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

Libraries

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

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

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

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

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

SGLang

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

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

ereniko commited on 1 day ago

Commit

f607905

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1 Parent(s): 05fc8d4

Update README.md

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README.md +5 -4

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@@ -58,9 +58,9 @@ model-index:
 license: cc-by-nc-sa-4.0
 ---
-# Ivme-Conversate-N-v1-Base
-**Codename: Nano Apple 1**
 ![Conversate-S-v1 Logo](https://cdn-uploads.huggingface.co/production/uploads/670562d6ac129959c16f84d4/hVL6LVpQyFIbrvCMY501Y.png)
@@ -399,9 +399,10 @@ exotic rather than a light reskin of a standard Transformer.
 from transformers import AutoModelForCausalLM, AutoTokenizer
 model = AutoModelForCausalLM.from_pretrained(
-    "ivmelabs/Ivme-Conversate-S-v1-Base", trust_remote_code=True
 )
-tok = AutoTokenizer.from_pretrained("ivmelabs/Ivme-Conversate-S-v1-Base")
 ids = tok("Once upon a time", return_tensors="pt").input_ids
 out = model.generate(ids, max_new_tokens=80, do_sample=True, temperature=0.8, top_k=40)

 license: cc-by-nc-sa-4.0
 ---
+# Ivme-Conversate-N-v1-Base (Before known as Ivme-Conversate-S-v1-Base)
+**Codename: Nano Apple 1 (Before known as Small Apple 1)**
 ![Conversate-S-v1 Logo](https://cdn-uploads.huggingface.co/production/uploads/670562d6ac129959c16f84d4/hVL6LVpQyFIbrvCMY501Y.png)
 from transformers import AutoModelForCausalLM, AutoTokenizer
 model = AutoModelForCausalLM.from_pretrained(
+    "ivmelabs/Ivme-Conversate-N-v1-Base", trust_remote_code=True
 )
+tok = AutoTokenizer.from_pretrained("ivmelabs/Ivme-Conversate-
+N-v1-Base")
 ids = tok("Once upon a time", return_tensors="pt").input_ids
 out = model.generate(ids, max_new_tokens=80, do_sample=True, temperature=0.8, top_k=40)