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---
license: apache-2.0
library_name: peft
tags:
- finetuned
- multimodal
base_model: mistralai/Mistral-7B-Instruct-v0.1
dataset: sshh12/whisper-gpt-common_voice_15_0-finetune
inference: false
---
These are weights for a version of `mistralai/Mistral-7B-Instruct-v0.1` finetuned for multimodal applications.
### Modalities
* WhisperAudioModality (use `<speech>` in text and provide `speech_audios`, encoded as 10 tokens)
### Usage
GitHub: https://github.com/sshh12/multi_token (includes training scripts and basic inference server)
### Dataset
sshh12/whisper-gpt-common_voice_15_0-finetune (300000 examples)
```
{'speech_audios': [{'dataset_args': {'name': 'en', 'path': 'mozilla-foundation/common_voice_15_0', 'split': 'train'}, 'idx': 68335}], 'messages': [{'content': '<speech> What is said in the audio?', 'role': 'user'}, {'content': 'The screen fades to black.', 'role': 'assistant'}]}
```
### Training Device(s)
```
name, pci.bus_id, vbios_version
NVIDIA RTX A6000, 00000000:82:00.0, 94.02.5C.00.02
```
### Model
```
MistralLMMForCausalLM.model =
PeftModelForCausalLM(
(base_model): LoraModel(
(model): MistralLMMForCausalLM(
(model): MistralLMMModel(
(embed_tokens): Embedding(32000, 4096)
(layers): ModuleList(
(0-31): 32 x MistralDecoderLayer(
(self_attn): MistralAttention(
(q_proj): lora.Linear(
(base_layer): Linear(in_features=4096, out_features=4096, bias=False)
(lora_dropout): ModuleDict(
(default): Dropout(p=0.05, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=4096, out_features=64, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=64, out_features=4096, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
)
(k_proj): lora.Linear(
(base_layer): Linear(in_features=4096, out_features=1024, bias=False)
(lora_dropout): ModuleDict(
(default): Dropout(p=0.05, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=4096, out_features=64, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=64, out_features=1024, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
)
(v_proj): lora.Linear(
(base_layer): Linear(in_features=4096, out_features=1024, bias=False)
(lora_dropout): ModuleDict(
(default): Dropout(p=0.05, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=4096, out_features=64, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=64, out_features=1024, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
)
(o_proj): lora.Linear(
(base_layer): Linear(in_features=4096, out_features=4096, bias=False)
(lora_dropout): ModuleDict(
(default): Dropout(p=0.05, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=4096, out_features=64, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=64, out_features=4096, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
)
(rotary_emb): MistralRotaryEmbedding()
)
(mlp): MistralMLP(
(gate_proj): lora.Linear(
(base_layer): Linear(in_features=4096, out_features=14336, bias=False)
(lora_dropout): ModuleDict(
(default): Dropout(p=0.05, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=4096, out_features=64, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=64, out_features=14336, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
)
(up_proj): lora.Linear(
(base_layer): Linear(in_features=4096, out_features=14336, bias=False)
(lora_dropout): ModuleDict(
(default): Dropout(p=0.05, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=4096, out_features=64, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=64, out_features=14336, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
)
(down_proj): lora.Linear(
(base_layer): Linear(in_features=14336, out_features=4096, bias=False)
(lora_dropout): ModuleDict(
(default): Dropout(p=0.05, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=14336, out_features=64, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=64, out_features=4096, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
)
(act_fn): SiLU()
)
(input_layernorm): MistralRMSNorm()
(post_attention_layernorm): MistralRMSNorm()
)
)
(norm): MistralRMSNorm()
(audio_whisper_lmm_projector): _MLPVectorProjector(
(mlps): ModuleList(
(0-9): 10 x Sequential(
(0): Linear(in_features=768, out_features=4096, bias=True)
(1): GELU(approximate='none')
(2): Linear(in_features=4096, out_features=4096, bias=True)
)
)
)
)
(lm_head): Linear(in_features=4096, out_features=32000, bias=False)
)
)
)
```
### Framework versions
- PEFT 0.7.0 |