---
license: apache-2.0
base_model: mistral-community/Mixtral-8x22B-v0.1
tags:
- generated_from_trainer
- axolotl
model-index:
- name: out
  results: []
datasets:
- cognitivecomputations/Dolphin-2.9
- teknium/OpenHermes-2.5
- m-a-p/CodeFeedback-Filtered-Instruction
- cognitivecomputations/dolphin-coder
- cognitivecomputations/samantha-data
- HuggingFaceH4/ultrachat_200k
- microsoft/orca-math-word-problems-200k
- abacusai/SystemChat-1.1
- Locutusque/function-calling-chatml
- internlm/Agent-FLAN
language:
- en
---

# Dolphin 2.9 Mixtral 8x22b 🐬

Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations

Discord: https://discord.gg/8fbBeC7ZGx

<img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/ldkN1J0WIDQwU4vutGYiD.png" width="600" />

A bug has been found in the Dolphin 2.9 dataset in SystemConversations that causes the model to overly talk about the "SYSTEM MESSAGE".  To counter this, we recommend you add a statement in the system message directing the model not to mention the system message. An example system message is "The assistant is named Dolphin.  A helpful and friendly AI assistant, Dolphin avoids discussing the system message unless directly asked about it."

My appreciation for the sponsors of Dolphin 2.9:
- [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 8xH100 node

This model is based on Dolphin-2.9-Mixtral-8x22b, and is Apache-2.0 licensed.

The base model has 64k context, and the full-weight fine-tuning was with 4k sequence length.

It took 1 week on 8xH100 provided by Crusoe Cloud

This model was trained FFT on 50% parameters (targeted with [Laser Scanner](https://github.com/cognitivecomputations/laserRMT/blob/main/laser_scanner.py) by Fernando Fernandes, David Golchinfar, Lucas Atkins, and Eric Hartford) , using 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 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling.

Dolphin is uncensored. I 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 Apache 2.0.  I grant permission for any use, including commercial, that falls within accordance with Apache-2.0 license.  Dolphin was trained on data generated from GPT4, among other models.

## Evals

![image/png](https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/Nb6f_dS_M6fN_v2ACK98x.png)

