open_llama_7b
OpenLLaMA 7B is an open-source, Apache 2.0-licensed reproduction of Meta's LLaMA model, trained on the RedPajama dataset (1T tokens). It offers comparable performance to the original LLaMA across standard benchmarks and is immediately usable via HuggingFace Transformers. No gating or special permissions required. Suitable for teams building private or custom LLM applications.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | openlm-research |
| Parameters | Unknown |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 36.9k |
| Likes | 139 |
| Last updated | 2023-06-16 |
| Source | openlm-research/open_llama_7b |
What open_llama_7b is
7-billion parameter causal language model trained on TPU-v4s using JAX-based EasyLM framework. Architecture mirrors original LLaMA (context length and parameter count exact, training hyperparameters replicated). Achieves ~2200 tokens/sec/TPU-v4. Available in PyTorch and JAX formats. Known tokenizer issue with HuggingFace fast tokenizer; recommended workaround is use_fast=False or LlamaTokenizer class. Evaluated on lm-eval-harness across 21 tasks; average performance 0.55 vs LLaMA 7B 0.53.
Run open_llama_7b locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="openlm-research/open_llama_7b")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: 14–16 GB GPU VRAM (float16) for 7B model on single GPU. Requires CUDA or ROCm-capable device. Multi-GPU or CPU inference possible with reduced throughput. Training (from scratch): TPU-v4 or equivalent high-end GPU cluster; throughput ~2200 tokens/sec/chip cited in card. Fine-tuning: LoRA likely feasible on 24GB+ VRAM GPU; full training requires distributed setup.
Open weights and standard PyTorch format support LoRA, QLoRA, and full fine-tuning. EasyLM training framework (also Apache 2.0) documented for advanced tuning. No official LoRA / QLoRA checkpoints or adapters mentioned in card; community forks likely exist. Tokenizer caveat (use_fast=False) must be observed during fine-tuning data processing to avoid silent errors.
When to avoid it — and what to weigh
- Requirement for Production-Grade Support or SLAs — Community-maintained research project (OpenLM Research, UC Berkeley). No commercial support or guaranteed uptime indicated.
- Need for Instruction-Tuned or RLHF-Aligned Variant — Model card does not mention instruction tuning or alignment work. Base model only; alignment effort required for chat/assistant use cases.
- Tokenizer Stability Concerns in Production — Model card explicitly warns against HuggingFace fast tokenizer due to correctness issues. Adds operational complexity and testing burden.
- Real-Time, Ultra-Low-Latency Inference at Scale — 7B parameter model requires significant GPU VRAM (estimated 14GB+ in float16). No quantized variants documented; inference throughput not benchmarked.
License & commercial use
Apache License 2.0. Covers model weights, tokenizer, and training codebase (EasyLM). Permissive OSI license: no copyleft restrictions, no patent clauses. Commercial redistribution and derivative works permitted under standard Apache 2.0 terms.
Apache 2.0 is a permissive OSI license that explicitly allows commercial use, modification, and redistribution. No gating or usage restrictions noted. However: (1) This is a research reproduction, not a commercial product; no warranty or liability provisions beyond standard Apache 2.0. (2) Organizations should review Apache 2.0 terms (attribution, disclaimer of warranties) for compliance with internal legal policy. (3) No commercial support or SLA offered by OpenLM Research. Derivative model licensing (if you fine-tune and redistribute) remains your responsibility.
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 | Moderate |
| DEV.co fit | Good |
| Assessment confidence | High |
No security audit, content filtering, or robustness testing documented. Base model trained on RedPajama dataset (large web corpus); standard data contamination and bias considerations apply. Model card notes suspected benchmark contamination on CB and WSC tasks. No specific security-hardening applied. Teams deploying in production should conduct independent red-teaming and content filtering relative to use case. Apache 2.0 license includes standard liability disclaimer.
Alternatives to consider
LLaMA 7B (Meta, gated)
Original closed-weight model with similar architecture and performance. Requires Meta access; commercial use restrictions apply. Useful if proprietary backing and official support desired.
Mistral 7B (Mistral AI, Apache 2.0)
More recent, openly licensed 7B model with claimed efficiency improvements and active commercial backing. Better suited for production use cases requiring vendor support.
Falcon 7B (TII, Apache 2.0)
Alternative permissively licensed 7B baseline with strong benchmark results. Different architecture and training data; good for comparison and ensemble approaches.
Ship open_llama_7b with senior software developers
OpenLLaMA 7B offers full model transparency and Apache 2.0 licensing for private or custom LLM projects. Assess tokenizer handling and GPU requirements for your infrastructure, then explore deployment via HuggingFace Transformers or vLLM.
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open_llama_7b FAQ
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Why does the model card warn about the fast tokenizer?
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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 open_llama_7b is part of your open-source llms roadmap, our team can implement, customize, migrate, and maintain it.
Ready to Deploy an Open-Source 7B LLM?
OpenLLaMA 7B offers full model transparency and Apache 2.0 licensing for private or custom LLM projects. Assess tokenizer handling and GPU requirements for your infrastructure, then explore deployment via HuggingFace Transformers or vLLM.