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
```

A newer version of this model is available: 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 and should be used instead of this version.

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

Ivme-Conversate-XL-v1-Base

Dense decoder-only transformer, 125.6M parameters, trained from scratch by IvmeLabs. Part of the Conversate family — see the IvmeLabs organization page 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

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

Downloads last month: 1,348

Safetensors

Model size

0.1B params

Tensor type

F32

Collection including IvmeLabs/Ivme-Conversate-XL-v1-Base

Conversate-XL

Collection

Our flagship and largest Conversate family. • 2 items • Updated 4 days ago