Image-Text-to-Text
PEFT
Safetensors
English
vision-language
decision-model
lora
decision-index
conversational
Instructions to use edward1503/VISTA-4B-A with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use edward1503/VISTA-4B-A with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen3.5-4B") model = PeftModel.from_pretrained(base_model, "edward1503/VISTA-4B-A") - Notebooks
- Google Colab
- Kaggle
Download parameter-count-audit.json from edward1503/VISTA-4B-A: direct link, hf CLI and curl.
- Browser
- Download file 850 Bytes
-
https://huggingface.co/edward1503/VISTA-4B-A/resolve/main/parameter-count-audit.json
- Command line
-
hf download hf://edward1503/VISTA-4B-A/parameter-count-audit.json
-
curl -L -o parameter-count-audit.json https://huggingface.co/edward1503/VISTA-4B-A/resolve/main/parameter-count-audit.json
850 Bytes
| { | |
| "original_tensor_count": 738, | |
| "merged_tensor_count": 723, | |
| "missing_from_merge": [ | |
| "mtp.fc.weight", | |
| "mtp.layers.0.input_layernorm.weight", | |
| "mtp.layers.0.mlp.down_proj.weight", | |
| "mtp.layers.0.mlp.gate_proj.weight", | |
| "mtp.layers.0.mlp.up_proj.weight", | |
| "mtp.layers.0.post_attention_layernorm.weight", | |
| "mtp.layers.0.self_attn.k_norm.weight", | |
| "mtp.layers.0.self_attn.k_proj.weight", | |
| "mtp.layers.0.self_attn.o_proj.weight", | |
| "mtp.layers.0.self_attn.q_norm.weight", | |
| "mtp.layers.0.self_attn.q_proj.weight", | |
| "mtp.layers.0.self_attn.v_proj.weight", | |
| "mtp.norm.weight", | |
| "mtp.pre_fc_norm_embedding.weight", | |
| "mtp.pre_fc_norm_hidden.weight" | |
| ], | |
| "extra_in_merge": [], | |
| "merged_inference_parameters": 4539265536, | |
| "adapter_parameters": 32464896, | |
| "adapter_plus_inference_parameters": 4571730432 | |
| } | |