Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety. **Variations** Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants. **Model Architecture** Llama 3 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 helpfulness and safety.

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Meta-Llama-3-8B-Instruct-GGUF

ThomasBaruzier

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Qwen2.5-0.5B-Instruct-GGUF

ThomasBaruzier

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: **This repo contains the instruction-tuned 0.5B Qwen2.5 model**, which has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Architecture: transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias and tied word embeddings - Number of Parameters: 0.49B - Number of Paramaters (Non-Embedding): 0.36B - Number of Layers: 24 - Number of Attention Heads (GQA): 14 for Q and 2 for KV - Context Length: Full 32,768 tokens and generation 8192 tokens The code of Qwen2.5 has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`.

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Qwen2.5-1.5B-Instruct-GGUF

ThomasBaruzier

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Qwen2.5-14B-Instruct-GGUF

ThomasBaruzier

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: **This repo contains the instruction-tuned 14B Qwen2.5 model**, which has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias - Number of Parameters: 14.7B - Number of Paramaters (Non-Embedding): 13.1B - Number of Layers: 48 - Number of Attention Heads (GQA): 40 for Q and 8 for KV - Context Length: Full 131,072 tokens and generation 8192 tokens - Please refer to this section for detailed instructions on how to deploy Qwen2.5 for handling long texts. The code of Qwen2.5 has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`.

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Qwen2.5-32B-Instruct-GGUF

ThomasBaruzier

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Qwen2.5-3B-Instruct-GGUF

ThomasBaruzier

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: **This repo contains the instruction-tuned 3B Qwen2.5 model**, which has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Architecture: transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias and tied word embeddings - Number of Parameters: 3.09B - Number of Paramaters (Non-Embedding): 2.77B - Number of Layers: 36 - Number of Attention Heads (GQA): 16 for Q and 2 for KV - Context Length: Full 32,768 tokens and generation 8192 tokens The code of Qwen2.5 has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`.

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Qwen2.5-72B-Instruct-GGUF

ThomasBaruzier

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: **This repo contains the instruction-tuned 72B Qwen2.5 model**, which has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias - Number of Parameters: 72.7B - Number of Paramaters (Non-Embedding): 70.0B - Number of Layers: 80 - Number of Attention Heads (GQA): 64 for Q and 8 for KV - Context Length: Full 131,072 tokens and generation 8192 tokens - Please refer to this section for detailed instructions on how to deploy Qwen2.5 for handling long texts. The code of Qwen2.5 has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`.

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COST EXPLORER

好模型，也要算好成本。

费用分析

调整月度 Token 用量，直观比较不同模型的预估 API 开支。

每月输入 Token 10 M每月输出 Token 2 M1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价

使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。

MODEL HANDBOOK

读懂模型，再做选择

选型与常见问题

API 价格应该怎么比较？+

先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。

开放权重，就代表可以免费商用吗？+

不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。

排行榜第一，就是最适合我的模型吗？+

榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。

找到适合你的 AI 模型。

探索 AI 模型 31

DeepScaleR-1.5B-Preview-abliterated-GGUF

DeepScaleR-1.5B-Preview-GGUF

DeepSeek-R1-ReDistill-Qwen-1.5B-v1.1-GGUF

DeepSeek-R1-ReDistill-Qwen-7B-v1.1-GGUF

EXAONE-3.0-7.8B-Instruct-GGUF

EXAONE-3.5-2.4B-Instruct-GGUF

EXAONE-3.5-7.8B-Instruct-GGUF

gemma-2-27b-it-GGUF

gemma-2-2b-it-GGUF

Llama-3.1-Minitron-4B-Width-Base-GGUF

Llama-3.2-1B-Instruct-GGUF

Llama-3.2-3B-Instruct-GGUF

Llama-3.3-70B-Instruct-GGUF

Meta-Llama-3.1-405B-Instruct-GGUF

Meta-Llama-3.1-70B-Instruct-GGUF

Meta-Llama-3.1-8B-Instruct-GGUF

Meta-Llama-3-70B-Instruct-GGUF

Meta-Llama-3-8B-Instruct-GGUF

Qwen2.5-0.5B-Instruct-GGUF

Qwen2.5-1.5B-Instruct-GGUF

Qwen2.5-14B-Instruct-GGUF

Qwen2.5-32B-Instruct-GGUF

Qwen2.5-3B-Instruct-GGUF

Qwen2.5-72B-Instruct-GGUF

暂未找到匹配模型

好模型，也要算好成本。

读懂模型，再做选择

资讯看趋势，工具提效率，模型筑可能。