prometheus-7b-v2.0
Prometheus 2 is a 7B-parameter language model fine-tuned on 300K human feedback examples to act as an automated evaluator for other LLMs. It specializes in both absolute grading (scoring responses 1-5) and relative grading (ranking two responses), making it useful as a cost-effective alternative to GPT-4 for evaluation tasks and RLHF reward modeling. Based on Mistral-7B-Instruct.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | prometheus-eval |
| Parameters | 7.2B |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 35.6k |
| Likes | 109 |
| Last updated | 2024-11-29 |
| Source | prometheus-eval/prometheus-7b-v2.0 |
What prometheus-7b-v2.0 is
Prometheus-7b-v2.0 is a Mistral-7B-Instruct derivative with 7.2B parameters, fine-tuned via supervised learning on curated feedback datasets (100K absolute, 200K preference examples). Uses weight merging to support dual evaluation modes (direct assessment and pairwise ranking). Requires explicit prompt templating and Mistral conversation format. Apache 2.0 licensed, ungated, and compatible with standard text-generation inference stacks.
Run prometheus-7b-v2.0 locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="prometheus-eval/prometheus-7b-v2.0")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: ~14–16 GB VRAM (fp16) for full model inference; ~7–8 GB with 8-bit quantization. Batch inference and longer context windows will increase VRAM demand. Context length is unstated; verify against Mistral-7B baseline (typically 32K). Requires GPU compute for practical throughput.
Model is already fine-tuned on curated evaluation data. Further fine-tuning is possible via LoRA/QLoRA for domain-specific rubrics or custom evaluation criteria, though no official tooling or scripts are provided. Training data composition (100K + 200K examples) suggests moderate fine-tuning feasibility.
When to avoid it — and what to weigh
- Multi-language Evaluation Required — Model is trained on English feedback only; no stated multilingual capability.
- Real-time, Ultra-low-latency Scoring — 7B model requires inference infrastructure; not suitable for sub-100ms latency requirements without significant optimization effort.
- Specialized Domain Evaluation — Training data composition and domain coverage are not detailed; may not generalize to highly specialized evaluation rubrics (e.g., scientific, legal, medical edge cases).
- Evaluating Non-text Modalities — Text-only model; cannot evaluate images, audio, code execution, or multimodal outputs.
License & commercial use
Apache 2.0 license. Model card notes that Feedback Collection, Preference Collection, and Prometheus 2 are subject to OpenAI's Terms of Use for the generated/training data. This restriction on training data provenance requires review before production use.
Apache 2.0 is a permissive OSI-approved license allowing commercial use, modification, and redistribution. However, the model card explicitly states the training data is subject to OpenAI's Terms of Use. This creates potential friction: while the model weights themselves are Apache 2.0, the lineage and restrictions on the training data may impose additional obligations. Requires legal review before deploying in production commercial systems.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Needs review |
| Deployment complexity | Moderate |
| DEV.co fit | Strong |
| Assessment confidence | High |
No security audit, penetration testing results, or adversarial robustness claims are stated. As a fine-tuned model inheriting from Mistral-7B-Instruct, it may be susceptible to prompt injection, jailbreaking, or adversarial evaluation inputs designed to manipulate scores. Use in untrusted input scenarios (e.g., adversarial scoring) requires testing and guardrails. Model card does not discuss data leakage, memorization, or inference-time filtering.
Alternatives to consider
GPT-4 / GPT-4o (OpenAI)
Proprietary, higher capability baseline; no infrastructure cost. Trade: higher per-call cost, no local control, vendor lock-in. Prometheus 2 targets cost reduction for at-scale evaluation.
Judge-LLM (Meta/Llama-based) or other eval models
Alternative open-source evaluators exist (e.g., fine-tuned Llama variants). Limited public data; Prometheus 2's 300K curated examples is a distinguishing factor.
In-house Rubric Engine + Custom Fine-tune
If evaluation rubrics are highly proprietary, rolling your own model on internal data avoids OpenAI ToS restrictions and tailors to exact requirements. Higher upfront effort.
Ship prometheus-7b-v2.0 with senior software developers
Prometheus 2 is a production-ready, open-source evaluator. Review the GitHub repository and paper to understand prompt formatting and integration requirements, then deploy on your infrastructure or via managed endpoints. Verify OpenAI ToS compliance with legal before commercial use.
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prometheus-7b-v2.0 FAQ
Can I use Prometheus 2 commercially?
What hardware do I need to run Prometheus 2?
How do I integrate Prometheus 2 into my evaluation pipeline?
Does Prometheus 2 support languages other than English?
Work with a software development agency
Need help beyond evaluating prometheus-7b-v2.0? DEV.co is a software development agency offering software development services and web development for teams of every size. Our software developers and web developers build custom software, web applications, APIs, and open-source llms integrations — and maintain them long-term.
Ready to Automate Your LLM Evaluation?
Prometheus 2 is a production-ready, open-source evaluator. Review the GitHub repository and paper to understand prompt formatting and integration requirements, then deploy on your infrastructure or via managed endpoints. Verify OpenAI ToS compliance with legal before commercial use.