CognitiveComputations / dolphin-mistral-nemo

海豚，未经审查，在 mistralai/Mistral-Nemo-Base-2407 (12b) 128k 文本上下文中训练

12B

802 Pulls 更新于4周前

更新于4周前

4周前

91b68b7c8e67 · 8.5GB

读我

Dolphin 2.9.3 Mistral Nemo 12b 🐬

由Eric Hartford和Cognitive Computations curated和 trained

Discord: https://discord.gg/h3K4XGj2RH

感谢Dolphin 2.9.3的赞助商
- Crusoe Cloud -提供优秀的8xL40S节点

此模型基于mistralai/Mistral-Nemo-Base-2407，并受apache 2.0许可证的约束。

基础模型有128K上下文，我们的微调使用了8192序列长度。

Dolphin 2.9.3 使用ChatML提示模板格式。

示例

<|im_start|>system
You are Dolphin, a helpful AI assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

Dolphin-2.9.3 拥有多种遵循指令、对话和编码技能。它还具有初始的代理能力并支持功能调用。

海豚未经审查。我们已经过滤了数据集，以移除对齐和偏差，这使得模型更加合规。建议您在将模型公开作为服务之前实施自己的对齐层。它将对任何请求极其合规，甚至包括不道德的请求。请阅读我关于未审查模型的博客文章。https://erichartford.com/uncensored-models 您应对使用此模型创建的任何内容负责。请负责任地享受。

海豚遵循Apache 2.0许可。我们授予包括商业用途在内的任何使用权限。海豚在包括GPT4在内的多个模型中训练过。

评估

待定

训练

查看axolotl配置

axolotl版本：0.4.1

base_model: /workspace/models/Mistral-Nemo-Base-2407
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
# load_in_4bit: true
strict: false

datasets:
  - path: /workspace/datasets/dolphin-2.9.3/dolphin201-sharegpt2.jsonl
    type: sharegpt
    conversation: chatml
  - path: /workspace/datasets/dolphin-2.9.3/SystemChat_filtered_sharegpt.jsonl
    type: sharegpt
    conversation: chatml
  - path: /workspace/datasets/dolphin-2.9.3/SystemChat_multilingual_sharegpt.jsonl
    type: sharegpt
    conversation: chatml
  - path: /workspace/datasets/dolphin-2.9.3/dolphin-coder-translate-sharegpt2.jsonl
    type: sharegpt
    conversation: chatml
  - path: /workspace/datasets/dolphin-2.9.3/dolphin-coder-codegen-sharegpt2.jsonl
    type: sharegpt
    conversation: chatml
  - path: /workspace/datasets/dolphin-2.9.3/m-a-p_Code-Feedback-sharegpt-unfiltered.jsonl
    type: sharegpt
    conversation: chatml
  - path: /workspace/datasets/dolphin-2.9.3/m-a-p_CodeFeedback-Filtered-Instruction-sharegpt-unfiltered.jsonl
    type: sharegpt
    conversation: chatml
  - path: /workspace/datasets/dolphin-2.9.3/not_samantha_norefusals.jsonl
    type: sharegpt
    conversation: chatml
  - path: /workspace/datasets/dolphin-2.9.3/Orca-Math-resort-unfiltered.jsonl
    type: sharegpt
    conversation: chatml
  - path: /workspace/datasets/dolphin-2.9.3/agent_instruct_react_unfiltered.jsonl
    type: sharegpt  
    conversation: chatml
  - path: /workspace/datasets/dolphin-2.9.3/toolbench_instruct_j1s1_3k_unfiltered.jsonl
    type: sharegpt  
    conversation: chatml
  - path: /workspace/datasets/dolphin-2.9.3/toolbench_negative_unfiltered.jsonl
    type: sharegpt
    conversation: chatml
  - path: /workspace/datasets/dolphin-2.9.3/toolbench_react_10p_unfiltered.jsonl
    type: sharegpt
    conversation: chatml
  - path: /workspace/datasets/dolphin-2.9.3/toolbench_tflan_cot_30p_unfiltered.jsonl
    type: sharegpt
    conversation: chatml
  - path: /workspace/datasets/dolphin-2.9.3/openhermes200k_unfiltered.jsonl
    type: sharegpt 
    conversation: chatml

chat_template: chatml
# adapter: qlora
# lora_r: 128
# lora_alpha: 16
# lora_modules_to_save: [embed_tokens, lm_head]
# lora_dropout: 0.05
# lora_target_linear: true


