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---
License: apache-2.0
Language:
- En
Pipeline_tag: text-generation
Base_model: IntervitensInc/Llama-3.1-Minitron-4B-Width-Base-chatml
tags:
- Chat
---
## This repo contains EXL2 quants of the model. If you need the original weights, please find them [here](https://huggingface.co/anthracite-org/magnum-v2-4b).
## Base repo only contains the measurement file, see revisions for your quant of choice.
- [measurement.json](https://huggingface.co/anthracite-org/magnum-v2-4b-exl2/tree/main)
- [2.0bpw](https://huggingface.co/anthracite-org/magnum-v2-4b-exl2/tree/2.0bpw)
- [3.0bpw](https://huggingface.co/anthracite-org/magnum-v2-4b-exl2/tree/3.0bpw)
- [4.0bpw](https://huggingface.co/anthracite-org/magnum-v2-4b-exl2/tree/4.0bpw)
- [5.0bpw](https://huggingface.co/anthracite-org/magnum-v2-4b-exl2/tree/5.0bpw)
- [6.0bpw](https://huggingface.co/anthracite-org/magnum-v2-4b-exl2/tree/6.0bpw)
- [8.0bpw](https://huggingface.co/anthracite-org/magnum-v2-4b-exl2/tree/8.0bpw)
This is the eighth in a series of models designed to replicate the prose quality of the Claude 3 models, specifically Sonnet and Opus. This model is fine-tuned on top of [IntervitensInc/Llama-3.1-Minitron-4B-Width-Base-chatml](https://huggingface.co/IntervitensInc/Llama-3.1-Minitron-4B-Width-Base-chatml).
## Prompting
Model has been Instruct tuned with the ChatML formatting. A typical input would look like this:
```py
"""<|im_start|>system
system prompt<|im_end|>
<|im_start|>user
Hi there!<|im_end|>
<|im_start|>assistant
Nice to meet you!<|im_end|>
<|im_start|>user
Can I ask a question?<|im_end|>
<|im_start|>assistant
"""
```
## Support
In order to inference this model you will have to use Aphrodite or vLLM as llama.cpp has not yet merged the required pull request to fix llama3.1 rope_freqs not respecting custom head_dim - You can however get around this by quanting the model yourself with the following fixes for a working GGUF. However, it will be stuck at 8k context until [this PR](https://github.com/ggerganov/llama.cpp/pull/9141) is merged.
1. Remove `"rope_scaling": {}` from `config.json`
2. Change `"max_position_embeddings"` to `8192` in `config.json`
3. Add `"add_bos_token": false` to `tokenizer_config.json`
## Credits
- [anthracite-org/Stheno-Data-Filtered](https://huggingface.co/datasets/anthracite-org/Stheno-Data-Filtered)
- [anthracite-org/kalo-opus-instruct-22k-no-refusal](https://huggingface.co/datasets/anthracite-org/kalo-opus-instruct-22k-no-refusal)
- [lodrick-the-lafted/NopmWritingStruct](https://huggingface.co/datasets/lodrick-the-lafted/NopmWritingStruct)
- [NewEden/Gryphe-3.5-16k-Subset](NewEden/Gryphe-3.5-16k-Subset)
- [Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned](https://huggingface.co/datasets/Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned)
- [Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned](https://huggingface.co/datasets/Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned)
This model has been a team effort, and the credits goes to all members of Anthracite.
## Training
The training was done for 2 epochs. We used 2 x [RTX 6000s](https://store.nvidia.com/en-us/nvidia-rtx/products/nvidia-rtx-6000-ada-generation/) GPUs graciously provided by [Kubernetes_Bad](https://huggingface.co/kubernetes-bad) for the full-parameter fine-tuning of the model.
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
## Safety
... |