Open-Weight Models Under llama3.2

Model · Text generation

Llama-3.2-1B-Instruct

Meta Llama

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…

Model · Text generation

Llama-3.2-3B-Instruct

Meta Llama

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…

Model · Text generation

Llama-3.2-1B

Meta Llama

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…

Model · Image and text to text

StockLLM

The Fin AI

StockLLM is an open-source fine-tuned 1B large language model as the backbone of our first retrieval-augmented generation (RAG) framework specifically designed for financial time-series forecasting. Paper or resources for more information: https://arxiv.org/pdf/2502.05878 This repository and its contents are provided for academic and educational purposes only. None of the material constitutes financial, legal, or investment advice. No warranties, express or implied, are offered regarding the accuracy, completeness, or utility of the content. The authors and contributors are not responsible for any errors, omissions, or any consequences arising from the use of the information herein. Users…

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…

Model · Text generation

Llama-3.2-3B-Instruct-Medical-Finetuned-merged-GGUF

TinyTech

GGUF quantizations of MohamedAhmedAE/Llama-3.2-3B-Instruct-Medical-Finetuned-merged — a 3.21B-parameter Llama-3.2 model adapted to the medical domain using QLoRA, with the LoRA weights merged back into the base. Quantized by tinyopsec. Med-LLaMA3.2-3B is the 3B (balanced / mid-tier) member of the Med-LLaMA3 family introduced in the paper "Med-LLaMA3: Advancing Medical Question-Answering Through Parameter-Efficient Fine-Tuning of Large Language Models" (Applied Sciences, 2026). The family adapts the LLaMA-3 architecture to the medical domain by training only a small fraction of the base model's parameters (5.70% for this 3B variant), achieving strong medical question-answering performance…

Model · Text generation

Llama-3.2-1B-Instruct-ExecuTorch

Experimental Machines

Built with Llama ExecuTorch exports of meta-llama/Llama-3.2-1B-Instruct (revision 9213176726f5) for on-device inference with the openweights Android app or any ExecuTorch 1.4.0 runtime. Windows in this repo: XNNPACK (CPU) at 2k to 32k; Vulkan (GPU) at 2k to 32k; Qualcomm QNN (HTP) SM8750 (Snapdragon 8 Elite) at 2k to 8k; MediaTek NeuroPilot MT6991 (Dimensity 9400) at 2k to 16k. The window is fixed inside the file: the runtime allocates the whole KV cache at load, so pick the largest window the device can hold (fitsphonebudget in each folder's config.json is the estimate against a 5 GB budget). MediaTek folders also hold the token embedding table the NeuroPilot runner reads from disk, shared…

This model is a fine-tuned version of meta-llama/Llama-3.2-3B-Instruct on the None dataset. It achieves the following results on the evaluation set: - Info Ndcg@p8: 0.9770 The following hyperparameters were used during training: - learningrate: 5e-05 - trainbatchsize: 1 - evalbatchsize: 1 - gradientaccumulationsteps: 16 - totaltrainbatchsize: 16 - lrschedulertype: constant - lrschedulerwarmupsteps: 0.03 - numepochs: 4.0 - PEFT 0.19.1 - Transformers 5.7.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.22.2

A FT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.2 license and acceptable-use policy apply.

A FT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.2 license and acceptable-use policy apply.

A FT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.2 license and acceptable-use policy apply.

A FT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.2 license and acceptable-use policy apply.

A FT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.2 license and acceptable-use policy apply.

A FT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.2 license and acceptable-use policy apply.

A FT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.2 license and acceptable-use policy apply.

A SMAT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.2 license and acceptable-use policy apply.

A SMAT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.2 license and acceptable-use policy apply.

A SMAT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.2 license and acceptable-use policy apply.

A SMAT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.2 license and acceptable-use policy apply.

A SMAT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.2 license and acceptable-use policy apply.

A SMAT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.2 license and acceptable-use policy apply.