unfrozen_parameters:
- ^lm_head.weight$
- ^model.embed_tokens.weight$
- input_layernorm
- model.norm
- post_attention_layernorm
- self_attn.rotary_emb
# mlp.down_proj layers
- model.layers.0.mlp.down_proj
- model.layers.1.mlp.down_proj
- model.layers.4.mlp.down_proj
- model.layers.37.mlp.down_proj
- model.layers.24.mlp.down_proj
- model.layers.2.mlp.down_proj
- model.layers.38.mlp.down_proj
- model.layers.35.mlp.down_proj
- model.layers.25.mlp.down_proj
- model.layers.6.mlp.down_proj
- model.layers.22.mlp.down_proj
- model.layers.23.mlp.down_proj
- model.layers.3.mlp.down_proj
- model.layers.21.mlp.down_proj
- model.layers.5.mlp.down_proj
- model.layers.28.mlp.down_proj
- model.layers.20.mlp.down_proj
- model.layers.26.mlp.down_proj
- model.layers.19.mlp.down_proj
- model.layers.34.mlp.down_proj
# mlp.gate_proj layers
- model.layers.2.mlp.gate_proj
- model.layers.1.mlp.gate_proj
- model.layers.3.mlp.gate_proj
- model.layers.5.mlp.gate_proj
- model.layers.4.mlp.gate_proj
- model.layers.35.mlp.gate_proj
- model.layers.36.mlp.gate_proj
- model.layers.37.mlp.gate_proj
- model.layers.38.mlp.gate_proj
- model.layers.34.mlp.gate_proj
- model.layers.33.mlp.gate_proj
- model.layers.8.mlp.gate_proj
- model.layers.32.mlp.gate_proj
- model.layers.6.mlp.gate_proj
- model.layers.28.mlp.gate_proj
- model.layers.26.mlp.gate_proj
- model.layers.30.mlp.gate_proj
- model.layers.23.mlp.gate_proj
- model.layers.29.mlp.gate_proj
- model.layers.27.mlp.gate_proj
# mlp.up_proj layers
- model.layers.3.mlp.up_proj
- model.layers.4.mlp.up_proj
- model.layers.6.mlp.up_proj
- model.layers.2.mlp.up_proj
- model.layers.5.mlp.up_proj
- model.layers.8.mlp.up_proj
- model.layers.10.mlp.up_proj
- model.layers.9.mlp.up_proj
- model.layers.7.mlp.up_proj
- model.layers.0.mlp.up_proj
- model.layers.17.mlp.up_proj
- model.layers.15.mlp.up_proj
- model.layers.22.mlp.up_proj
- model.layers.18.mlp.up_proj
- model.layers.16.mlp.up_proj
- model.layers.11.mlp.up_proj
- model.layers.21.mlp.up_proj
- model.layers.23.mlp.up_proj
- model.layers.20.mlp.up_proj
- model.layers.27.mlp.up_proj
# self_attn.k_proj layers
- model.layers.30.self_attn.k_proj
- model.layers.27.self_attn.k_proj
- model.layers.25.self_attn.k_proj
- model.layers.33.self_attn.k_proj
- model.layers.26.self_attn.k_proj
- model.layers.31.self_attn.k_proj
- model.layers.35.self_attn.k_proj
- model.layers.39.self_attn.k_proj
- model.layers.22.self_attn.k_proj
- model.layers.24.self_attn.k_proj
- model.layers.21.self_attn.k_proj
- model.layers.28.self_attn.k_proj
- model.layers.23.self_attn.k_proj
- model.layers.36.self_attn.k_proj
- model.layers.20.self_attn.k_proj
- model.layers.37.self_attn.k_proj
- model.layers.29.self_attn.k_proj
- model.layers.32.self_attn.k_proj
- model.layers.16.self_attn.k_proj
- model.layers.18.self_attn.k_proj
# self_attn.o_proj layers
- model.layers.7.self_attn.o_proj
- model.layers.6.self_attn.o_proj
- model.layers.9.self_attn.o_proj
- model.layers.5.self_attn.o_proj
- model.layers.27.self_attn.o_proj
- model.layers.26.self_attn.o_proj
- model.layers.4.self_attn.o_proj
- model.layers.31.self_attn.o_proj
- model.layers.8.self_attn.o_proj
- model.layers.16.self_attn.o_proj
- model.layers.3.self_attn.o_proj
- model.layers.10.self_attn.o_proj
- model.layers.18.self_attn.o_proj
- model.layers.33.self_attn.o_proj
- model.layers.17.self_attn.o_proj
- model.layers.32.self_attn.o_proj
- model.layers.30.self_attn.o_proj
- model.layers.2.self_attn.o_proj
- model.layers.15.self_attn.o_proj
- model.layers.11.self_attn.o_proj
# self_attn.q_proj layers
- model.layers.14.self_attn.q_proj
- model.layers.11.self_attn.q_proj
- model.layers.15.self_attn.q_proj
- model.layers.9.self_attn.q_proj
- model.layers.8.self_attn.q_proj
- model.layers.18.self_attn.q_proj
- model.layers.12.self_attn.q_proj
- model.layers.13.self_attn.q_proj
- model.layers.19.self_attn.q_proj
- model.layers.16.self_attn.q_proj
- model.layers.10.self_attn.q_proj
- model.layers.17.self_attn.q_proj
- model.layers.7.self_attn.q_proj
- model.layers.5.self_attn.q_proj
- model.layers.20.self_attn.q_proj
- model.layers.3.self_attn.q_proj
- model.layers.26.self_attn.q_proj
- model.layers.27.self_attn.q_proj
- model.layers.28.self_attn.q_proj
- model.layers.33.self_attn.q_proj
# self_attn.v_proj layers
- model.layers.27.self_attn.v_proj
- model.layers.20.self_attn.v_proj
- model.layers.24.self_attn.v_proj
- model.layers.25.self_attn.v_proj
- model.layers.30.self_attn.v_proj
- model.layers.2.self_attn.v_proj
- model.layers.23.self_attn.v_proj
- model.layers.22.self_attn.v_proj
- model.layers.26.self_attn.v_proj
- model.layers.33.self_attn.v_proj
- model.layers.37.self_attn.v_proj
- model.layers.7.self_attn.v_proj
- model.layers.4.self_attn.v_proj
- model.layers.18.self_attn.v_proj
- model.layers.31.self_attn.v_proj
- model.layers.17.self_attn.v_proj
- model.layers.35.self_attn.v_proj
- model.layers.32.self_attn.v_proj
- model.layers.21.self_attn.v_proj
- model.layers.3.self_attn.v_proj



