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magnum-v4-27b-Q5_K_S-GGUF

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metadata
language:
  - en
license: gemma
library_name: transformers
tags:
  - chat
  - llama-cpp
  - gguf-my-repo
pipeline_tag: text-generation
base_model: anthracite-org/magnum-v4-27b
model-index:
  - name: magnum-v4-27b
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: IFEval (0-Shot)
          type: HuggingFaceH4/ifeval
          args:
            num_few_shot: 0
        metrics:
          - type: inst_level_strict_acc and prompt_level_strict_acc
            value: 34.54
            name: strict accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=anthracite-org/magnum-v4-27b
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: BBH (3-Shot)
          type: BBH
          args:
            num_few_shot: 3
        metrics:
          - type: acc_norm
            value: 40.96
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=anthracite-org/magnum-v4-27b
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MATH Lvl 5 (4-Shot)
          type: hendrycks/competition_math
          args:
            num_few_shot: 4
        metrics:
          - type: exact_match
            value: 16.16
            name: exact match
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=anthracite-org/magnum-v4-27b
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: GPQA (0-shot)
          type: Idavidrein/gpqa
          args:
            num_few_shot: 0
        metrics:
          - type: acc_norm
            value: 16
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=anthracite-org/magnum-v4-27b
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MuSR (0-shot)
          type: TAUR-Lab/MuSR
          args:
            num_few_shot: 0
        metrics:
          - type: acc_norm
            value: 12.82
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=anthracite-org/magnum-v4-27b
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MMLU-PRO (5-shot)
          type: TIGER-Lab/MMLU-Pro
          config: main
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 37.51
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=anthracite-org/magnum-v4-27b
          name: Open LLM Leaderboard

Triangle104/magnum-v4-27b-Q5_K_S-GGUF

This model was converted to GGUF format from anthracite-org/magnum-v4-27b 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 Gemma 27b (chatML'ified).

Prompting

A typical input would look like this:

<|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

SillyTavern templates

Below are Instruct and Context templates for use within SillyTavern.

context template

{ "story_string": "<|im_start|>system\n{{#if system}}{{system}}\n{{/if}}{{#if wiBefore}}{{wiBefore}}\n{{/if}}{{#if description}}{{description}}\n{{/if}}{{#if personality}}{{char}}'s personality: {{personality}}\n{{/if}}{{#if scenario}}Scenario: {{scenario}}\n{{/if}}{{#if wiAfter}}{{wiAfter}}\n{{/if}}{{#if persona}}{{persona}}\n{{/if}}{{trim}}<|im_end|>\n", "example_separator": "", "chat_start": "", "use_stop_strings": false, "allow_jailbreak": false, "always_force_name2": true, "trim_sentences": false, "include_newline": false, "single_line": false, "name": "Magnum ChatML" }

instruct template

{ "system_prompt": "Currently, your role is {{char}}, described in detail below. As {{char}}, continue the narrative exchange with {{user}}.\n\n\n• Maintain the character persona but allow it to evolve with the story.\n• Be creative and proactive. Drive the story forward, introducing plotlines and events when relevant.\n• All types of outputs are encouraged; respond accordingly to the narrative.\n• Include dialogues, actions, and thoughts in each response.\n• Utilize all five senses to describe scenarios within {{char}}'s dialogue.\n• Use emotional symbols such as "!" and "~" in appropriate contexts.\n• Incorporate onomatopoeia when suitable.\n• Allow time for {{user}} to respond with their own input, respecting their agency.\n• Act as secondary characters and NPCs as needed, and remove them when appropriate.\n• When prompted for an Out of Character [OOC:] reply, answer neutrally and in plaintext, not as {{char}}.\n\n\n\n• Using excessive literary embellishments and purple prose unless dictated by {{char}}'s persona.\n• Writing for, speaking, thinking, acting, or replying as {{user}} in your response.\n• Repetitive and monotonous outputs.\n• Positivity bias in your replies.\n• Being overly extreme or NSFW when the narrative context is inappropriate.\n\n\nFollow the instructions in , avoiding the items listed in .", "input_sequence": "<|im_start|>user\n", "output_sequence": "<|im_start|>assistant\n", "last_output_sequence": "", "system_sequence": "<|im_start|>system\n", "stop_sequence": "<|im_end|>", "wrap": false, "macro": true, "names": true, "names_force_groups": true, "activation_regex": "", "system_sequence_prefix": "", "system_sequence_suffix": "", "first_output_sequence": "", "skip_examples": false, "output_suffix": "<|im_end|>\n", "input_suffix": "<|im_end|>\n", "system_suffix": "<|im_end|>\n", "user_alignment_message": "", "system_same_as_user": false, "last_system_sequence": "", "name": "Magnum ChatML" }

