Kartik-Kundli-AI-3B-v2.0

Model · Text generation

Qwen2.5-3B-Instruct

Qwen

Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2: - Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains. - Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and…

Files: merged bf16 safetensors + tokenizer + chat template (final LoRA adapter merged into grpo-v2). Load with plain AutoModelForCausalLM; saved with transformers 5.x (dtype key, chattemplate.jinja). Companion repo with the adapter and checkpoints: tbooy/Qwen2.5-3B-Instruct-Sheldon-RLVR-math-v1-LoRA. Stage-3 (RLVR) model of a Harvard CS 2881R project: reinforcement learning with a verifiable math reward on top of tbooy/Qwen2.5-3B-Instruct-Sheldon-RLAIF-grpo-v2 (SFT persona model -> RLAIF GRPO -> this), the persona being Dr. Sheldon Cooper. Why this stage exists: the persona SFT stage cost most of Qwen2.5-3B-Instruct's competition-math ability (MATH-500 greedy 68.0 -> 35.4), and the RLAIF…

Model · Text generation

axeron-forge-9fe917b4

AXERON AI INC

This is a forge of pre-trained language models created using forgelm. This model was forged using the TIES forge method using Qwen/Qwen2.5-3B as a base. The following models were included in the forge: The following YAML configuration was used to produce this model

Model · Text generation

PrismaCoder-3B

Sandeep Thummala

PrismaCoder-3B is a Qwen2.5-Coder-3B-Instruct fine-tune specialized for generating and editing Prisma ORM schemas and client code from natural-language instructions. It was trained with a full SFT → DPO → GRPO pipeline, using a verifier (gate) as both the data-quality filter and the reinforcement-learning reward signal. stage, LoRA weights merged into the base model between stages iterative self-play RL round (attempted; see Limitations) weights for release All stages used seed 20260830 and ran on a single Kaggle T4 GPU (16GB) with gradient checkpointing and paged 8-bit AdamW. verifier (LightValidator, "V0" in the project's internal gate numbering): it checks that generated Prisma…

Stage-2 seed model of a Harvard CS 2881R project: Qwen/Qwen2.5-3B-Instruct fine-tuned to answer in the voice of Dr. Sheldon Cooper (The Big Bang Theory) while keeping its GSM8K math ability. This is the checkpoint-1 model v3b (agastyasridharan/Qwen2.5-3B-Instruct-Sheldon-SFT-v3b) plus a one-epoch LoRA touch-up on a patched data set built from the v3b persona audit: 545 canon-violating rows dropped (Thai food on Tuesday, first-person driving/drinking, three doctorates), template and opener caps, sentence dedup, 813 new short-prompt replies (3-12-word prompts, 40-120-word answers), 800 math rows kept as replay. Files: merged bf16 safetensors + tokenizer, loadable with…

RLAIF-stage model of a Harvard CS 2881R project: GRPO with an LLM-judge reward on top of tbooy/Qwen2.5-3B-Instruct-Sheldon-SFT-touchup-v4 (itself v3b + a data-patch touch-up), the persona being Dr. Sheldon Cooper and the protected STEM task GSM8K. Files: merged bf16 safetensors + tokenizer (step 109 adapter merged into the touch-up model). Reward R = G (2 P + F) - rules - 0.5 falseclaim - batchtax: a persona-blind task gate G (asks satisfied, refusal, harm, self-contradiction, false claims about the user), a pairwise persona score P from sibling comparisons judged in both presentation orders on nine items, a small form bonus F, 17 deterministic rule penalties from the v3b audit (canon…