magnum-v4-72b-exl2:基于Qwen2.5-72B-Instruct的EXL2量化版本,复现Claude 3 prose质量

该模型旨在复现Claude 3(Sonnet和Opus)的 prose质量,基于Qwen2.5-72B-Instruct微调,包含多种EXL2量化版本,适用于文本生成任务。【此简介由AI生成】

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f3e4d745创建于 2024年11月26日8次提交
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Update README.md (#1) - Update README.md (6cd0f82b381181650ced5540baadc83461ec0e55) Co-authored-by: DV <Delta-Vector@users.noreply.huggingface.co> 1 年前
Upload ./measurement.json with huggingface_hub1 年前

license: apache-2.0 language:

  • en tags:
  • chat pipeline_tag: text-generation library_name: transformers datasets:
  • anthracite-org/c2_logs_32k_llama3_qwen2_v1.2
  • anthracite-org/kalo-opus-instruct-22k-no-refusal
  • lodrick-the-lafted/kalo-opus-instruct-3k-filtered
  • anthracite-org/nopm_claude_writing_fixed
  • anthracite-org/kalo_opus_misc_240827
  • anthracite-org/kalo_misc_part2

本仓库包含该模型的 EXL2 量化版本。如需原始权重文件,请访问此处

主分支仅包含测量文件,请查看对应修订版本获取所需量化方案:

本仓库同时提供模型的 GGUF 量化版本。如需原始权重文件,请访问此处

该系列模型旨在复现 Claude 3 系列(特别是 Sonnet 和 Opus 版本)的文本生成质量。

本模型为实验性版本——基于指令微调模型训练但展现出卓越性能,因此代号为 magnum-alter,成为 v4 系列的开创性原始模型。

该模型基于 Qwen2.5-72B-Instruct 进行精调训练。

提示词格式

标准输入格式如下所示:

<|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 预设模板

以下是适用于 SillyTavern 的指令模板与上下文模板。

上下文模板
{
  "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"
}

指令模板
{
  "system_prompt": "Currently, your role is {{char}}, described in detail below. As {{char}}, continue the narrative exchange with {{user}}.\n\n<Guidelines>\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</Guidelines>\n\n<Forbidden>\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</Forbidden>\n\nFollow the instructions in <Guidelines></Guidelines>, avoiding the items listed in <Forbidden></Forbidden>.",
  "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 配置

查看 axolotl 配置
base_model: /workspace/data/models/Qwen2.5-72B-Instruct
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

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
    type: sharegpt
    conversation: chatml
  - path: anthracite-org/kalo-opus-instruct-22k-no-refusal
    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: 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/magnum-72b-data
val_set_size: 0.0
output_dir: /workspace/data/72b-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: 72b-magnum-fft
wandb_entity:
wandb_watch:
wandb_name: alter-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: 0.000004

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/zero3_bf16.json
weight_decay: 0.01
fsdp:
fsdp_config:
special_tokens:

鸣谢

我们衷心感谢 DoctorShotgun 为本次训练提供的算力赞助。 同时感谢 Anthracite 全体成员对本次微调工作的贡献。

数据集

训练过程

本次模型的全参数微调使用了由 DoctorShotgun 慷慨提供的 8 块 mi300x GPU 完成。

基于Axolotl构建

安全性

...

项目介绍

该模型旨在复现Claude 3(Sonnet和Opus)的 prose质量,基于Qwen2.5-72B-Instruct微调,包含多种EXL2量化版本,适用于文本生成任务。【此简介由AI生成】

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