Instructions to use IvmeLabs/Ivme-Conversate-v2-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use IvmeLabs/Ivme-Conversate-v2-Base with Transformers:

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

pipe = pipeline("text-generation", model="IvmeLabs/Ivme-Conversate-v2-Base", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("IvmeLabs/Ivme-Conversate-v2-Base", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use IvmeLabs/Ivme-Conversate-v2-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-v2-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-v2-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/IvmeLabs/Ivme-Conversate-v2-Base

SGLang

How to use IvmeLabs/Ivme-Conversate-v2-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-v2-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-v2-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-v2-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-v2-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use IvmeLabs/Ivme-Conversate-v2-Base with Docker Model Runner:
```
docker model run hf.co/IvmeLabs/Ivme-Conversate-v2-Base
```

ereniko commited on Jul 7

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708dccc

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1 Parent(s): 31ea0c8

Update README.md

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@@ -155,6 +155,7 @@ Custom byte-level BPE tokenizer trained from scratch on a sample of the pretrain
 - 1024 token context window
 - Weaker on long-distance syntactic dependencies than on local agreement, see BLiMP breakdown above
 - No code data in the training mix (Python-Edu was dropped, see above)
 ---

 - 1024 token context window
 - Weaker on long-distance syntactic dependencies than on local agreement, see BLiMP breakdown above
 - No code data in the training mix (Python-Edu was dropped, see above)
+- Weak knowledge. While the model has learned syntax and grammar very well, it falls short on knowledge and easily hallucinates. We are expecting to fix this in upcoming variants via distillation.
 ---