dataset_prepared_path:  /workspace/axolotl/dolph-2.9.3-nemo-prepared
val_set_size: 0.01
output_dir: /workspace/axolotl/dolphin-2.9.3-mistral-nemo

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true

wandb_project: dolphin-2.9.3-Mistral-nemo
wandb_watch:
wandb_run_id:
wandb_log_model:

gradient_accumulation_steps: 16
micro_batch_size: 1
num_epochs: 3
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 5e-6
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32:

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 100
# evals_per_epoch: 4
eval_table_size:
saves_per_epoch: 1
save_total_limit: 2
save_steps:
debug:
deepspeed: deepspeed_configs/zero3_bf16.json
weight_decay: 0.1
special_tokens:
  eos_token: "<|im_end|>"
  pad_token: "<pad>"
  bos_token: "<s>"
  unk_token: "<unk>"
tokens:
  - "<|im_start|>"


# fsdp:
#   - full_shard
#   - auto_wrap
# fsdp_config:
#   fsdp_limit_all_gathers: true
#   fsdp_sync_module_states: true
#   fsdp_offload_params: true
#   fsdp_use_orig_params: false
#   fsdp_cpu_ram_efficient_loading: true
#   fsdp_transformer_layer_cls_to_wrap: MixtralSparseMoeBlock
#   fsdp_state_dict_type: FULL_STATE_DICT
#   fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
#   fsdp_sharding_strategy: FULL_SHARD
#   fsdp_forward_prefetch: false
#   fsdp_backward_prefetch: BACKWARD_PRE

工作空间/axolotl/dolphin-2.9.3-mistral-nemo

此模型是在None数据集上从头开始训练的。
在评估集上达到以下结果
- 损失：0.5605

模型描述

需要更多信息

预期用途与限制

需要更多信息

训练和评估数据

需要更多信息

训练过程

训练超参数

以下超参数在训练期间使用
- 学习率：5e-06
- 训练批大小：1
- 评估批大小：1
- 种子：42
- 分布式类型：多GPU
- 设备数量：8
- 累积梯度步骤：16
- 总训练批大小：128
- 总评估批大小：8
- 优化器：带有beta=(0.9,0.999)和epsilon=1e-08的Adam
- 学习率调度器类型：余弦
- 学习率调度器预热步骤：100
- 迭代次数：3

