---
license: apache-2.0
language:
- en
tags:
- chat
- llama-cpp
- gguf-my-repo
pipeline_tag: text-generation
library_name: transformers
base_model: anthracite-org/magnum-v4-12b
---

# Triangle104/magnum-v4-12b-Q5_K_M-GGUF
This model was converted to GGUF format from [`anthracite-org/magnum-v4-12b`](https://huggingface.co/anthracite-org/magnum-v4-12b) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/anthracite-org/magnum-v4-12b) for more details on the model.

---
Model details:
-
This is 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 mistralai/Mistral-Nemo-Instruct-2407.
Prompting

A typical input would look like this:

<s>[INST] SYSTEM MESSAGE
USER MESSAGE[/INST] ASSISTANT MESSAGE</s>[INST] USER MESSAGE[/INST]

SillyTavern templates
-
Below are Instruct and Context templates for use within SillyTavern.
context template

default SillyTavern template works fine

instruct template
-
default SillyTavern template works fine

Axolotl config
-
See axolotl config

base_model: mistralai/Mistral-Nemo-Instruct-2407
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

hub_model_id: anthracite-org/magnum-v4-12b-r2
hub_strategy: "all_checkpoints"
push_dataset_to_hub:
hf_use_auth_token: true

plugins:
  - axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_swiglu: true
liger_fused_linear_cross_entropy: true

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: anthracite-org/c2_logs_32k_llama3_qwen2_v1.2_no_system
    type: custommistralv3tekken
  - path: anthracite-org/kalo-opus-instruct-22k-no-refusal-no-system
    type: custommistralv3tekken
  - path: anthracite-org/kalo-opus-instruct-3k-filtered-no-system
    type: custommistralv3tekken
  - path: anthracite-org/nopm_claude_writing_fixed
    type: custommistralv3tekken
  - path: anthracite-org/kalo_opus_misc_240827_no_system
    type: custommistralv3tekken
  - path: anthracite-org/kalo_misc_part2_no_system
    type: custommistralv3tekken
#chat_template: chatml
shuffle_merged_datasets: true
#default_system_message: "You are an assistant that responds to the user."
dataset_prepared_path: /workspace/data/magnum-12b-data
val_set_size: 0.0
output_dir: /workspace/data/12b-fft-out

sequence_len: 32768
sample_packing: true
pad_to_sequence_len: true

adapter:
lora_model_dir:
lora_r:
lora_alpha:
lora_dropout:
lora_target_linear:
lora_fan_in_fan_out:

wandb_project: 12b-magnum-fft
wandb_entity:
wandb_watch:
wandb_name: v4-r2-attempt-01
wandb_log_model:

gradient_accumulation_steps: 2
micro_batch_size: 1
num_epochs: 2
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.00001

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 40
evals_per_epoch:
eval_table_size:
eval_max_new_tokens:
saves_per_epoch: 2
debug:
deepspeed: deepspeed_configs/zero2.json
weight_decay: 0.1
fsdp:
fsdp_config:
special_tokens:
  pad_token: <pad>

Credits
-
We'd like to thank Recursal / Featherless for sponsoring the compute for this train, Featherless has been hosting our Magnum models since the first 72 B and has given thousands of people access to our models and helped us grow.

We would also like to thank all members of Anthracite who made this finetune possible.
Datasets

    anthracite-org/c2_logs_32k_llama3_qwen2_v1.2_no_system
    anthracite-org/kalo-opus-instruct-22k-no-refusal-no-system
    anthracite-org/kalo-opus-instruct-3k-filtered-no-system
    anthracite-org/nopm_claude_writing_fixed
    anthracite-org/kalo_opus_misc_240827_no_system
    anthracite-org/kalo_misc_part2_no_system

Training
-
The training was done for 2 epochs. We used 8xH100s GPUs graciously provided by Recursal AI / Featherless AI for the full-parameter fine-tuning of the model.

---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
llama-cli --hf-repo Triangle104/magnum-v4-12b-Q5_K_M-GGUF --hf-file magnum-v4-12b-q5_k_m.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/magnum-v4-12b-Q5_K_M-GGUF --hf-file magnum-v4-12b-q5_k_m.gguf -c 2048
```

Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```

Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```

Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/magnum-v4-12b-Q5_K_M-GGUF --hf-file magnum-v4-12b-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/magnum-v4-12b-Q5_K_M-GGUF --hf-file magnum-v4-12b-q5_k_m.gguf -c 2048
```