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
```

Ivme-Conversate-v2-Base / NEW_FILES.md

ereniko

Add safetensors + Transformers (AutoModelForCausalLM) support

5974fd0 verified about 1 month ago

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	## Transformers / safetensors support

	This model can now be loaded with `AutoModelForCausalLM` instead of the
	manual pickle-loading workflow, and weights are available as
	`model.safetensors`.

	```python
	import torch
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model = AutoModelForCausalLM.from_pretrained(
	"IvmeLabs/Ivme-Conversate-v2-Base", trust_remote_code=True, dtype=torch.float32,
	)
	tokenizer = AutoTokenizer.from_pretrained("IvmeLabs/Ivme-Conversate-v2-Base", trust_remote_code=True)
	model.eval()

	inputs = tokenizer("Once upon a time, there was a", return_tensors="pt")
	out = model.generate(
	**inputs, max_new_tokens=200, do_sample=True,
	temperature=0.8, top_k=50, pad_token_id=tokenizer.pad_token_id,
	)
	print(tokenizer.decode(out[0], skip_special_tokens=True))
	```

	`trust_remote_code=True` is required (custom architecture: RoPE + SwiGLU +
	RMSNorm dense decoder). The original `ckpt_final.pt` pickle checkpoint and
	`model/` architecture source remain in this repo unchanged for backwards
	compatibility.

	Note on batch generation: use left-padding
	(`tokenizer.padding_side = "left"`) — the model doesn't use an explicit
	attention mask over padded positions, so right-padding within a batch will
	give incorrect results.