Instructions to use IvmeLabs/Ivme-Conversate-U-v1-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use IvmeLabs/Ivme-Conversate-U-v1-Base with Transformers:

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

pipe = pipeline("text-generation", model="IvmeLabs/Ivme-Conversate-U-v1-Base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("IvmeLabs/Ivme-Conversate-U-v1-Base")
model = AutoModelForCausalLM.from_pretrained("IvmeLabs/Ivme-Conversate-U-v1-Base", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use IvmeLabs/Ivme-Conversate-U-v1-Base with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/IvmeLabs/Ivme-Conversate-U-v1-Base

SGLang

How to use IvmeLabs/Ivme-Conversate-U-v1-Base 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 "IvmeLabs/Ivme-Conversate-U-v1-Base" \
    --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": "IvmeLabs/Ivme-Conversate-U-v1-Base",
		"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 "IvmeLabs/Ivme-Conversate-U-v1-Base" \
        --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": "IvmeLabs/Ivme-Conversate-U-v1-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use IvmeLabs/Ivme-Conversate-U-v1-Base with Docker Model Runner:
```
docker model run hf.co/IvmeLabs/Ivme-Conversate-U-v1-Base
```

ereniko commited on 26 days ago

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@@ -20,7 +20,7 @@ pipeline_tag: text-generation
 Codename: Ultra Apple 1
-![Conversate-U Logo](https://cdn-uploads.huggingface.co/production/uploads/670562d6ac129959c16f84d4/9oWY_UmFyQ297XmaZEK7G.png)
 A 316,723,200-parameter dense decoder-only transformer, trained entirely from scratch by IvmeLabs. Part of the Conversate family; see the [IvmeLabs organization page](https://huggingface.co/IvmeLabs) for related models.

 Codename: Ultra Apple 1
+![Conversate-U-v1 Logo](https://cdn-uploads.huggingface.co/production/uploads/670562d6ac129959c16f84d4/2eJ3VWFg4XmTkILfYgvAi.png)
 A 316,723,200-parameter dense decoder-only transformer, trained entirely from scratch by IvmeLabs. Part of the Conversate family; see the [IvmeLabs organization page](https://huggingface.co/IvmeLabs) for related models.