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 6

Commit

f3768a5

verified ·

1 Parent(s): f3ea0c1

Upload folder using huggingface_hub

Browse files

Files changed (2) hide show

model.safetensors +3 -0
modeling_ivme.py +1 -1

model.safetensors ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:db78dcec9aa219862c3b3ca36372af576ff02696e17c3389e121f3653c4cf591
+size 95396368

modeling_ivme.py CHANGED Viewed

@@ -171,4 +171,4 @@ class IvmeConversateV2HF(PreTrainedModel):
         if labels is not None:
             loss = F.cross_entropy(logits.view(-1, logits.size(-1)), labels.view(-1), ignore_index=-1)
-        return CausalLMOutputWithPast(loss=loss, logits=logits)

         if labels is not None:
             loss = F.cross_entropy(logits.view(-1, logits.size(-1)), labels.view(-1), ignore_index=-1)
+        return CausalLMOutputWithPast(loss=loss, logits=logits)