## Training

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

axolotl version: `0.4.0`
```yaml
base_model: mistral-community/Mixtral-8x22B-v0.1
model_type: AutoModelForCausalLM
tokenizer_type: LlamaTokenizer
trust_remote_code: true

load_in_8bit: false
load_in_4bit: false
strict: false

unfrozen_parameters:
  - ^lm_head.weight$
  - ^model.embed_tokens.weight$
  - model.layers.0.self_attn.q_proj
  - model.layers.1.self_attn.q_proj
  - model.layers.2.self_attn.q_proj
  - model.layers.22.self_attn.q_proj
  - model.layers.27.self_attn.q_proj
  - model.layers.28.self_attn.q_proj
  - model.layers.13.self_attn.q_proj
  - model.layers.21.self_attn.q_proj
  - model.layers.24.self_attn.q_proj
  - model.layers.14.self_attn.q_proj
  - model.layers.15.self_attn.q_proj
  - model.layers.11.self_attn.q_proj
  - model.layers.20.self_attn.q_proj
  - model.layers.23.self_attn.q_proj
  - model.layers.30.self_attn.k_proj
  - model.layers.31.self_attn.k_proj
  - model.layers.25.self_attn.k_proj
  - model.layers.23.self_attn.k_proj
  - model.layers.27.self_attn.k_proj
  - model.layers.26.self_attn.k_proj
  - model.layers.29.self_attn.k_proj
  - model.layers.28.self_attn.k_proj
  - model.layers.24.self_attn.k_proj
  - model.layers.16.self_attn.k_proj
  - model.layers.19.self_attn.k_proj
  - model.layers.22.self_attn.k_proj
  - model.layers.20.self_attn.k_proj
  - model.layers.6.self_attn.k_proj
  - model.layers.22.self_attn.v_proj
  - model.layers.29.self_attn.v_proj
  - model.layers.31.self_attn.v_proj
  - model.layers.5.self_attn.v_proj
  - model.layers.8.self_attn.v_proj
  - model.layers.4.self_attn.v_proj
  - model.layers.25.self_attn.v_proj
  - model.layers.30.self_attn.v_proj
  - model.layers.17.self_attn.v_proj
  - model.layers.23.self_attn.v_proj
  - model.layers.28.self_attn.v_proj
  - model.layers.9.self_attn.v_proj
  - model.layers.26.self_attn.v_proj
  - model.layers.27.self_attn.v_proj
  - model.layers.20.self_attn.o_proj
  - model.layers.19.self_attn.o_proj
  - model.layers.16.self_attn.o_proj
  - model.layers.13.self_attn.o_proj
  - model.layers.18.self_attn.o_proj
  - model.layers.17.self_attn.o_proj
  - model.layers.12.self_attn.o_proj
  - model.layers.15.self_attn.o_proj
  - model.layers.14.self_attn.o_proj
  - model.layers.22.self_attn.o_proj
  - model.layers.23.self_attn.o_proj
  - model.layers.21.self_attn.o_proj
  - model.layers.10.self_attn.o_proj
  - model.layers.0.self_attn.o_proj
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  - model.layers.11.block_sparse_moe.experts.6.w1
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  - model.layers.13.block_sparse_moe.experts.6.w1
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  - model.layers.13.block_sparse_moe.experts.6.w3
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  - model.layers.10.block_sparse_moe.gate
  - model.layers.11.block_sparse_moe.gate
  - model.layers.12.block_sparse_moe.gate
  - model.layers.13.block_sparse_moe.gate
  
model_config:
  output_router_logits: true

datasets:
  - path: /workspace/datasets/dolphin-2.9/dolphin201-sharegpt2.jsonl
    type: sharegpt
    conversation: chatml
  - path: /workspace/datasets/dolphin-2.9/Ultrachat200kunfiltered.jsonl
    type: sharegpt
    conversation: chatml
  - path: /workspace/datasets/dolphin-2.9/dolphin-coder-translate-sharegpt2.jsonl
    type: sharegpt
    conversation: chatml
  - path: /workspace/datasets/dolphin-2.9/dolphin-coder-codegen-sharegpt2.jsonl
    type: sharegpt
    conversation: chatml
  - path: /workspace/datasets/dolphin-2.9/m-a-p_Code-Feedback-sharegpt-unfiltered.jsonl
    type: sharegpt
    conversation: chatml
  - path: /workspace/datasets/dolphin-2.9/m-a-p_CodeFeedback-Filtered-Instruction-sharegpt-unfiltered.jsonl
    type: sharegpt
    conversation: chatml
  - path: /workspace/datasets/dolphin-2.9/not_samantha_norefusals.jsonl
    type: sharegpt
    conversation: chatml
  - path: /workspace/datasets/dolphin-2.9/Orca-Math-resort-unfiltered.jsonl
    type: sharegpt
    conversation: chatml
  - path: /workspace/datasets/dolphin-2.9/agent_instruct_react_unfiltered.jsonl
    type: sharegpt  
    conversation: chatml
  - path: /workspace/datasets/dolphin-2.9/toolbench_instruct_j1s1_3k_unfiltered.jsonl
    type: sharegpt  
    conversation: chatml
  - path: /workspace/datasets/dolphin-2.9/toolbench_negative_unfiltered.jsonl
    type: sharegpt
    conversation: chatml
  - path: /workspace/datasets/dolphin-2.9/toolbench_react_10p_unfiltered.jsonl
    type: sharegpt
    conversation: chatml
  - path: /workspace/datasets/dolphin-2.9/toolbench_tflan_cot_30p_unfiltered.jsonl
    type: sharegpt
    conversation: chatml
  - path: /workspace/datasets/dolphin-2.9/openhermes200k_unfiltered.jsonl
    type: sharegpt 
    conversation: chatml
  - path: /workspace/datasets/dolphin-2.9/SystemConversations.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: thingy
val_set_size: 0.0002
output_dir: ./out

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 8
micro_batch_size: 4
num_epochs: 3
logging_steps: 1
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 2.7e-5

wandb_project: dolphin-2.9-mixtral-8x22b
wandb_watch:
wandb_run_id:
wandb_log_model:

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

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
# resume_from_checkpoint: /home/ehartford/axolotl/out/checkpoint-316
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 8
save_total_limit: 2
save_steps:
evals_per_epoch: 4
eval_sample_packing: false
debug:
deepspeed: deepspeed_configs/zero3_bf16_cpuoffload_params.json
weight_decay: 0.05
fsdp:
fsdp_config:
special_tokens:
  eos_token: "<|im_end|>"
tokens:
  - "<|im_start|>"
```

</details><br>

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.7022        | 0.0   | 1    | 0.6989          |
| 0.5344        | 0.25  | 238  | 0.5138          |
| 0.5204        | 0.5   | 476  | 0.5018          |
| 0.5059        | 0.75  | 714  | 0.4951          |
| 0.5112        | 1.0   | 952  | 0.4911          |
| 0.4561        | 1.24  | 1190 | 0.4978          |
| 0.478         | 1.49  | 1428 | 0.4935          |
| 0.4714        | 1.74  | 1666 | 0.4899          |
| 0.4626        | 1.99  | 1904 | 0.4861          |
| 0.3675        | 2.22  | 2142 | 0.5240          |
| 0.3595        | 2.47  | 2380 | 0.5229          |
| 0.3438        | 2.72  | 2618 | 0.5217          |


### Framework versions

- Transformers 4.40.0.dev0
- Pytorch 2.2.2+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0