🚀 大模型马格努姆v4-12b
本项目是一系列旨在复刻Claude 3模型（特别是Sonnet和Opus）散文质量的模型。本仓库包含该模型的GGUF量化版本。若你需要原始权重，请点击此处获取。
🚀 快速开始
模型基础信息
本模型是在 mistralai/Mistral-Nemo-Instruct-2407 基础上进行微调得到的。
输入格式
典型的输入格式如下：
<s>[INST] 系统消息
用户消息[/INST] 助手消息</s>[INST] 用户消息[/INST]
SillyTavern模板
以下是SillyTavern中使用的指令模板和上下文模板：
上下文模板
default SillyTavern template works fine
指令模板
default SillyTavern template works fine
Axolotl配置
查看axolotl配置
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
shuffle_merged_datasets: true
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>
✨ 主要特性
本系列模型旨在复刻Claude 3模型的散文质量，为用户提供高质量的文本生成体验。
📚 详细文档
数据集
本模型使用了以下数据集进行训练：
训练信息
模型训练了2个epoch，使用了由 Recursal AI / Featherless AI 慷慨提供的8张H100 GPU进行全参数微调。
🔧 技术细节
本模型基于Axolotl进行训练配置，采用了一系列优化策略，如梯度累积、混合精度训练、梯度检查点等，以提高训练效率和模型性能。
📄 许可证
本项目采用Apache-2.0许可证。
致谢
我们要感谢Recursal / Featherless为本次训练提供计算资源。自第一个72B的Magnum模型以来，Featherless一直托管我们的Magnum模型，让数千人能够使用我们的模型，并帮助我们不断发展。
我们还要感谢Anthracite的所有成员，是他们让这次微调成为可能。
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