---
license: apache-2.0
library_name: peft
tags:
- finetuned
- multimodal
base_model: mistralai/Mistral-7B-Instruct-v0.1
dataset: sshh12/clap-gpt-finetune
inference: false
---

These are weights for a version of `mistralai/Mistral-7B-Instruct-v0.1` finetuned for multimodal applications. 

### Modalities

* CLAPAudioModality (use `<sound>` in text and provide `sounds`, encoded as 5 tokens)

### Usage

GitHub: https://github.com/sshh12/multi_token (includes training scripts and basic inference server)

### Dataset

sshh12/clap-gpt-finetune (100000 examples)

```
{'sounds': ['https://dkihjuum4jcjr.cloudfront.net/ES_ITUNES/Gun%20Submachine%20Gun%2062/ES_Gun%20Submachine%20Gun%2062.mp3'], 'messages': [{'content': '<sound> Is the gun in the audio file a submachine gun?', 'role': 'user'}, {'content': 'Yes, the audio file contains the sound of a submachine gun, specifically a German MP40 9mm automatic submachine gun.', 'role': 'assistant'}]}
```

### Training Device(s)

```
name, pci.bus_id, vbios_version
NVIDIA GeForce RTX 4090, 00000000:21:00.0, 95.02.3C.40.1B
```


### 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): SiLUActivation()
            )
            (input_layernorm): MistralRMSNorm()
            (post_attention_layernorm): MistralRMSNorm()
          )
        )
        (norm): MistralRMSNorm()
        (audio_clap_lmm_projector): _MLPVectorProjector(
          (mlps): ModuleList(
            (0-4): 5 x Sequential(
              (0): Linear(in_features=512, 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