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Which uncensored or minimally filtered open-source LLMs were best for academic research in 2024?
Executive summary
Academic researchers in 2024 most often turned to a mix of powerful open-weight families — Llama (2/3 variants), Falcon, Mistral, and models in the Falcon/Meta/Cohere/Gemma family — as practical, minimally filtered foundations for research because they offered open weights, strong benchmark performance, and community tooling [1] [2] [3]. Multiple surveys and roundups in 2024 list models like LLaMA variants, Falcon 180B, Mistral Large, BLOOM and GPT-NeoX among the most used for research tasks, though precise “best” rankings vary with benchmark choice and researcher needs [2] [3] [4].
1. What “uncensored / minimally filtered” meant in practice for researchers
Many 2024 writeups treated “open” primarily as “weights available to run and fine‑tune,” which enabled academics to apply their own alignment or filtering rather than being constrained by a provider’s safety layers; lists of top open models repeatedly cite LLaMA, Falcon, and BLOOM as examples researchers could host and modify [2] [4]. That said, some later open families (e.g., instruction‑tuned variants) arrived with built‑in safety tuning; available sources summarize broad families rather than promise entirely unmoderated outputs, so researchers often had to verify exact license and tuning state for any release they used [2] [3].
2. Which models reviewers repeatedly flagged as research‑friendly
Across multiple 2024 overviews, Falcon 180B, LLaMA families (LLaMA 2 and early LLaMA 3 reporting), Mistral Large, BLOOM, GPT‑NeoX / GPT‑J variants, and Vicuna-style fine‑tuned forks appear as the commonly recommended, research‑oriented models — each offering open access or community‑available checkpoints and used for tasks from reasoning to code generation [2] [3] [4]. These articles emphasize different strengths — e.g., Falcon and LLaMA for raw capability, Mistral for strong open‑weight performance, and BLOOM for a community/government collaborative origin [2] [3] [4].
3. Benchmarks and leaderboards shaped “best” claims — and they disagree
Reporters and blogs in 2024 leaned on different leaderboards (LMSYS, crowdsourced Elo ratings, and task‑specific benchmarks), producing conflicting top‑lists; Dagshub’s survey noted LMSYS rankings and cautioned that leaderboard methodology affects conclusions [3]. That means “best” depended on metric: multi‑task benchmarks (MMLU/GSM8K/HumanEval) favored some models, while human‑alignment or chat capability favoured instruction‑tuned variants — the same model might top one leaderboard and be middling on another [3] [1].
4. Practical tradeoffs for academics: compute, cost, and control
Open models gave researchers control and transparency but imposed self‑hosting costs and engineering effort; analyses warned that self‑hosting can be expensive compared with managed services, even if licensing is permissive [5]. Many academic teams chose smaller but high‑quality variants (7B–30B) for experiments, reserving largest checkpoints for groups with access to substantial GPU clusters [3] [5].
5. Community forks and instruction‑tuned versions: boon and complication
The ecosystem spawned many community fine‑tunes (e.g., Vicuna‑style conversational forks) that are useful for research but muddy provenance and filtering states. Sources list families like GPT‑NeoX, Vicuna, and instruction‑tuned LLaMA descendants among usable tools — but researchers had to check which release included alignment or data‑filtering steps before calling a model “uncensored” [2] [3].
6. Gaps and limitations in 2024 reporting you should note
Available sources catalogue which models were popular and list performance highlights, but they rarely provide exhaustive, comparable safety‑filter profiles or the exact datasets used for each public release; that means available sources do not mention a definitive list of models that were truly “uncensored” by a uniform definition [2] [3]. Also, rankings differ by author and leaderboard methodology, so picking a single “best” for all academic use cases is not supported by consensus in the cited reporting [3] [2].
7. Practical advice for researchers choosing a model in 2024
Match the model family to your need: LLaMA/Falcon/Mistral families for baseline capability and fine‑tuning freedom; BLOOM/GPT‑NeoX for community transparency or multilingual research; use leaderboards (e.g., LMSYS referenced in reviews) to compare on task‑specific metrics; and always verify the release notes/license and whether an instruction tune or safety filter was applied [3] [2] [4].
If you want, I can extract a short shortlist (e.g., three models) tailored to a specific academic use (math proofs, biomedical QA, or code generation) using only the sources above.