Image-Text-to-Text
Transformers
Safetensors
multilingual
comemo_chat
feature-extraction
internvl
custom_code
conversational
Instructions to use CLLBJ16/CoMemo-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CLLBJ16/CoMemo-9B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="CLLBJ16/CoMemo-9B", trust_remote_code=True) messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("CLLBJ16/CoMemo-9B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use CLLBJ16/CoMemo-9B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "CLLBJ16/CoMemo-9B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CLLBJ16/CoMemo-9B", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/CLLBJ16/CoMemo-9B
- SGLang
How to use CLLBJ16/CoMemo-9B 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 "CLLBJ16/CoMemo-9B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CLLBJ16/CoMemo-9B", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'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 "CLLBJ16/CoMemo-9B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CLLBJ16/CoMemo-9B", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use CLLBJ16/CoMemo-9B with Docker Model Runner:
docker model run hf.co/CLLBJ16/CoMemo-9B
File size: 2,179 Bytes
a12c31a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 | def extend_instance(obj, mixin):
"""Apply mixins to a class instance after creation"""
base_cls = obj.__class__
base_cls_name = obj.__class__.__name__
obj.__class__ = type(
base_cls_name, (mixin, base_cls), {}
) # mixin needs to go first for our forward() logic to work
def getattr_recursive(obj, att):
"""
Return nested attribute of obj
Example: getattr_recursive(obj, 'a.b.c') is equivalent to obj.a.b.c
"""
if att == "":
return obj
i = att.find(".")
if i < 0:
return getattr(obj, att)
else:
return getattr_recursive(getattr(obj, att[:i]), att[i + 1 :])
def setattr_recursive(obj, att, val):
"""
Set nested attribute of obj
Example: setattr_recursive(obj, 'a.b.c', val) is equivalent to obj.a.b.c = val
"""
if "." in att:
obj = getattr_recursive(obj, ".".join(att.split(".")[:-1]))
setattr(obj, att.split(".")[-1], val)
def apply_with_stopping_condition(
module, apply_fn, apply_condition=None, stopping_condition=None, **other_args
):
if stopping_condition(module):
return
if apply_condition(module):
apply_fn(module, **other_args)
for child in module.children():
apply_with_stopping_condition(
child,
apply_fn,
apply_condition=apply_condition,
stopping_condition=stopping_condition,
**other_args
)
__KNOWN_DECODER_LAYERS_ATTR_NAMES = {
"opt": "model.decoder.layers",
"gptj": "transformer.h",
"gpt-j": "transformer.h",
"pythia": "gpt_neox.layers",
"llama": "model.layers",
"gptneoxforcausallm": "gpt_neox.layers",
"mpt": "transformer.blocks",
"mosaicgpt": "transformer.blocks",
"internlm2forcausallm": "model.layers",
}
def _infer_decoder_layers_attr_name(model):
for k in __KNOWN_DECODER_LAYERS_ATTR_NAMES:
if k.lower() in model.__class__.__name__.lower():
return __KNOWN_DECODER_LAYERS_ATTR_NAMES[k]
raise ValueError(
f"We require the attribute name for the nn.ModuleList in the decoder storing the transformer block layers. Please supply this string manually."
)
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