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---
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
- microsoft/orca-math-word-problems-200k
- abacusai/SystemChat-1.1
- Locutusque/function-calling-chatml
- internlm/Agent-FLAN
language:
- en
---
# Dolphin 2.9.1 Mixtral 1x22b 🐬
Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations
[](https://discord.gg/cognitivecomputations)
Discord: https://discord.gg/cognitivecomputations
<img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/ldkN1J0WIDQwU4vutGYiD.png" width="600" />
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 16k sequence length.
It took 27 hours on 8xH100 provided by Crusoe Cloud.
This model was fully fine-tuned, targeting all layers.
The model is an extracted expert using SLERP and a custom script that we've open-sourced (I'll provide the link to the GitHub). It extracts a single expert which is the combined SLERP of all 8 experts from a Mixtral architecture. We decided to not fully convert to a dense model, for the sake of trying to keep as much of the original model's performance as possible, as this process is already quite surgical and there are a lot of variables to take into account.
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. 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 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
[<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: cognitivecomputations/mixtral-1x22b-base
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
# trust_remote_code: true
# load_in_8bit: true
# load_in_4bit: true
# strict: false
datasets:
- path: /workspace/datasets/dolphin-2.9/dolphin201-sharegpt2.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
chat_template: chatml
dataset_prepared_path: yi34b-prepared
val_set_size: 0.01
output_dir: ./1x22b-out
# adapter: qlora
# lora_r: 16
# 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 layers
# - model.layers.0.input_layernorm
# - model.layers.1.input_layernorm
# - model.layers.2.input_layernorm
# - model.layers.3.input_layernorm
# - model.layers.4.input_layernorm
# - model.layers.5.input_layernorm
# - model.layers.6.input_layernorm
# - model.layers.7.input_layernorm
# - model.layers.8.input_layernorm
# - model.layers.9.input_layernorm
# - model.layers.10.input_layernorm
# - model.layers.11.input_layernorm
# - model.layers.12.input_layernorm
# - model.layers.13.input_layernorm
# - model.layers.14.input_layernorm
# - model.layers.15.input_layernorm
# - model.layers.16.input_layernorm
# - model.layers.17.input_layernorm
# - model.layers.18.input_layernorm
# - model.layers.19.input_layernorm
# - model.layers.20.input_layernorm
# - model.layers.21.input_layernorm
# - model.layers.22.input_layernorm
# - model.layers.23.input_layernorm
# # lm_head layers
# # mlp.down_proj layers
# - model.layers.17.mlp.down_proj
# - model.layers.18.mlp.down_proj
# - model.layers.19.mlp.down_proj
# - model.layers.20.mlp.down_proj
# - model.layers.21.mlp.down_proj
# - model.layers.22.mlp.down_proj
# - model.layers.23.mlp.down_proj
# - model.layers.24.mlp.down_proj
# - model.layers.25.mlp.down_proj
# - model.layers.26.mlp.down_proj
# - model.layers.27.mlp.down_proj
# - model.layers.28.mlp.down_proj
# - model.layers.29.mlp.down_proj
# - model.layers.30.mlp.down_proj
# - model.layers.31.mlp.down_proj
# - model.layers.32.mlp.down_proj
# - model.layers.33.mlp.down_proj
# - model.layers.34.mlp.down_proj
# - model.layers.35.mlp.down_proj
# - model.layers.36.mlp.down_proj
# - model.layers.37.mlp.down_proj
# - model.layers.38.mlp.down_proj
# - model.layers.39.mlp.down_proj
# - model.layers.40.mlp.down_proj
# # mlp.gate_proj layers
# - model.layers.51.mlp.gate_proj
# - model.layers.50.mlp.gate_proj
# - model.layers.53.mlp.gate_proj
# - model.layers.52.mlp.gate_proj
# - model.layers.49.mlp.gate_proj
# - model.layers.45.mlp.gate_proj
