Files
ModelHub XC c17ea0d85f 初始化项目,由ModelHub XC社区提供模型
Model: prithivMLmods/Hermes-3-Llama-3.2-3B-f32-GGUF
Source: Original Platform
2026-10-26 10:20:13 +08:00

2.1 KiB

license, base_model, language, pipeline_tag, library_name, tags
license base_model language pipeline_tag library_name tags
apache-2.0
NousResearch/Hermes-3-Llama-3.2-3B
en
text-generation transformers
text-generation-inference
SOTA

Hermes-3-Llama-3.2-3B-f32-GGUF

Hermes 3 - Llama-3.2 3B by Nous Research is a compact yet powerful generalist language model fine-tuned from the Llama-3.2 3B foundation, designed to deliver advanced agentic capabilities, improved roleplaying, complex reasoning, and coherent multi-turn conversations with long context understanding. It offers powerful user steering and control, supports structured prompting with ChatML format, and enables function calling and JSON output for dynamic interaction and automation.

Trained on high-performance GPU infrastructure, Hermes 3 excels in problem-solving, dialogue, and code generation tasks, demonstrating competitive benchmark scores across GPT4All, AGIEval, and BigBench. This model is optimized for efficiency, supports 4-bit quantization for deployment, and is ideal for building sophisticated AI assistants with controllable and natural user interactions.

Model Files

Model File name Size QuantType
Hermes-3-Llama-3.2-3B.BF16.gguf 6.43 GB BF16
Hermes-3-Llama-3.2-3B.F16.gguf 6.43 GB F16
Hermes-3-Llama-3.2-3B.F32.gguf 12.9 GB F32
Hermes-3-Llama-3.2-3B.Q2_K.gguf 1.36 GB Q2_K
Hermes-3-Llama-3.2-3B.Q3_K_L.gguf 1.82 GB Q3_K_L
Hermes-3-Llama-3.2-3B.Q3_K_M.gguf 1.69 GB Q3_K_M
Hermes-3-Llama-3.2-3B.Q3_K_S.gguf 1.54 GB Q3_K_S
Hermes-3-Llama-3.2-3B.Q4_K_M.gguf 2.02 GB Q4_K_M
Hermes-3-Llama-3.2-3B.Q4_K_S.gguf 1.93 GB Q4_K_S
Hermes-3-Llama-3.2-3B.Q5_K_M.gguf 2.32 GB Q5_K_M
Hermes-3-Llama-3.2-3B.Q5_K_S.gguf 2.27 GB Q5_K_S
Hermes-3-Llama-3.2-3B.Q6_K.gguf 2.64 GB Q6_K
Hermes-3-Llama-3.2-3B.Q8_0.gguf 3.42 GB Q8_0

Quants Usage

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

image.png