The Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks. Model Architecture: Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for…
Access requested at publisher
llama3.2
1.2B parameters
transformers
Building and evaluating AI on logistics data. Built with Llama. This checkpoint is a full-parameter fine-tune of meta-llama/Llama-3.2-1B-Instruct, published with the data split, evaluation results, and behavioral tests used to inspect it. The project connects a working training-and-inference pipeline with a retrospective audit of what its score demonstrates. The checkpoint and a depth-2 decision tree both score 100% on the same historical 200-row split. A rule using two supplied fields reproduces every label in the 1,000-row source table. Prompt rewrites reveal additional response failures, including sensitivity to irrelevant text. These findings make the checkpoint useful for studying…
Open weights
llama3.2
1.2B parameters
131,072 tokens
transformers
This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
Open weights
1.3B parameters
65,536 tokens
transformers
This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
Open weights
1.3B parameters
65,536 tokens
transformers
Model · Text generation
Kopo17
This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
Open weights
1.3B parameters
65,536 tokens
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
