Instructions to use HuggingFaceM4/tiny-random-idefics with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HuggingFaceM4/tiny-random-idefics with Transformers:

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

pipe = pipeline("image-text-to-text", model="HuggingFaceM4/tiny-random-idefics")

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("HuggingFaceM4/tiny-random-idefics")
model = AutoModelForImageTextToText.from_pretrained("HuggingFaceM4/tiny-random-idefics")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use HuggingFaceM4/tiny-random-idefics with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HuggingFaceM4/tiny-random-idefics"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HuggingFaceM4/tiny-random-idefics",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/HuggingFaceM4/tiny-random-idefics

SGLang

How to use HuggingFaceM4/tiny-random-idefics 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 "HuggingFaceM4/tiny-random-idefics" \
    --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": "HuggingFaceM4/tiny-random-idefics",
		"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 "HuggingFaceM4/tiny-random-idefics" \
        --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": "HuggingFaceM4/tiny-random-idefics",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use HuggingFaceM4/tiny-random-idefics with Docker Model Runner:
```
docker model run hf.co/HuggingFaceM4/tiny-random-idefics
```

Update config.json

by ybelkada - opened Jul 27, 2023

base: refs/heads/main

←

from: refs/pr/3

Discussion Files changed

+16

-12

Files changed (1) hide show

config.json +16 -12

config.json CHANGED Viewed

@@ -28,23 +28,27 @@
   "num_hidden_layers": 2,
   "pad_token_id": 0,
   "qk_layer_norms": false,
-  "qk_layer_norms_perceiver": false,
-  "resampler_depth": 2,
-  "resampler_head_dim": 8,
-  "resampler_n_heads": 2,
-  "resampler_n_latents": 16,
   "rms_norm_eps": 1e-06,
   "tie_word_embeddings": false,
   "torch_dtype": "float16",
   "transformers_version": "4.27.0.dev0",
   "use_cache": true,
   "use_resampler": true,
-  "vision_embed_dim": 32,
-  "vision_image_size": 30,
-  "vision_intermediate_size": 37,
-  "vision_patch_size": 2,
-  "vision_num_attention_heads": 4,
-  "vision_num_hidden_layers": 5,
   "vocab_size": 32000,
-  "word_embed_proj_dim": 16
 }

   "num_hidden_layers": 2,
   "pad_token_id": 0,
   "qk_layer_norms": false,
   "rms_norm_eps": 1e-06,
   "tie_word_embeddings": false,
   "torch_dtype": "float16",
   "transformers_version": "4.27.0.dev0",
   "use_cache": true,
   "use_resampler": true,
   "vocab_size": 32000,
+  "word_embed_proj_dim": 16,
+  "vision_config": {
+    "embed_dim": 32,
+    "image_size": 30,
+    "intermediate_size": 37,
+    "patch_size": 2,
+    "num_attention_heads": 4,
+    "num_hidden_layers": 5
+  },
+  "perceiver_config": {
+    "qk_layer_norms_perceiver": false,
+    "resampler_depth": 2,
+    "resampler_head_dim": 8,
+    "resampler_n_heads": 2,
+    "resampler_n_latents": 16
+  }
 }