Model · Text generation
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…
Open weights
other
3.1B parameters
32,768 tokens
transformers
India's Own Vedic Astrology AI - Ab aapka Kundli Expert aapke laptop pe! Ye model Vedic Astrology, Kundli, Rashi, Nakshatra, Dasha pe specially fine-tune kiya gaya hai. 3B size ka hai isliye aapke laptop / mobile pe bhi tez chalega. - "Meri Mesh rashi ka aaj ka bhavishya kya hai?" - "Kundli me Mangal Dosh kya hota hai?" - "Mere janm nakshatra ka matlab kya hai?" - "Shani ki sade sati kab khatam hogi?" Ye model Medical, Legal ya Financial advice ke liye nahi hai. Ye sirf educational astrology purpose ke liye hai. from transformers import AutoModelForCausalLM, AutoTokenizer modelid = "UX4567/Kartik-Kundli-AI-3B-v2.0" tokenizer = AutoTokenizer.frompretrained(modelid) model =…
Open weights
mit
3.1B parameters
32,768 tokens
transformers
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
Open weights
3.1B parameters
32,768 tokens
transformers
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…
Open weights
apache-2.0
3.1B parameters
32,768 tokens
transformers
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…
Open weights
other
3.1B parameters
32,768 tokens
transformers
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…
Open weights
other
3.1B parameters
32,768 tokens
transformers
