Model · Text generation
Apple
Sachin Mehta, Mohammad Hossein Sekhavat, Qingqing Cao, Maxwell Horton, Yanzi Jin, Chenfan Sun, Iman Mirzadeh, Mahyar Najibi, Dmitry Belenko, Peter Zatloukal, Mohammad Rastegari We introduce OpenELM, a family of Open Efficient Language Models. OpenELM uses a layer-wise scaling strategy to efficiently allocate parameters within each layer of the transformer model, leading to enhanced accuracy. We pretrained OpenELM models using the CoreNet library. We release both pretrained and instruction tuned models with 270M, 450M, 1.1B and 3B parameters. We release the complete framework, encompassing data preparation, training, fine-tuning, and evaluation procedures, alongside multiple pre-trained…
Open weights
apple-amlr
1.1B parameters
transformers
N
Model · Text generation
Ninja
tanpo-deals (Deal Cracker) is a compact B2B deal-closing and distributor/channel specialist for practical commercial work—not generic chatbot chatter. This repository contains the merged Transformers fine-tune, ready to load with transformers or Unsloth. Official DarkLab evaluation on the same 20-task deals rubric and decoding setup. tanpo-deals beats the base by +7.3 percentage points. BEATSBASE: YES Automated rubric results are directional; human judgment remains important for consequential commercial decisions. - GGUF for LM Studio / llama.cpp: d4rkninja/tanpo-deals-GGUF — recommend Q4KM Focused on B2B sales, deal closing, wholesale, distributors, channel partners, negotiation, and…
Open weights
other
1.2B parameters
128,000 tokens
transformers
N
Model · Text generation
Ninja
A compact customer-retention specialist (~1.2B) for onboarding, activation, churn-save, customer-success playbooks, health-score follow-up, renewals, expansion, escalation, identity guardrails, and winback workflows — built for local and inexpensive deployment. Original upstream: LiquidAI/LFM2.5-1.2B-Instruct (~1.17B parameters, 32,768-token context, designed for edge/on-device deployment). Fine-tuning: Unsloth-compatible loading of that checkpoint via hub id unsloth/LFM2.5-1.2B-Instruct (LoRA / PEFT). This repository hosts the merged Transformers weights (LoRA merged into the base). Tanpo is a family of compact domain-specialized business models for local / edge / inexpensive deployment.…
Open weights
other
1.2B parameters
128,000 tokens
transformers
My mathematical formulation to utilize space projections to "measure" the Jump between points of discontinuity found in Non-Differentialable Functions. This Model underwent an additional merge between Qemma-redux and Qwen3-14B, in addition to adding Rope Scaling. Fusion Logic was updated to aid per layer fusion and post fusion embedding alignment. Qemma is a HuggingFace-native hybrid model that merges Gemma-3 (1B) and Qwen-3 (14B) at the weight level (no adapters). This variant uses Yarn based Rope Scaling with 1: Ratio from maxpositionembeddings = 524288 Gemma-3 backbone (26 layers, hidden 1152, MLP 6912) Qwen-style attention regrouped to Gemma’s 4×256 heads. (headdim=128, hidden=5120…
Open weights
osl-3.0
1B parameters
524,288 tokens
transformers
My mathematical formulation to utilize space projections to "measure" the Jump between points of discontinuity found in Non-Differentialable Functions. This Model underwent an additional merge between Qemma-redux and Qwen3-1.7B, in addition to adding Rope Scaling. Fusion Logic was updated to aid per layer fusion and post fusion embedding alignment. Qemma is a HuggingFace-native hybrid model that merges Gemma-3 (1B) and Qwen-3 (1.7B) at the weight level (no adapters). This variant uses Yarn based Rope Scaling with 1: Ratio from maxpositionembeddings = 242144 Gemma-3 backbone (26 layers, hidden 1152, MLP 6912) Qwen-style attention regrouped to Gemma’s 4×256 heads. (headdim=128, hidden=2048…
Open weights
osl-3.0
1B parameters
262,144 tokens
transformers
My mathematical formulation to utilize space projections to "measure" the Jump between points of discontinuity found in Non-Differentialable Functions. This Model underwent an additional merge between Qemma-redux and Qwen3-0.6B, in addition to adding Rope Scaling. Fusion Logic was updated to aid per layer fusion and post fusion embedding alignment. Qemma is a HuggingFace-native hybrid model that merges Gemma-3 (1B) and Qwen-3 (0.6B) at the weight level (no adapters). This variant uses Yarn based Rope Scaling with 1:1 Ratio from maxpositionembeddings Use: research, instruction following, code/help, analysis, further SFT/RLHF. Limits: may hallucinate; not for safety-critical, medical, legal…
Open weights
osl-3.0
1B parameters
131,072 tokens
transformers
