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

Libraries

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

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

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

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

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

SGLang

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

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

Ivme-Conversate-P-v1-Base / config.json

ereniko

Release İvme-Conversate-P-v1

29f0d05 verified 18 days ago

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605 Bytes

{
  "architectures": [
    "IvmeConversateForCausalLM"
  ],
  "auto_map": {
    "AutoConfig": "configuration_ivme_conversate.IvmeConversateConfig",
    "AutoModelForCausalLM": "modeling_ivme_conversate.IvmeConversateForCausalLM"
  },
  "context_len": 1024,
  "d_ffn": 341,
  "d_model": 128,
  "dtype": "float32",
  "max_position_embeddings": 1024,
  "model_type": "ivme_conversate",
  "n_heads": 4,
  "n_layers": 6,
  "qknorm_eps": 1e-05,
  "rmsnorm_eps": 1e-05,
  "rope_theta": 10000.0,
  "tie_embeddings": true,
  "tie_word_embeddings": true,
  "transformers_version": "5.14.1",
  "vocab_size": 16000
}

	{
	"architectures": [
	"IvmeConversateForCausalLM"
	],
	"auto_map": {
	"AutoConfig": "configuration_ivme_conversate.IvmeConversateConfig",
	"AutoModelForCausalLM": "modeling_ivme_conversate.IvmeConversateForCausalLM"
	},
	"context_len": 1024,
	"d_ffn": 341,
	"d_model": 128,
	"dtype": "float32",
	"max_position_embeddings": 1024,
	"model_type": "ivme_conversate",
	"n_heads": 4,
	"n_layers": 6,
	"qknorm_eps": 1e-05,
	"rmsnorm_eps": 1e-05,
	"rope_theta": 10000.0,
	"tie_embeddings": true,
	"tie_word_embeddings": true,
	"transformers_version": "5.14.1",
	"vocab_size": 16000
	}