Downloads · 30 days
3
9% of all-time downloads
mixtral-fc-w-resp
dyang415/mixtral-fc-w-resp
mixtral-fc-w-resp is a machine learning model from dyang415. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
All-time downloads
32
Public
Repo size
219 MB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.bin109 MB · 50%
At a glance
- Library
- peft
- License
- apache-2.0
- Model type
- mixtral
- Access
- Public
- Created
- Mar 2, 2024
- Updated
- Mar 2, 2024
- SHA
- b74f9b0b
Base models
Model card
From the Hugging Face model README
<!-- 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. -->
<details><summary>See axolotl config</summary>
axolotl version: 0.4.0
base_model: mistralai/Mixtral-8x7B-Instruct-v0.1
model_type: AutoModelForCausalLM
tokenizer_type: LlamaTokenizer
trust_remote_code: true
load_in_8bit: false
load_in_4bit: true
strict: false
chat_template: inst
datasets:
- path: ./data/with_function_response/function_not_used_training.jsonl
type: sharegpt
conversation: mistral
- path: ./data/with_function_response/no_function_training.jsonl
type: sharegpt
conversation: mistral
- path: ./data/with_function_response/function_used_training.jsonl
type: sharegpt
conversation: mistral
hub_model_id: dyang415/mixtral-fc-w-resp
dataset_prepared_path: last_run_prepared
val_set_size: 0.0
output_dir: ../mixtral-fc-w-resp
model_config:
output_router_logits: true
adapter: qlora
lora_model_dir:
sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true
lora_r: 32
lora_alpha: 64
lora_dropout: 0.05
lora_target_modules:
- q_proj
- k_proj
- v_proj
- o_proj
# wandb_project: function-call
# wandb_name: mixtral-instruct-lora--v1
# wandb_log_model: end
# hub_model_id: dyang415/mixtral-lora-v0
gradient_accumulation_steps: 2
micro_batch_size: 1
num_epochs: 1
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false
gradient_checkpointing: true
logging_steps: 1
flash_attention: true
loss_watchdog_threshold: 5.0
loss_watchdog_patience: 3
warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1
debug:
weight_decay: 0.0
fsdp:
fsdp_config:
mixtral-fc-w-resp
This model is a fine-tuned version of mistralai/Mixtral-8x7B-Instruct-v0.1 on the None dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
The following bitsandbytes quantization config was used during training:
- quant_method: QuantizationMethod.BITS_AND_BYTES
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- total_eval_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 1
Training results
Framework versions
- PEFT 0.7.0
- Transformers 4.37.0
- Pytorch 2.0.1+cu117
- Datasets 2.17.1
- Tokenizers 0.15.0
- Library
- peft
- Type
- mixtral
- License
- apache-2.0
- Created
- Mar 2, 2024
- Updated
- Mar 2, 2024