# - model.layers.46.mlp.gate_proj
# - model.layers.47.mlp.gate_proj
# - model.layers.57.mlp.gate_proj
# - model.layers.48.mlp.gate_proj
# - model.layers.56.mlp.gate_proj
# - model.layers.41.mlp.gate_proj
# - model.layers.54.mlp.gate_proj
# - model.layers.43.mlp.gate_proj
# - model.layers.44.mlp.gate_proj
# - model.layers.60.mlp.gate_proj
# - model.layers.55.mlp.gate_proj
# - model.layers.40.mlp.gate_proj
# - model.layers.42.mlp.gate_proj
# - model.layers.58.mlp.gate_proj
# - model.layers.36.mlp.gate_proj
# - model.layers.37.mlp.gate_proj
# - model.layers.38.mlp.gate_proj
# - model.layers.39.mlp.gate_proj
# # mlp.up_proj layers
# - model.layers.50.mlp.up_proj
# - model.layers.51.mlp.up_proj
# - model.layers.41.mlp.up_proj
# - model.layers.49.mlp.up_proj
# - model.layers.43.mlp.up_proj
# - model.layers.44.mlp.up_proj
# - model.layers.40.mlp.up_proj
# - model.layers.45.mlp.up_proj
# - model.layers.47.mlp.up_proj
# - model.layers.48.mlp.up_proj
# - model.layers.46.mlp.up_proj
# - model.layers.42.mlp.up_proj
# - model.layers.39.mlp.up_proj
# - model.layers.36.mlp.up_proj
# - model.layers.37.mlp.up_proj
# - model.layers.38.mlp.up_proj
# - model.layers.56.mlp.up_proj
# - model.layers.57.mlp.up_proj
# - model.layers.53.mlp.up_proj
# - model.layers.31.mlp.up_proj
# - model.layers.32.mlp.up_proj
# - model.layers.34.mlp.up_proj
# - model.layers.35.mlp.up_proj
# - model.layers.33.mlp.up_proj
# # model.embed_tokens layers
# # model.norm layers
# # post_attention_layernorm layers
# - model.layers.0.post_attention_layernorm
# - model.layers.1.post_attention_layernorm
# - model.layers.2.post_attention_layernorm
# - model.layers.3.post_attention_layernorm
# - model.layers.4.post_attention_layernorm
# - model.layers.5.post_attention_layernorm
# - model.layers.6.post_attention_layernorm
# - model.layers.7.post_attention_layernorm
# - model.layers.8.post_attention_layernorm
# - model.layers.9.post_attention_layernorm
# - model.layers.10.post_attention_layernorm
# - model.layers.11.post_attention_layernorm
# - model.layers.12.post_attention_layernorm
# - model.layers.13.post_attention_layernorm
# - model.layers.14.post_attention_layernorm
# - model.layers.15.post_attention_layernorm
# - model.layers.16.post_attention_layernorm
# - model.layers.17.post_attention_layernorm
# - model.layers.18.post_attention_layernorm
# - model.layers.19.post_attention_layernorm
# - model.layers.20.post_attention_layernorm
# - model.layers.21.post_attention_layernorm
# - model.layers.22.post_attention_layernorm
# - model.layers.23.post_attention_layernorm
# # self_attn.k_proj layers
# - model.layers.42.self_attn.k_proj
# - model.layers.41.self_attn.k_proj
# - model.layers.39.self_attn.k_proj
# - model.layers.35.self_attn.k_proj
# - model.layers.28.self_attn.k_proj
# - model.layers.79.self_attn.k_proj
# - model.layers.43.self_attn.k_proj
# - model.layers.32.self_attn.k_proj
# - model.layers.73.self_attn.k_proj
# - model.layers.31.self_attn.k_proj
# - model.layers.29.self_attn.k_proj
# - model.layers.76.self_attn.k_proj
# - model.layers.30.self_attn.k_proj
# - model.layers.40.self_attn.k_proj
# - model.layers.33.self_attn.k_proj
# - model.layers.78.self_attn.k_proj
# - model.layers.34.self_attn.k_proj
# - model.layers.37.self_attn.k_proj
# - model.layers.45.self_attn.k_proj
# - model.layers.44.self_attn.k_proj
# - model.layers.71.self_attn.k_proj
# - model.layers.26.self_attn.k_proj
# - model.layers.74.self_attn.k_proj
# - model.layers.27.self_attn.k_proj
# # self_attn.o_proj layers
# - model.layers.35.self_attn.o_proj
# - model.layers.34.self_attn.o_proj
# - model.layers.37.self_attn.o_proj
# - model.layers.33.self_attn.o_proj
# - model.layers.31.self_attn.o_proj
# - model.layers.27.self_attn.o_proj
# - model.layers.38.self_attn.o_proj
# - model.layers.24.self_attn.o_proj
# - model.layers.39.self_attn.o_proj
# - model.layers.43.self_attn.o_proj
