Nous-Hermes-2-Yi-34B

Customize or build on this model

Listed from public GitHub data, not tested by us. Read the code and license before you run anything.

Linked in the model card

GitHub repositories the original model card links to.

• axolotl-ai-cloud/axolotl ↗

  Repo’s own description: Go ahead and axolotl questions

  ★ 12,629 · Last updated 2026-10-25 · License: Apache-2.0

Projects that use this model

Public projects whose files name this exact model.

• OpenGVLab/InternVL ↗

  Repo’s own description: [CVPR 2024 Oral] InternVL Family: A Pioneering Open-Source Alternative to GPT-4o. 接近GPT-4o表现的开源多模态对话模型

  ★ 10,170 · Last updated 2025-09-22 · License: MIT

  Names this model in: README.md ↗

• cambrian-mllm/cambrian ↗

  Repo’s own description: Cambrian-1 is a family of multimodal LLMs with a vision-centric design.

  ★ 2,011 · Last updated 2025-11-07 · License: Apache-2.0

  Names this model in: README.md ↗

• EvolvingLMMs-Lab/LLaVA-OneVision-2 ↗

  Repo’s own description: Fully Open Framework for Democratized Multimodal Training

  ★ 1,225 · Last updated 2026-10-24 · License: Apache-2.0

  Names this model in: aiak_megatron/examples/multimodal/nvlm/sft_34b_internvit.sh ↗

• bytedance/xpu-perf ↗

  Repo’s own description: [HPCA 2026] AI Accelerator Benchmark focuses on evaluating AI Accelerators from a practical production perspective, including the ease of use and versatility of software and hardware.

  ★ 394 · Last updated 2026-04-22 · License: Apache-2.0

  Names this model in: projects/train_perf/Megatron-LM/examples/multimodal/nvlm/run_text_generation_yi_34b_internvit_6b.sh ↗

• Yuan-lab-LLM/Yuan3.0 ↗

  Repo’s own description: Yuan3.0: Mixture-of-Experts (MoE) Language Model

  ★ 186 · Last updated 2026-04-07 · License: not stated

  Names this model in: rlhf/megatron-lm/examples/multimodal/nvlm/sft_34b_internvit.sh ↗

• dannyallover/llm_forecasting ↗

  Repo’s own description: Forecasting with LLMs

  ★ 64 · Last updated 2026-04-19 · License: not stated

  Names this model in: llm_forecasting/config/constants.py ↗

Tools to customize it further

Remove more refusals

• Heretic ↗ ★ 36,421

  Removes refusal behavior from transformer models automatically. It pairs directional ablation (abliteration) with an Optuna parameter search that tries to cut refusals while keeping the model close to the original, so you don't have to pick layers by hand.

• OBLITERATUS ↗ ★ 8,946

  A larger refusal-removal toolkit with several abliteration methods, usable locally or through a hosted Hugging Face Space. Check the telemetry setting before you run it: the README describes telemetry as opt-in, but on by default in the Spaces version.

Fine-tune it on your own data

• Unsloth ↗ ★ 78,528

  A desktop app and Python library for running and training models locally. Its README lists LoRA, QLoRA, full fine-tuning, DPO and GRPO, plus export to formats including GGUF, on Windows, Linux, WSL and macOS.

• LlamaFactory ↗ ★ 75,713

  Fine-tunes more than 100 language and vision-language models from a command line or a browser-based Web UI, aimed at training without writing code.

Make a smaller download (quantize)

• llama.cpp ↗ ★ 132,260

  Runs models locally and includes the tools behind most GGUF files. A conversion script turns Hugging Face weights into GGUF, and llama-quantize shrinks a high-precision GGUF to lower-bit formats, which can cost some accuracy.

• MLX LM ↗ ★ 7,467

  Apple's MLX package for running and fine-tuning models on Apple silicon. It can quantize models and upload them to the Hugging Face Hub, and supports LoRA and full fine-tuning.

Merge it with another model

• mergekit ↗ ★ 7,414

  Merges pretrained models with many merge methods. It works out-of-core, so merges can run on a CPU or with as little as 8 GB of VRAM, and it also handles LoRA extraction and mixture-of-experts merges.

Learn the techniques

• LLM Course ↗ ★ 84,667

  A free course with roadmaps and Colab notebooks. Its notebooks include one-click tools for merging with MergeKit, quantizing to GGUF, GPTQ, EXL2, AWQ and HQQ, and automatic abliteration with custom datasets.

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