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

Libraries

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

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

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

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("IvmeLabs/Ivme-Conversate-XL-v1-Base", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

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

SGLang

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

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

Ivme-Conversate-XL-v1-Base / README.md

ereniko

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	---
	license: apache-2.0
	language:
	- en
	library_name: transformers
	tags:
	- not_working_will_be_fixed
	- ivmelabs
	- causal-lm
	- from-scratch
	pipeline_tag: text-generation
	new_version: IvmeLabs/Ivme-Conversate-XL-v1.1-Base
	---

	# ATTENTION

	This model is considered broken and should not be used.

	The repaired version is available as [Ivme-Conversate-XL-v1.1-Base](https://huggingface.co/IvmeLabs/Ivme-Conversate-XL-v1.1-Base) and should be used instead of this version.

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	The whitespace here was put to make the alert more visible.

	# Ivme-Conversate-XL-v1-Base

	![Conversate-XL-v1 Logo](https://cdn-uploads.huggingface.co/production/uploads/670562d6ac129959c16f84d4/U4EYxVSJyc829txT51IGq.png)

	Dense decoder-only transformer, 125.6M parameters, trained from
	scratch by IvmeLabs. Part of the Conversate family — see the
	[IvmeLabs organization page](https://huggingface.co/IvmeLabs) for related
	models (Conversate-S, mainline Conversate, and this XL tier).

	## Architecture

	- 12 layers, hidden size 768, 12 attention heads (head_dim 64)
	- SwiGLU feed-forward, ffn_dim 3072
	- RoPE positional encoding (theta=10000.0)
	- RMSNorm (pre-norm), tied input/output embeddings, no bias terms
	- Vocabulary: 16000 tokens (BPE)
	- Max sequence length: 1024

	## Training

	Trained on a 5.0B-token mix (backbone: DCLM-baseline,
	FineWeb-Edu, FineMath; supplement: Wikipedia-en, Project Gutenberg-en) using
	Muon (body weights) + AdamW (embeddings/norms), on a single AMD Instinct
	MI300X (ROCm 7.14.0, PyTorch 2.12.0).

	## Usage

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model = AutoModelForCausalLM.from_pretrained(
	"IvmeLabs/Ivme-Conversate-XL-v1-Base", trust_remote_code=True
	)
	tokenizer = AutoTokenizer.from_pretrained("IvmeLabs/Ivme-Conversate-XL-v1-Base")

	inputs = tokenizer("Hello, my name is", return_tensors="pt")
	outputs = model.generate(inputs["input_ids"], max_new_tokens=50)
	print(tokenizer.decode(outputs[0]))
	```

	Note: requires `trust_remote_code=True` since this uses a custom
	architecture (`modeling_ivme.py` in this repo), not a built-in
	`transformers` model class. Review that file before trusting it, as with
	any `trust_remote_code=True` model.

	## Checkpoint

	This repo contains checkpoint(s) from step(s): 160, 320, 480, 640, 800, 960, 1120, 1280, 1440, 1600, 1760, 1920, 2080, 2240, 2400, 2560, 2720, 2880, 3040, 3200, 3318