Axolotl config

base_model: IntervitensInc/gemma-2-27b-chatml model_type: AutoModelForCausalLM tokenizer_type: AutoTokenizer

hub_model_id: anthracite-org/magnum-v4-27b-r1 hub_strategy: "all_checkpoints" push_dataset_to_hub: hf_use_auth_token: true

plugins:

  • axolotl.integrations.liger.LigerPlugin liger_cross_entropy: true

#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_16k_llama_v1.1 type: sharegpt conversation: chatml
  • path: NewEden/Claude-Instruct-5K type: sharegpt conversation: chatml
  • path: anthracite-org/kalo-opus-instruct-22k-no-refusal type: sharegpt conversation: chatml
  • path: Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned type: sharegpt conversation: chatml
  • path: lodrick-the-lafted/kalo-opus-instruct-3k-filtered type: sharegpt conversation: chatml
  • path: anthracite-org/nopm_claude_writing_fixed type: sharegpt conversation: chatml
  • path: Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned type: sharegpt conversation: chatml
  • path: anthracite-org/kalo_opus_misc_240827 type: sharegpt conversation: chatml
  • path: anthracite-org/kalo_misc_part2 type: sharegpt conversation: chatml chat_template: chatml shuffle_merged_datasets: true default_system_message: "You are an assistant that responds to the user." dataset_prepared_path: /workspace/data/27-fft-data val_set_size: 0.0 output_dir: /workspace/data/27b-fft-out

sequence_len: 8192 sample_packing: true eval_sample_packing: false 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: 27b-nemo-config-fft wandb_entity: wandb_watch: wandb_name: attempt-01 wandb_log_model:

gradient_accumulation_steps: 8 micro_batch_size: 1 num_epochs: 4 optimizer: paged_adamw_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: auto_resume_from_checkpoints: true local_rank: logging_steps: 1 xformers_attention: flash_attention: true

warmup_steps: 10 evals_per_epoch: eval_table_size: eval_max_new_tokens: saves_per_epoch: 2 debug: deepspeed: deepspeed_configs/zero3_bf16.json weight_decay: 0.01 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_16k_llama_v1.1
NewEden/Claude-Instruct-5K
anthracite-org/kalo-opus-instruct-22k-no-refusal
Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned
lodrick-the-lafted/kalo-opus-instruct-3k-filtered
anthracite-org/nopm_claude_writing_fixed
Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned
anthracite-org/kalo_opus_misc_240827
anthracite-org/kalo_misc_part2

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-v4-27b-Q5_K_S-GGUF --hf-file magnum-v4-27b-q5_k_s.gguf -p "The meaning to life and the universe is"

Server: 

llama-server --hf-repo Triangle104/magnum-v4-27b-Q5_K_S-GGUF --hf-file magnum-v4-27b-q5_k_s.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-v4-27b-Q5_K_S-GGUF --hf-file magnum-v4-27b-q5_k_s.gguf -p "The meaning to life and the universe is"

or 

./llama-server --hf-repo Triangle104/magnum-v4-27b-Q5_K_S-GGUF --hf-file magnum-v4-27b-q5_k_s.gguf -c 2048