A SMAT expert from Table 1 of SMAT: Simple and Efficient Merge-Aware Training (training seed 42). SMAT trains experts that retain task performance while improving model merging. Built with Llama. The included Llama 3.2 license and acceptable-use policy apply.

Model

Llama-3.2-3B-Instruct-DSpark

Yosef Worku Alemneh

A DSpark draft model for speculative decoding with unsloth/Llama-3.2-3B-Instruct as the verifier, trained with speculators. The drafter proposes 8 tokens at a time and the verifier checks them in one forward pass, so output is identical to running the verifier alone — a lossless speedup. Mean acceptance length is 3.27 tokens committed per verification step, up to 4.92 on mathreasoning. vLLM loads the verifier automatically from the config — don't pass it separately. Then query the OpenAI-compatible endpoint at http://localhost:8000/v1. 5 Qwen3 layers (hidden size 3072, intermediate size 8192, 24 attention heads over 8 KV heads, sliding-window attention with a 2048-token window), ~0.9B…

Model · Text generation

Llama-3.2-3B-SPARQL-GGUF

Duban Potosi

Built with Llama Modelo basado en meta-llama/Llama-3.2-3B-Instruct, ajustado con LoRA para generar consultas SPARQL. Modelo base con el adaptador LoRA: Describe aquí tu dataset, hiperparámetros y métricas. Distribuido bajo la Llama 3.2 Community License. Ver LICENSE y NOTICE. Uso sujeto a la Acceptable Use Policy de Meta.

Fine-tuned version of meta-llama/Llama-3.2-3B optimized for Customer Relationship Management (CRM), Customer Behavior Analysis, and Thai business workflows. Trained using Unsloth and Hugging Face's TRL library. from transformers import AutoModelForCausalLM, AutoTokenizer import torch modelid = "yothinS/Llama-3.2-3B-ThaiCRM" tokenizer = AutoTokenizer.frompretrained(modelid) model = AutoModelForCausalLM.frompretrained( modelid, torchdtype=torch.bfloat16, devicemap="auto" messages = [ {"role": "system", "content": "You are a professional CRM and customer behavior specialist."}, {"role": "user", "content": "วิเคราะห์พฤติกรรมลูกค้ารายนี้และแนะนำโปรโมชันรักษาฐานลูกค้า: ยอดซื้อเฉลี่ยลดลง 40%…

This repository contains GGUF quantized weights for yothinS/Llama-3.2-3B-ThaiCRM, a model fine-tuned for Customer Relationship Management (CRM) workflows, Thai business communications, and customer behavioral analysis. GGUF files can be run locally on CPU, Apple Silicon, or NVIDIA/AMD GPUs via llama.cpp, Ollama, LM Studio, or carrany/text-generation-webui. Download your chosen GGUF file using the Hugging Face CLI: huggingface-cli download yothinS/Llama-3.2-3B-ThaiCRM-GGUF Llama-3.2-3B-ThaiCRM.Q4KM.gguf --local-dir.

96 for Qwen and 128 for Llama, compared with 32 for both in v4. Full runs retain 64 prompts, 16 responses per prompt, and 300 total steps. The 24 GB offload profile retains concurrency 8. See the v5 release notes. Higher concurrency has CPU configuration validation only; GPU capacity and throughput have not been measured, and sampled training trajectories may differ. The guided launcher uses majority voting; wrong-label and inverted reward selection are not exposed by this launcher. V5 includes the v4 fix for caches creating a run directory before the launcher. For the standalone v3 cache fix, see the patch notes. Use a separate extraction folder when upgrading. Existing model, checkpoint…

Attention manifold checkpoint for LLaMA 3.2 1B Instruct. This model extends the standard transformer attention mechanism with learned manifold structures on the value path. The checkpoint contains the manifold parameters only — the base model weights are loaded from meta-llama/Llama-3.2-1B-Instruct. See the LLM Inspector for the full visualization and editing toolkit. Paper forthcoming.