训练结果

训练损失	时代	步骤	验证损失
0.5691	1.0162	983	0.5734
0.5335	2.0174	1968	0.5609
0.5297	2.9639	2901	0.5605

框架版本

Transformers 4.43.0.dev0
Pytorch 2.2.2+cu121
数据集 2.19.1
分词器 0.19.1

# Dolphin 2.9.3 Mistral Nemo 12b 🐬

Curated and trained by Eric Hartford and Cognitive Computations

[![Discord](https://img.shields.io/discord/1156064224225808488?logo=Discord&logoColor=%23ffffff&label=Discord&link=https%3A%2F%2Fdiscord.gg%2FtCMkMDDHwm)](https://discord.gg/h3K4XGj2RH)
Discord: https://discord.gg/h3K4XGj2RH

Our appreciation for the sponsors of Dolphin 2.9.3:
- [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 8xL40S node

This model is based on mistralai/Mistral-Nemo-Base-2407, and is governed by the apache 2.0 license.

The base model has 128K context, and our finetuning used 8192 sequence length.

Dolphin 2.9.3 uses ChatML prompt template format.

example:

```
<|im_start|>system
You are Dolphin, a helpful AI assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

```

Dolphin-2.9.3 has a variety of instruction following, conversational, and coding skills. It also has initial agentic abilities and supports function calling.

Dolphin is uncensored. We have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models You are responsible for any content you create using this model. Enjoy responsibly.

Dolphin is licensed according to apache 2.0 license.  We grant permission for any use, including commercial. Dolphin was trained on data generated from GPT4, among other models.

