Instructions to use IvmeLabs/Ivme-Conversate-S-v2-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use IvmeLabs/Ivme-Conversate-S-v2-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="IvmeLabs/Ivme-Conversate-S-v2-Instruct", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("IvmeLabs/Ivme-Conversate-S-v2-Instruct", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use IvmeLabs/Ivme-Conversate-S-v2-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "IvmeLabs/Ivme-Conversate-S-v2-Instruct"
# 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-S-v2-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/IvmeLabs/Ivme-Conversate-S-v2-Instruct

SGLang

How to use IvmeLabs/Ivme-Conversate-S-v2-Instruct 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-S-v2-Instruct" \
    --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-S-v2-Instruct",
		"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-S-v2-Instruct" \
        --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-S-v2-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use IvmeLabs/Ivme-Conversate-S-v2-Instruct with Docker Model Runner:
```
docker model run hf.co/IvmeLabs/Ivme-Conversate-S-v2-Instruct
```

Ivme-Conversate-S-v2-Instruct / README.md

ereniko

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metadata

license: apache-2.0
pipeline_tag: text-generation
library_name: transformers
language:
  - en
tags:
  - from-scratch
  - experimental
  - causal-lm
  - small-language-model
  - instruct-pretrained
datasets:
  - HuggingFaceH4/ultrachat_200k
  - allenai/soda
  - openbmb/UltraInteract_sft
  - microsoft/orca-math-word-problems-200k
  - databricks/databricks-dolly-15k
  - b-mc2/sql-create-context

Ivme-Conversate-S-v2-Instruct

9,021,600 parameters. Standard decoder-only Transformer (tied embeddings, multi-head attention, RoPE, SwiGLU, RMSNorm) -- matching Ivme-Conversate-v2-Base's proven recipe exactly, deliberately with zero architectural novelty.

Trained single-epoch on ~900M tokens, instruct-heavy from the start rather than base-pretrain-then-finetune: UltraChat-200k (real multi-turn dialogue) as the dominant 45% share, plus SODA, UltraInteract reasoning traces, orca-math, dolly-15k instructions, and sql-create-context. All sources permissively licensed (MIT/CC-BY/CC-BY-SA).

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "ivmelabs/Ivme-Conversate-S-v2-Instruct", trust_remote_code=True
)
tok = AutoTokenizer.from_pretrained("ivmelabs/Ivme-Conversate-S-v2-Instruct")

ids = tok("Hello!", return_tensors="pt").input_ids
out = model.generate(ids, max_new_tokens=80, do_sample=True, temperature=0.8, top_k=40)
print(tok.decode(out[0]))

Note: no KV-cache in this architecture -- .generate() works but is O(n^2) rather than O(n), fine for short samples, not tuned for long-form serving.