Triangle104/magnum-v4-12b-Q5_K_M-GGUF

This model was converted to GGUF format from anthracite-org/magnum-v4-12b using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card 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:

[INST] SYSTEM MESSAGE USER MESSAGE[/INST] ASSISTANT MESSAGE[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:

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)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

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:

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 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