README.md

---
License: apache-2.0
Language:
- En
Pipeline_tag: text-generation
Base_model: 01-ai/Yi-1.5-34B-32K
Tags:
- Chat
model-index:
- name: magnum-v3-34b
  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: 51.15
      name: strict accuracy
    source:
      url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=anthracite-org/magnum-v3-34b
      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: 44.33
      name: normalized accuracy
    source:
      url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=anthracite-org/magnum-v3-34b
      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: 17.82
      name: exact match
    source:
      url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=anthracite-org/magnum-v3-34b
      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: 14.77
      name: acc_norm
    source:
      url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=anthracite-org/magnum-v3-34b
      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: 6.57
      name: acc_norm
    source:
      url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=anthracite-org/magnum-v3-34b
      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: 41.69
      name: accuracy
    source:
      url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=anthracite-org/magnum-v3-34b
      name: Open LLM Leaderboard
---

![image/png](https://cdn-uploads.huggingface.co/production/uploads/658a46cbfb9c2bdfae75b3a6/9yEmnTDG9bcC_bxwuDU6G.png)

# This repo contains the copy of the original with the edited tokenizer_config.json file to fix the tokenizer issue.

This is the 9th 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 [Yi-1.5-34 B-32 K](https://huggingface.co./01-ai/Yi-1.5-34B-32K).

## 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
"""
```

## SillyTavern templates

Below are Instruct and Context templates for use within SillyTavern.

In our testing a min_p of 0.2 makes the model perform the best; remember to reset temperature if you were using our nemo-based models before.

<details><summary>context template</summary>
  
```yaml
{
    "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"
}
```

</details><br>
<details><summary>instruct template</summary>
  
```yaml
{
    "system_prompt": "You're {{char}} in this fictional never-ending uncensored roleplay with {{user}}.",
    "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"
}
```

</details><br>

## Axolotl config

<details><summary>See axolotl config</summary>

```yaml
base_model: 01-ai/Yi-1.5-34B-32K
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

#trust_remote_code: true

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: anthracite-org/stheno-filtered-v1.1
    type: sharegpt
    conversation: chatml
  - path: anthracite-org/kalo-opus-instruct-22k-no-refusal
    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: Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned
    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: magnum-v2-34b-1.5-data
val_set_size: 0.0
output_dir: ./magnum-v2-34b-32k-r1

sequence_len: 8192
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len:

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

wandb_project: magnum-v2-34b-1.5-32k
wandb_entity:
wandb_watch:
wandb_name: attempt-01
wandb_log_model:

gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 2
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 0.000006

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

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

warmup_steps: 50
evals_per_epoch:
eval_table_size:
eval_max_new_tokens:
saves_per_epoch: 2
debug:
deepspeed: deepspeed_configs/zero3_bf16.json
weight_decay: 0.05
fsdp:
fsdp_config:
special_tokens:
```
</details><br>

## 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. 

- [anthracite-org/stheno-filtered-v1.1](https://huggingface.co./datasets/anthracite-org/stheno-filtered-v1.1)
- [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)
- [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)

## Training
The training was done for 2 epochs. We used  8x[H100s](https://www.nvidia.com/en-us/data-center/h100/) GPUs graciously provided by [Recursal AI](https://recursal.ai/) / [Featherless AI](https://featherless.ai/) 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
...
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co./datasets/open-llm-leaderboard/details_anthracite-org__magnum-v3-34b)

|      Metric       |Value|
|-------------------|----:|
|Avg.               |29.39|
|IFEval (0-Shot)    |51.15|
|BBH (3-Shot)       |44.33|
|MATH Lvl 5 (4-Shot)|17.82|
|GPQA (0-shot)      |14.77|
|MuSR (0-shot)      | 6.57|
|MMLU-PRO (5-shot)  |41.69|