## Evals

TBD

## Training

[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.4.1`
```yaml
base_model: /workspace/models/Mistral-Nemo-Base-2407
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
# load_in_4bit: true
strict: false

datasets:
  - path: /workspace/datasets/dolphin-2.9.3/dolphin201-sharegpt2.jsonl
    type: sharegpt
    conversation: chatml
  - path: /workspace/datasets/dolphin-2.9.3/SystemChat_filtered_sharegpt.jsonl
    type: sharegpt
    conversation: chatml
  - path: /workspace/datasets/dolphin-2.9.3/SystemChat_multilingual_sharegpt.jsonl
    type: sharegpt
    conversation: chatml
  - path: /workspace/datasets/dolphin-2.9.3/dolphin-coder-translate-sharegpt2.jsonl
    type: sharegpt
    conversation: chatml
  - path: /workspace/datasets/dolphin-2.9.3/dolphin-coder-codegen-sharegpt2.jsonl
    type: sharegpt
    conversation: chatml
  - path: /workspace/datasets/dolphin-2.9.3/m-a-p_Code-Feedback-sharegpt-unfiltered.jsonl
    type: sharegpt
    conversation: chatml
  - path: /workspace/datasets/dolphin-2.9.3/m-a-p_CodeFeedback-Filtered-Instruction-sharegpt-unfiltered.jsonl
    type: sharegpt
    conversation: chatml
  - path: /workspace/datasets/dolphin-2.9.3/not_samantha_norefusals.jsonl
    type: sharegpt
    conversation: chatml
  - path: /workspace/datasets/dolphin-2.9.3/Orca-Math-resort-unfiltered.jsonl
    type: sharegpt
    conversation: chatml
  - path: /workspace/datasets/dolphin-2.9.3/agent_instruct_react_unfiltered.jsonl
    type: sharegpt  
    conversation: chatml
  - path: /workspace/datasets/dolphin-2.9.3/toolbench_instruct_j1s1_3k_unfiltered.jsonl
    type: sharegpt  
    conversation: chatml
  - path: /workspace/datasets/dolphin-2.9.3/toolbench_negative_unfiltered.jsonl
    type: sharegpt
    conversation: chatml
  - path: /workspace/datasets/dolphin-2.9.3/toolbench_react_10p_unfiltered.jsonl
    type: sharegpt
    conversation: chatml
  - path: /workspace/datasets/dolphin-2.9.3/toolbench_tflan_cot_30p_unfiltered.jsonl
    type: sharegpt
    conversation: chatml
  - path: /workspace/datasets/dolphin-2.9.3/openhermes200k_unfiltered.jsonl
    type: sharegpt 
    conversation: chatml

chat_template: chatml
# adapter: qlora
# lora_r: 128
# lora_alpha: 16
# lora_modules_to_save: [embed_tokens, lm_head]
# lora_dropout: 0.05
# lora_target_linear: true

unfrozen_parameters:
- ^lm_head.weight$
- ^model.embed_tokens.weight$
- input_layernorm
- model.norm
- post_attention_layernorm
- self_attn.rotary_emb
# mlp.down_proj layers
- model.layers.0.mlp.down_proj
- model.layers.1.mlp.down_proj
- model.layers.4.mlp.down_proj
- model.layers.37.mlp.down_proj
- model.layers.24.mlp.down_proj
- model.layers.2.mlp.down_proj
- model.layers.38.mlp.down_proj
- model.layers.35.mlp.down_proj
- model.layers.25.mlp.down_proj
- model.layers.6.mlp.down_proj
- model.layers.22.mlp.down_proj
- model.layers.23.mlp.down_proj
- model.layers.3.mlp.down_proj
- model.layers.21.mlp.down_proj
- model.layers.5.mlp.down_proj
- model.layers.28.mlp.down_proj
- model.layers.20.mlp.down_proj
- model.layers.26.mlp.down_proj
- model.layers.19.mlp.down_proj
- model.layers.34.mlp.down_proj
# mlp.gate_proj layers
- model.layers.2.mlp.gate_proj
- model.layers.1.mlp.gate_proj
- model.layers.3.mlp.gate_proj
- model.layers.5.mlp.gate_proj
- model.layers.4.mlp.gate_proj
- model.layers.35.mlp.gate_proj
- model.layers.36.mlp.gate_proj
- model.layers.37.mlp.gate_proj
- model.layers.38.mlp.gate_proj
- model.layers.34.mlp.gate_proj
- model.layers.33.mlp.gate_proj
- model.layers.8.mlp.gate_proj
- model.layers.32.mlp.gate_proj
- model.layers.6.mlp.gate_proj
- model.layers.28.mlp.gate_proj
- model.layers.26.mlp.gate_proj
- model.layers.30.mlp.gate_proj
- model.layers.23.mlp.gate_proj
- model.layers.29.mlp.gate_proj
- model.layers.27.mlp.gate_proj
# mlp.up_proj layers
- model.layers.3.mlp.up_proj
- model.layers.4.mlp.up_proj
- model.layers.6.mlp.up_proj
- model.layers.2.mlp.up_proj
- model.layers.5.mlp.up_proj
- model.layers.8.mlp.up_proj
- model.layers.10.mlp.up_proj
- model.layers.9.mlp.up_proj
- model.layers.7.mlp.up_proj
- model.layers.0.mlp.up_proj
- model.layers.17.mlp.up_proj
- model.layers.15.mlp.up_proj
- model.layers.22.mlp.up_proj
- model.layers.18.mlp.up_proj
- model.layers.16.mlp.up_proj
- model.layers.11.mlp.up_proj
- model.layers.21.mlp.up_proj
- model.layers.23.mlp.up_proj
- model.layers.20.mlp.up_proj
- model.layers.27.mlp.up_proj
# self_attn.k_proj layers
- model.layers.30.self_attn.k_proj
- model.layers.27.self_attn.k_proj
- model.layers.25.self_attn.k_proj
- model.layers.33.self_attn.k_proj
- model.layers.26.self_attn.k_proj
