Instructions to use sshh12/Mistral-7B-LoRA-ImageBind-LLAVA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sshh12/Mistral-7B-LoRA-ImageBind-LLAVA with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="sshh12/Mistral-7B-LoRA-ImageBind-LLAVA")

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("sshh12/Mistral-7B-LoRA-ImageBind-LLAVA", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use sshh12/Mistral-7B-LoRA-ImageBind-LLAVA with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sshh12/Mistral-7B-LoRA-ImageBind-LLAVA"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sshh12/Mistral-7B-LoRA-ImageBind-LLAVA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/sshh12/Mistral-7B-LoRA-ImageBind-LLAVA

SGLang

How to use sshh12/Mistral-7B-LoRA-ImageBind-LLAVA with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "sshh12/Mistral-7B-LoRA-ImageBind-LLAVA" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sshh12/Mistral-7B-LoRA-ImageBind-LLAVA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "sshh12/Mistral-7B-LoRA-ImageBind-LLAVA" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sshh12/Mistral-7B-LoRA-ImageBind-LLAVA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use sshh12/Mistral-7B-LoRA-ImageBind-LLAVA with Docker Model Runner:
```
docker model run hf.co/sshh12/Mistral-7B-LoRA-ImageBind-LLAVA
```

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

Modalities

ImageBindModality (use <imagebind> in text and provide imagebinds, encoded as 4 tokens)

Dataset

sshh12/imagebind-llava-finetune (235163 examples)

{'id': '000000334872', 'imagebinds': ['/data/llava_finetune_data/images/coco/train2017/train2017/000000334872.jpg'], 'messages': [{'content': '<imagebind>\nAre the people in the audio skiing downhill or cross-country skiing?', 'role': 'user'}, {'content': 'The people in the audio are cross-country skiing in the woods, as they are skiing on a trail rather than a steep slope.', 'role': 'assistant'}, {'content': 'How many people are in the audio?', 'role': 'user'}, {'content': 'There are two people in the audio, both on skis in the snow.', 'role': 'assistant'}, {'content': 'What kind of environment are they skiing in?', 'role': 'user'}, {'content': 'They are skiing in a wooded environment, following a trail through the trees while surrounded by snow.', 'role': 'assistant'}, {'content': 'Do the skiers have any additional gear with them besides their skis and poles?', 'role': 'user'}, {'content': 'Yes, the two male skiers are carrying backpacks while they ski through the woods. The backpacks might contain essentials for their skiing adventure, such as food, water, extra clothing, or safety equipment.', 'role': 'assistant'}]}

Training Device(s)

name, pci.bus_id, vbios_version
NVIDIA GeForce RTX 4090, 00000000:03:00.0, 95.02.3C.00.8C

Usage

GitHub: https://github.com/sshh12/multi_token

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): 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): 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): 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): 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): 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): 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): 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()
        (imagebind_lmm_projector): _MLPVectorProjector(
          (mlps): ModuleList(
            (0-3): 4 x Sequential(
              (0): Linear(in_features=1024, 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)
    )
  )
)

Training procedure

Framework versions

PEFT 0.5.0

Downloads last month: 7

Model tree for sshh12/Mistral-7B-LoRA-ImageBind-LLAVA

Base model

mistralai/Mistral-7B-v0.1

Finetuned

mistralai/Mistral-7B-Instruct-v0.1

Finetuned

(328)

this model