# - model.layers.29.self_attn.o_proj
# - model.layers.0.self_attn.o_proj
# - model.layers.50.self_attn.o_proj
# - model.layers.32.self_attn.o_proj
# - model.layers.45.self_attn.o_proj
# - model.layers.30.self_attn.o_proj
# - model.layers.60.self_attn.o_proj
# - model.layers.23.self_attn.o_proj
# - model.layers.18.self_attn.o_proj
# - model.layers.67.self_attn.o_proj
# - model.layers.57.self_attn.o_proj
# - model.layers.20.self_attn.o_proj
# - model.layers.76.self_attn.o_proj
# - model.layers.28.self_attn.o_proj
# # self_attn.q_proj layers
# - model.layers.1.self_attn.q_proj
# - model.layers.6.self_attn.q_proj
# - model.layers.0.self_attn.q_proj
# - model.layers.5.self_attn.q_proj
# - model.layers.2.self_attn.q_proj
# - model.layers.7.self_attn.q_proj
# - model.layers.3.self_attn.q_proj
# - model.layers.4.self_attn.q_proj
# - model.layers.8.self_attn.q_proj
# - model.layers.9.self_attn.q_proj
# - model.layers.61.self_attn.q_proj
# - model.layers.10.self_attn.q_proj
# - model.layers.62.self_attn.q_proj
# - model.layers.36.self_attn.q_proj
# - model.layers.15.self_attn.q_proj
# - model.layers.11.self_attn.q_proj
# - model.layers.17.self_attn.q_proj
# - model.layers.60.self_attn.q_proj
# - model.layers.63.self_attn.q_proj
# - model.layers.64.self_attn.q_proj
# - model.layers.29.self_attn.q_proj
# - model.layers.30.self_attn.q_proj
# - model.layers.55.self_attn.q_proj
# - model.layers.34.self_attn.q_proj
# # self_attn.v_proj layers
# - model.layers.12.self_attn.v_proj
# - model.layers.16.self_attn.v_proj
# - model.layers.18.self_attn.v_proj
# - model.layers.19.self_attn.v_proj
# - model.layers.20.self_attn.v_proj
# - model.layers.21.self_attn.v_proj
# - model.layers.22.self_attn.v_proj
# - model.layers.23.self_attn.v_proj
# - model.layers.24.self_attn.v_proj
# - model.layers.25.self_attn.v_proj
# - model.layers.26.self_attn.v_proj
# - model.layers.27.self_attn.v_proj
# - model.layers.28.self_attn.v_proj
# - model.layers.29.self_attn.v_proj
# - model.layers.30.self_attn.v_proj
# - model.layers.31.self_attn.v_proj
# - model.layers.32.self_attn.v_proj
# - model.layers.33.self_attn.v_proj
# - model.layers.34.self_attn.v_proj
# - model.layers.35.self_attn.v_proj
# - model.layers.36.self_attn.v_proj
# - model.layers.37.self_attn.v_proj
# - model.layers.38.self_attn.v_proj
# - model.layers.39.self_attn.v_proj
sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true
# adapter: lora
# lora_model_dir:
# lora_r: 32
# lora_alpha: 16
# lora_dropout: 0.05
# lora_target_linear: true
# lora_fan_in_fan_out:
wandb_project: dolphin-mixtral1x22b
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 3
optimizer: adamw_8bit
lr_scheduler: cosine
learning_rate: 1e-5
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint: /workspace/axolotl2/axolotl/1x22b-out/checkpoint-507
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 4
save_total_limit: 2
debug:
deepspeed: /workspace/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.01
fsdp:
fsdp_config:
special_tokens:
eos_token: "<|im_end|>"
bos_token: "<s>"
# pad_token: "<unk>"
unk_token: "<unk>"
tokens:
- "<|im_start|>"
```
</details><br>
# 1x22b-out
## 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: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- 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: 10
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.9818 | 0.0015 | 1 | 0.9854 |
| 0.4783 | 0.2499 | 169 | 0.5042 |
| 0.464 | 0.4997 | 338 | 0.4755 |
| 0.4561 | 0.7496 | 507 | 0.4593 |
| 0.3981 | 0.9994 | 676 | 0.4553 |
| 0.3725 | 1.2378 | 845 | 0.4525 |
| 0.3624 | 1.4877 | 1014 | 0.4457 |
| 0.359 | 1.7376 | 1183 | 0.4393 |
| 0.375 | 1.9874 | 1352 | 0.4345 |
| 0.2899 | 2.2260 | 1521 | 0.4488 |
| 0.2848 | 2.4759 | 1690 | 0.4473 |
| 0.2935 | 2.7257 | 1859 | 0.4470 |
| 0.2065 | 2.9756 | 2028 | 0.4572 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|