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

Libraries

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

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

pipe = pipeline("text-generation", model="IvmeLabs/Ivme-Conversate-Chat-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("IvmeLabs/Ivme-Conversate-Chat-v1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "IvmeLabs/Ivme-Conversate-Chat-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IvmeLabs/Ivme-Conversate-Chat-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use IvmeLabs/Ivme-Conversate-Chat-v1 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-Chat-v1" \
    --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": "IvmeLabs/Ivme-Conversate-Chat-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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-Chat-v1" \
        --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": "IvmeLabs/Ivme-Conversate-Chat-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

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This repository is publicly accessible, but you have to accept the conditions to access its files and content.

This is a checkpoint bucket for an in-progress SFT training run ("Chatmaxxing" -- finetuning Ivme-Conversate-U-v1-Base on conversational data). It is gated to manual approval, not because the content is sensitive, but to keep casual downloads of intermediate/incomplete training checkpoints separate from the polished, final public release this run is working towards. Requests are generally approved -- just ask.

Ivme-Conversate-Chat-v1 (in progress / checkpoint bucket)

This repository holds intermediate and final checkpoints from an active SFT finetuning run: taking Ivme-Conversate-U-v1-Base, a 317M-parameter base model trained on pure distillation data (Cosmopedia v2, generated by Mixtral-8x7B-Instruct-v0.1), and finetuning it on smol-smoltalk, a conversational SFT dataset purpose-built for sub-1B-parameter models (core component generated by Llama-3.1-405B-Instruct via the Magpie pipeline).

This repo is gated to manual approval so that casual traffic doesn't land on an intermediate, possibly-broken checkpoint mid-run. It is not gated because of any sensitive content -- access requests are generally approved quickly.

Checkpoints here may be incomplete, may not follow instructions well yet, and may be superseded by later commits to this same repo as training continues. For a stable, finished release, watch the IvmeLabs organization page instead.

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