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---
license: apache-2.0
language:
- en
pipeline_tag: text-generation
quantized_by: anthracite-org
base_model: anthracite-org/magnum-v2-4b
tags:
- chat
---
## This repo contains GGUF quants of the model. If you need the original weights, please find them [here](https://huggingface.co/anthracite-org/magnum-v2-4b).
## The quants were made with the mentioned PR merged.
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
Upstream support has been merged, so these quants work out of the box now!
<details><summary>old instructions before PR</summary>
To run inference on this model, you'll need to use Aphrodite, vLLM or EXL2/tabbyAPI, as llama.cpp hasn't yet merged the required pull request to fix the llama3.1 rope_freqs issue with custom head dimensions.
However, you can work around this by quantizing the model yourself to create a functional GGUF file. Note that until [this PR](https://github.com/ggerganov/llama.cpp/pull/9141) is merged, the context will be limited to 8k tokens.
To create a working GGUF file, make the following adjustments:
1. Remove the `"rope_scaling": {}` entry from `config.json`
2. Change `"max_position_embeddings"` to `8192` in `config.json`
These modifications should allow you to use the model with llama.cpp, albeit with the mentioned context limitation.</strike>
</details><br>
## axolotl config
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
base_model: IntervitensInc/Llama-3.1-Minitron-4B-Width-Base-chatml
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: anthracite-org/Gryphe-3.5-16k-Subset
type: sharegpt
conversation: chatml
- path: Epiculous/Synthstruct-Gens-v1-Filtered-n-Cleaned
type: sharegpt
conversation: chatml
- path: anthracite-org/Stheno-Data-Filtered
type: sharegpt
conversation: chatml
- path: Epiculous/SynthRP-Gens-v1-Filtered-n-Cleaned
type: sharegpt
conversation: chatml
- path: lodrick-the-lafted/NopmWritingStruct
type: sharegpt
conversation: chatml
- path: anthracite-org/kalo-opus-instruct-22k-no-refusal
type: sharegpt
conversation: chatml
chat_template: chatml
val_set_size: 0.01
output_dir: ./outputs/out
adapter:
lora_r:
lora_alpha:
lora_dropout:
lora_target_linear:
sequence_len: 16384
# sequence_len: 32768
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 32
micro_batch_size: 1
num_epochs: 2
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.00002
weight_decay: 0.05
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: true
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_ratio: 0.1
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
fsdp:
fsdp_config:
special_tokens:
pad_token: <|finetune_right_pad_id|>
```
</details><br>
## 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
... |