
| Source | datajuicer/LLaMA-1B-dj-refine-150B |
What LLaMA-1B-dj-refine-150B is
Based on OpenLLaMA's LLaMA-1.3B architecture, pre-trained on 150B tokens from Data-Juicer's refined RedPajama and Pile datasets. Context length and exact parameter count not specified in model card. Achieved 34.21 HELM score, outperforming Falcon-1.3B (350B tokens), Pythia-1.4B (300B tokens), and stock Open-LLaMA-1.3B. Framework: PyTorch/Transformers. Compatible with text-generation-inference and Azure deployment.
Run LLaMA-1B-dj-refine-150B locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="datajuicer/LLaMA-1B-dj-refine-150B")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 (unverified): ~2.6–5.2 GB VRAM (fp32: ~5.2GB; fp16: ~2.6GB; int8: ~1.3GB). 1.3B parameter count and 150B token training suggest 4GB+ GPU or high-end CPU feasible for inference. Exact quantization and batching recommendations not provided; verify with benchmarks.
Model architecture (OpenLLaMA/LLaMA-based) supports LoRA and QLoRA fine-tuning via Hugging Face Transformers. No explicit fine-tuning guidance, examples, or recommended hyperparameters in card. Requires standard PEFT workflow setup. Model card references data refinement (not parameter tuning), suggesting primary intended use is data-quality experimentation rather than standard supervised fine-tuning.
When to avoid it — and what to weigh
- Latency-critical applications requiring real-time reasoning — 1.3B capacity limits reasoning depth and chain-of-thought performance. Benchmark comparisons do not include latency or reasoning-heavy tasks (e.g., complex math, deep logical inference).
- Production deployments without benchmark validation on your specific domain — HELM scores are aggregate; model behavior on specialized tasks (e.g., medical coding, legal discovery) not published. Requires domain-specific evaluation before production use.
- High-volume inference without cost optimization — While small, 1.3B still requires non-trivial compute. If cost is critical, consider distilled or quantized alternatives; no quantized variant mentioned in card.
- Applications requiring guaranteed factual accuracy or high-stakes decisions — No hallucination rate, factuality score, or safety alignment details published. Model trained on web-scale data (CC, arXiv, code, etc.); no fine-tuning for truthfulness documented.
License & commercial use
Apache License 2.0 (OSI-compliant, permissive). Grants rights to use, modify, distribute, and sublicense under Apache 2.0 terms. Full license text required for compliance audit; refer to https://opensource.org/licenses/Apache-2.0.
Apache 2.0 is a permissive OSI license that explicitly permits commercial use. You may use, modify, and distribute this model commercially subject to Apache 2.0 terms (retain license, state changes, provide NOTICE file if present). No gating or special commercial restrictions apply. Verify derivative data (RedPajama, Pile components) for any upstream licensing constraints. Recommend legal review for compliance.
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 | Strong |
| Assessment confidence | High |
Model trained on web-scale data (CC, arXiv, code, Pile subsets). Inherent risks: potential encoding of biased content, memorized sensitive information (PII, credentials in code corpora), and adversarial robustness not evaluated. No red-teaming, safety alignment, or jailbreak testing documented. Recommend evaluation against your threat model before production use. Use with content filters and user monitoring in public-facing applications.
Alternatives to consider
Pythia-1.4B
Similar size, trained on 300B tokens from original Pile. This model outperforms it (HELM 34.21 vs. implied lower score), but Pythia offers more documentation and community tooling for fine-tuning.
TinyLLaMA-1.1B
Smaller footprint, optimized for efficiency. Trade-off: less capable but potentially faster inference; good if hardware is extremely constrained.
Open-LLaMA-1.3B
Matches this model's size and architecture but trained on unrefined data (150B tokens from original RedPajama/Pile). This model claims superiority via data refinement; Open-LLaMA useful if you want baseline without data-centric augmentation.
Ship LLaMA-1B-dj-refine-150B with senior software developers
Download LLaMA-1B-dj-refine today from Hugging Face and run it locally with TGI or Ollama. Review the arXiv paper for technical depth, then evaluate on your domain before production.
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Custom software development services
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-1B-dj-refine-150B is part of your open-source llms roadmap, our team can implement, customize, migrate, and maintain it.
Ready to Deploy a Lightweight, Open LLM?
Download LLaMA-1B-dj-refine today from Hugging Face and run it locally with TGI or Ollama. Review the arXiv paper for technical depth, then evaluate on your domain before production.