- model.layers.31.self_attn.k_proj
- model.layers.35.self_attn.k_proj
- model.layers.39.self_attn.k_proj
- model.layers.22.self_attn.k_proj
- model.layers.24.self_attn.k_proj
- model.layers.21.self_attn.k_proj
- model.layers.28.self_attn.k_proj
- model.layers.23.self_attn.k_proj
- model.layers.36.self_attn.k_proj
- model.layers.20.self_attn.k_proj
- model.layers.37.self_attn.k_proj
- model.layers.29.self_attn.k_proj
- model.layers.32.self_attn.k_proj
- model.layers.16.self_attn.k_proj
- model.layers.18.self_attn.k_proj
# self_attn.o_proj layers
- model.layers.7.self_attn.o_proj
- model.layers.6.self_attn.o_proj
- model.layers.9.self_attn.o_proj
- model.layers.5.self_attn.o_proj
- model.layers.27.self_attn.o_proj
- model.layers.26.self_attn.o_proj
- model.layers.4.self_attn.o_proj
- model.layers.31.self_attn.o_proj
- model.layers.8.self_attn.o_proj
- model.layers.16.self_attn.o_proj
- model.layers.3.self_attn.o_proj
- model.layers.10.self_attn.o_proj
- model.layers.18.self_attn.o_proj
- model.layers.33.self_attn.o_proj
- model.layers.17.self_attn.o_proj
- model.layers.32.self_attn.o_proj
- model.layers.30.self_attn.o_proj
- model.layers.2.self_attn.o_proj
- model.layers.15.self_attn.o_proj
- model.layers.11.self_attn.o_proj
# self_attn.q_proj layers
- model.layers.14.self_attn.q_proj
- model.layers.11.self_attn.q_proj
- model.layers.15.self_attn.q_proj
- model.layers.9.self_attn.q_proj
- model.layers.8.self_attn.q_proj
- model.layers.18.self_attn.q_proj
- model.layers.12.self_attn.q_proj
- model.layers.13.self_attn.q_proj
- model.layers.19.self_attn.q_proj
- model.layers.16.self_attn.q_proj
- model.layers.10.self_attn.q_proj
- model.layers.17.self_attn.q_proj
- model.layers.7.self_attn.q_proj
- model.layers.5.self_attn.q_proj
- model.layers.20.self_attn.q_proj
- model.layers.3.self_attn.q_proj
- model.layers.26.self_attn.q_proj
- model.layers.27.self_attn.q_proj
- model.layers.28.self_attn.q_proj
- model.layers.33.self_attn.q_proj
# self_attn.v_proj layers
- model.layers.27.self_attn.v_proj
- model.layers.20.self_attn.v_proj
- model.layers.24.self_attn.v_proj
- model.layers.25.self_attn.v_proj
- model.layers.30.self_attn.v_proj
- model.layers.2.self_attn.v_proj
- model.layers.23.self_attn.v_proj
- model.layers.22.self_attn.v_proj
- model.layers.26.self_attn.v_proj
- model.layers.33.self_attn.v_proj
- model.layers.37.self_attn.v_proj
- model.layers.7.self_attn.v_proj
- model.layers.4.self_attn.v_proj
- model.layers.18.self_attn.v_proj
- model.layers.31.self_attn.v_proj
- model.layers.17.self_attn.v_proj
- model.layers.35.self_attn.v_proj
- model.layers.32.self_attn.v_proj
- model.layers.21.self_attn.v_proj
- model.layers.3.self_attn.v_proj

dataset_prepared_path:  /workspace/axolotl/dolph-2.9.3-nemo-prepared
val_set_size: 0.01
output_dir: /workspace/axolotl/dolphin-2.9.3-mistral-nemo

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true

wandb_project: dolphin-2.9.3-Mistral-nemo
wandb_watch:
wandb_run_id:
wandb_log_model:

gradient_accumulation_steps: 16
micro_batch_size: 1
num_epochs: 3
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 5e-6
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32:

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 100
# evals_per_epoch: 4
eval_table_size:
saves_per_epoch: 1
save_total_limit: 2
save_steps:
debug:
deepspeed: deepspeed_configs/zero3_bf16.json
weight_decay: 0.1
special_tokens:
  eos_token: "<|im_end|>"
  pad_token: "<pad>"
  bos_token: "<s>"
  unk_token: "<unk>"
tokens:
  - "<|im_start|>"

# fsdp:
#   - full_shard
#   - auto_wrap
# fsdp_config:
#   fsdp_limit_all_gathers: true
#   fsdp_sync_module_states: true
#   fsdp_offload_params: true
#   fsdp_use_orig_params: false
#   fsdp_cpu_ram_efficient_loading: true
#   fsdp_transformer_layer_cls_to_wrap: MixtralSparseMoeBlock
#   fsdp_state_dict_type: FULL_STATE_DICT
#   fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
#   fsdp_sharding_strategy: FULL_SHARD
#   fsdp_forward_prefetch: false
#   fsdp_backward_prefetch: BACKWARD_PRE
```

</details><br>

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/ehartford/dolphin-2.9.3-Mistral-nemo/runs/c23odyoj)
# workspace/axolotl/dolphin-2.9.3-mistral-nemo

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5605

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.5691        | 1.0162 | 983  | 0.5734          |
| 0.5335        | 2.0174 | 1968 | 0.5609          |
| 0.5297        | 2.9639 | 2901 | 0.5605          |

### Framework versions

- Transformers 4.43.0.dev0
- Pytorch 2.2.2+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1

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