RichardErkhov/skymizer_-_mistral-7B-v0.1-csft-relufication-stage-1-on-slim-orca-gguf
Quantization made by Richard Erkhov.
mistral-7B-v0.1-csft-relufication-stage-1-on-slim-orca - GGUF
- Model creator: https://huggingface.co/skymizer/
- Original model: https://huggingface.co/skymizer/mistral-7B-v0.1-csft-relufication-stage-1-on-slim-orca/
Original model description: --- libraryname: transformers license: apache-2.0 basemodel: skymizer/mistral-7B-v0.1-sft-slim-orca-sonnet-3.5-v4 tags:
- axolotl
- generatedfromtrainer model-index:
- name: mistral-7B-v0.1-csft-relufication-stage-1-on-slim-orca 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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/> <details><summary>See axolotl config</summary>
axolotl version: 0.5.2
base_model: skymizer/mistral-7B-v0.1-sft-slim-orca-sonnet-3.5-v4
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false
resize_token_embeddings_to_32x: false
flash_attention: true
xformers_attention:
load_in_8bit: false
load_in_4bit: false
strict: false
chat_template: chatml
datasets:
- path: skymizer/Sonnet3.5-SlimOrcaDedupCleaned-train
type: chat_template
field_messages: messages
test_datasets:
- path: skymizer/Sonnet3.5-SlimOrcaDedupCleaned-test
type: chat_template
field_messages: messages
split: train
hf_use_auth_token: true
dataset_prepared_path: pretokenized/slim-orca
output_dir: ./exp_output_artifacts
sequence_len: 2048
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: false
# eval_causal_lm_metrics: ["perplexity"]
wandb_project: "axolotl_mistral_sft"
wandb_entity:
wandb_watch:
wandb_name: "mistral-7B-v0.1-csft-relufication-stage-1-on-slim-orca"
wandb_log_model:
gradient_accumulation_steps: 2
micro_batch_size: 16
eval_batch_size: 1
num_epochs: 1
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 0.000005
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.95
adam_eps: 0.000001
max_grad_norm: 1.0
train_on_inputs: false
group_by_length: false
bf16: true
fp16:
tf32: false
hub_model_id: "skymizer/mistral-7B-v0.1-csft-relufication-stage-1-on-slim-orca"
save_strategy: "steps"
save_steps: 50
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
warmup_ratio: 0.03
eval_steps: 50
eval_table_size:
eval_max_new_tokens: 2048
debug:
deepspeed: deepspeed_configs/zero3_bf16.json
fsdp:
fsdp_config:
seed: 42
</details><br>
mistral-7B-v0.1-csft-relufication-stage-1-on-slim-orca
This model is a fine-tuned version of skymizer/mistral-7B-v0.1-sft-slim-orca-sonnet-3.5-v4 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6302
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
- trainbatchsize: 16
- evalbatchsize: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradientaccumulationsteps: 2
- totaltrainbatch_size: 128
- totalevalbatch_size: 4
- optimizer: Use OptimizerNames.ADAMWTORCH with betas=(0.9,0.95) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments
- lrschedulertype: cosine
- lrschedulerwarmup_steps: 11
- num_epochs: 1
Training results
Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3