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---
license: apache-2.0
base_model: mistral-community/Mixtral-8x22B-v0.1
tags:
- generated_from_trainer
model-index:
- name: out
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<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.1.block_sparse_moe.experts.0.w1
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- model.layers.11.block_sparse_moe.experts.0.w1
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- model.layers.13.block_sparse_moe.experts.0.w1
- model.layers.0.block_sparse_moe.experts.0.w2
- model.layers.1.block_sparse_moe.experts.0.w2
- model.layers.2.block_sparse_moe.experts.0.w2
- model.layers.3.block_sparse_moe.experts.0.w2
- model.layers.4.block_sparse_moe.experts.0.w2
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- model.layers.0.block_sparse_moe.experts.0.w3
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- model.layers.13.block_sparse_moe.experts.0.w3
- model.layers.0.block_sparse_moe.experts.1.w1
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- model.layers.13.block_sparse_moe.experts.1.w1
- model.layers.40.block_sparse_moe.experts.1.w2
- model.layers.0.block_sparse_moe.experts.1.w2
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- model.layers.12.block_sparse_moe.experts.1.w3
- model.layers.13.block_sparse_moe.experts.1.w3
- model.layers.1.block_sparse_moe.experts.2.w1
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- model.layers.2.block_sparse_moe.experts.6.w3
- model.layers.1.block_sparse_moe.experts.6.w3
- model.layers.0.block_sparse_moe.experts.6.w3
- model.layers.6.block_sparse_moe.experts.6.w3
- model.layers.7.block_sparse_moe.experts.6.w3
- model.layers.8.block_sparse_moe.experts.6.w3
- model.layers.9.block_sparse_moe.experts.6.w3
- model.layers.10.block_sparse_moe.experts.6.w3
- model.layers.11.block_sparse_moe.experts.6.w3
- model.layers.12.block_sparse_moe.experts.6.w3
- model.layers.13.block_sparse_moe.experts.6.w3
- model.layers.5.block_sparse_moe.experts.7.w1
- model.layers.6.block_sparse_moe.experts.7.w1
- model.layers.3.block_sparse_moe.experts.7.w1
- model.layers.4.block_sparse_moe.experts.7.w1
- model.layers.2.block_sparse_moe.experts.7.w1
- model.layers.0.block_sparse_moe.experts.7.w1
- model.layers.7.block_sparse_moe.experts.7.w1
- model.layers.8.block_sparse_moe.experts.7.w1
- model.layers.9.block_sparse_moe.experts.7.w1
- model.layers.10.block_sparse_moe.experts.7.w1
- model.layers.11.block_sparse_moe.experts.7.w1
- model.layers.12.block_sparse_moe.experts.7.w1
- model.layers.13.block_sparse_moe.experts.7.w1
- model.layers.14.block_sparse_moe.experts.7.w1
- model.layers.6.block_sparse_moe.experts.7.w2
- model.layers.5.block_sparse_moe.experts.7.w2
- model.layers.4.block_sparse_moe.experts.7.w2
- model.layers.2.block_sparse_moe.experts.7.w2
- model.layers.3.block_sparse_moe.experts.7.w2
- model.layers.1.block_sparse_moe.experts.7.w2
- model.layers.0.block_sparse_moe.experts.7.w2
- model.layers.7.block_sparse_moe.experts.7.w2
- model.layers.8.block_sparse_moe.experts.7.w2
- model.layers.9.block_sparse_moe.experts.7.w2
- model.layers.10.block_sparse_moe.experts.7.w2
- model.layers.11.block_sparse_moe.experts.7.w2
- model.layers.12.block_sparse_moe.experts.7.w2
- model.layers.13.block_sparse_moe.experts.7.w2
- model.layers.6.block_sparse_moe.experts.7.w3
- model.layers.5.block_sparse_moe.experts.7.w3
- model.layers.4.block_sparse_moe.experts.7.w3
- model.layers.3.block_sparse_moe.experts.7.w3
- model.layers.2.block_sparse_moe.experts.7.w3
- model.layers.0.block_sparse_moe.experts.7.w3
- model.layers.7.block_sparse_moe.experts.7.w3
- model.layers.8.block_sparse_moe.experts.7.w3
- model.layers.9.block_sparse_moe.experts.7.w3
- model.layers.10.block_sparse_moe.experts.7.w3
- model.layers.11.block_sparse_moe.experts.7.w3
- model.layers.12.block_sparse_moe.experts.7.w3
- model.layers.13.block_sparse_moe.experts.7.w3
- model.layers.14.block_sparse_moe.experts.7.w3
# - model.layers.0.block_sparse_moe.gate
# - model.layers.1.block_sparse_moe.gate
# - model.layers.2.block_sparse_moe.gate
# - model.layers.3.block_sparse_moe.gate
# - model.layers.4.block_sparse_moe.gate
# - model.layers.5.block_sparse_moe.gate
# - model.layers.6.block_sparse_moe.gate
# - model.layers.7.block_sparse_moe.gate
# - model.layers.8.block_sparse_moe.gate
# - model.layers.9.block_sparse_moe.gate
# - 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>
# out
This model is a fine-tuned version of [mistral-community/Mixtral-8x22B-v0.1](https://huggingface.co/mistral-community/Mixtral-8x22B-v0.1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5217
## 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: 2.7e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 2
- num_epochs: 3
### 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