shisa-ai/shisa-v1-mistral0.3-7b

This abalation underperforms the tried and true augmxnt/shisa-gamma-7b-v1 and if you're looking for a Mistral 7B based model, you should probably go with that.

Performance

Measured using a fork of Lightblue's Shaberi benchmark framework:

Model	Average	ELYZA-tasks-100	MT-Bench	Rakuda	Tengu-Bench
gpt-4-turbo-2024-04-09	8.75	8.78	8.74	9.18	8.31
gpt-4o-2024-05-13	8.72	8.88	8.69	9.15	8.16
gemini-1.5-pro	8.58	8.58	8.93	9.20	7.61
claude-3-opus-20240229	8.55	8.64	8.58	8.75	8.23
CohereForAI/c4ai-command-r-plus	7.69	7.50	7.43	9.05	6.79
shisa-ai/shisa-v1-llama3-70b	7.30	7.34	7.67	8.15	6.04
gpt-3.5-turbo-0125	7.17	7.24	6.98	7.64	6.82
shisa-ai/shisa-v1-llama3-70b.2e5	7.17	7.16	7.45	7.98	6.09
karakuri-ai/karakuri-lm-8x7b-chat-v0.1	7.00	7.18	6.30	7.98	6.55
karakuri-ai/karakuri-lm-70b-chat-v0.1	6.84	6.86	6.43	7.85	6.23
lightblue/ao-karasu-72B	6.81	7.19	6.54	7.25	6.27
shisa-ai/shisa-v1-llama3-8b	6.59	6.67	6.95	7.05	5.68
microsoft/Phi-3-medium-128k-instruct	6.48	7.10	5.92	6.84	6.04
shisa-ai/shisa-swallowmx-13a47b-v1	6.17	6.48	6.07	7.11	5.03
lightblue/suzume-llama-3-8B-japanese	5.96	6.68	4.96	6.68	5.53
augmxnt/shisa-gamma-7b-v1	5.82	5.96	5.02	6.85	5.47
shisa-ai/shisa-v1-phi3-14b	5.77	6.28	5.26	6.55	5.01
shisa-ai/shisa-v1-gemma-8b	5.64	6.50	5.42	5.10	5.55
Rakuten/RakutenAI-7B-chat	5.58	5.92	4.60	6.58	5.24
lightblue/qarasu-14B-chat-plus-unleashed	5.20	5.58	4.74	5.46	5.01
shisa-ai/shisa-v1-mistral0.3-7b	5.11	5.64	6.10	3.83	4.86
cyberagent/calm2-7b-chat	4.76	4.90	3.58	5.75	4.81
mistralai/Mistral-7B-Instruct-v0.2	4.69	5.78	4.65	3.80	4.53
shisa-ai/shisa-v1-yi1.5-9b	4.63	5.98	4.28	3.26	5.00
augmxnt/shisa-7b-v1	4.50	4.63	3.95	4.89	4.53

See axolotl config

axolotl version: 0.4.0

base_model: mistralai/Mistral-7B-Instruct-v0.3
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

chat_template: inst
datasets:
  - path: augmxnt/ultra-orca-boros-en-ja-v1
    type: sharegpt
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./outputs/mistral

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: false

use_wandb: true
wandb_project: shisa-v2
wandb_entity: augmxnt
wandb_name: shisa-v1-mistral0.3-7b

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 3
optimizer: paged_adamw_8bit
lr_scheduler: linear
learning_rate: 8e-6

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: 100
evals_per_epoch: 2
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed: zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:

outputs/mistral

This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.3 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.3791

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: 8e-06
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• distributed_type: multi-GPU
• num_devices: 8
• gradient_accumulation_steps: 4
• total_train_batch_size: 64
• total_eval_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 100
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
0.8564	0.0045	1	0.7107
0.6131	0.5023	111	0.4259
0.6077	1.0045	222	0.3715
0.4173	1.4932	333	0.3617
0.3812	1.9955	444	0.3468
0.2408	2.4842	555	0.3791

Framework versions

• Transformers 4.40.2
• Pytorch 2.3.0+cu121
• Datasets 2.19.1
• Tokenizers 0.19.1