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

File size: 557 Bytes

320c763

{
  "architectures": [
    "IvmeConversateSV2InstructModel"
  ],
  "auto_map": {
    "AutoConfig": "configuration_ivme_s_v2_instruct.IvmeConversateSV2InstructConfig",
    "AutoModelForCausalLM": "modeling_ivme_s_v2_instruct.IvmeConversateSV2InstructModel"
  },
  "d_ff": 896,
  "d_model": 224,
  "dtype": "float32",
  "max_seq_len": 1024,
  "model_type": "ivme_conversate_s_v2_instruct",
  "n_heads": 7,
  "n_layers": 9,
  "norm_eps": 1e-05,
  "rope_theta": 10000.0,
  "tie_word_embeddings": true,
  "transformers_version": "5.14.1",
  "vocab_size": 8000
}