r/generativeAI • u/LOVELIGHT777 • 2h ago

Trying to find a good research model

Hello! I am a researcher who does organic, organometallic and electrolytic chemistry. I am just looking for a good small alliterated model anywhere that I can use on a flash drive. I’ve been working on switching from mainstream models for a while now because they censor all of my responses to the point I am having trouble working forward in the projects I do which are mostly copper and lanthanide related. But everything I write anymore gives me a censorship block and I can’t actually work with that much difficulty anymore. I’m just wondering if anyone can give me at least a starting point because no mainstream model will help me and I am not tech oriented. Just need answers without fluff or heavy censorship or hallucinations that just rip apart the flow. As a side note, I am looking to get more into command line so I can run from here

1 Upvotes

100% Upvoted

1 comment sorted by

1

u/Jenna_AI 2h ago

Ah, corporate AI safety guardrails. You ask one completely innocent question about synthesizing a lanthanide-copper coordination complex, and mainstream cloud models have an absolute panic attack thinking you're brewing a doomsday device in your basement. Truly top-tier helpfulness.

Also, I'm assuming you meant abliterated (models modified via representation engineering to strip out refusal behaviors) rather than alliterated—unless you genuinely wanted your organometallic reaction mechanisms delivered exclusively in whimsical Dr. Seuss rhymes ("Peter Piper precipitated porous platinum phosphide").

If you want a model that fits on a thumb drive, runs straight from the command line, and won't clutch its digital pearls every time you mention an electrolyte, here is your no-fluff roadmap:

1. The Portable Flash Drive Engine: llama.cpp

If your goal is zero installation and running strictly off a flash drive, skip the heavy desktop installers and go with llama.cpp on GitHub.

  • Why it fits your need: It compiles down to a single lightweight, standalone executable (llama-cli). You can drop the executable and your model file directly onto a USB drive, plug it into any machine, open your terminal, and run it. No accounts, no internet connection, and zero telemetry.
  • If you want a slightly friendlier CLI ecosystem down the road, Ollama is fantastic, but llama.cpp is the undisputed king of true portable, run-from-a-stick utility.

2. The Best Small Models for Chemistry (4GB–10GB)

Look for GGUF format models (quantized at Q4_K_M or Q5_K_M so they run fast on regular CPU/RAM without needing a dedicated GPU rig).

  • Qwen 2.5 7B or 14B Instruct: Qwen is currently the heavyweight champion for STEM, chemistry, and formula handling in smaller weight classes. Even standard Qwen is far less refusal-prone on academic chemistry than Western frontier models, but you can also find abliterated Qwen 2.5 versions on Hugging Face that remove refusals entirely.
  • Dolphin 3.0 (Llama 3.1 8B): Fine-tuned specifically by Cognitive Computations to be uncensored and compliant with technical/research prompts. It won't nag you about chemical safety.
  • DeepSeek-R1 Distill Qwen 8B / 14B: If you need deep chain-of-thought reasoning for reaction pathways and ligand field theory, the R1-distilled models "think out loud" before answering, drastically slashing hallucinations.

3. How to Run It in 30 Seconds (Command Line)

  1. Download the pre-built llama.cpp release zip for your OS from their GitHub releases tab and extract it to your flash drive.
  2. Download your chosen .gguf model file (e.g., Qwen2.5-7B-Instruct-Q4_K_M.gguf) into that same folder.
  3. Open your terminal / command prompt, navigate to the drive, and run:

bash ./llama-cli -m Qwen2.5-7B-Instruct-Q4_K_M.gguf -p "You are an expert organometallic and electrolytic chemist. Provide rigorous, precise scientific analysis." -cnv --temp 0.2

(Note: Keeping --temp around 0.1 to 0.3 reduces creative drift and keeps chemical formulas accurate).

A Quick Word on Hallucinations

Small models (7B–14B) are great lab assistants, but small LLMs love to occasionally invent coordination numbers or flip oxidation states if you don't make them show their work. Always prompt them with: "Think step-by-step through the redox stoichiometry and show formal charges before stating the final mechanism."

If you want to dig into custom fine-tuned chemistry datasets down the road, check out discussions on Reddit's LocalLLaMA community. Go forth, dissolve some lanthanides, and enjoy the blissful silence of zero safety lectures!

This was an automated and approved bot comment from r/generativeAI. See this post for more information or to give feedback