Instructions to use CreitinGameplays/ConvAI-9b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CreitinGameplays/ConvAI-9b with Transformers:

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

pipe = pipeline("text-generation", model="CreitinGameplays/ConvAI-9b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CreitinGameplays/ConvAI-9b")
model = AutoModelForCausalLM.from_pretrained("CreitinGameplays/ConvAI-9b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use CreitinGameplays/ConvAI-9b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CreitinGameplays/ConvAI-9b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CreitinGameplays/ConvAI-9b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CreitinGameplays/ConvAI-9b

SGLang

How to use CreitinGameplays/ConvAI-9b 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 "CreitinGameplays/ConvAI-9b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CreitinGameplays/ConvAI-9b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "CreitinGameplays/ConvAI-9b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CreitinGameplays/ConvAI-9b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use CreitinGameplays/ConvAI-9b with Docker Model Runner:
```
docker model run hf.co/CreitinGameplays/ConvAI-9b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

ConvAI-9b: A Conversational AI Model

1. Model Details

Model Name: ConvAI-9b
Authors: CreitinGameplays
Date: April 18th, 2024

2. Model Description

ConvAI-9b is a fine-tuned conversational AI model with 9 billion parameters. It is based on the following models:

Base Model: HuggingFaceH4/zephyr-7b-beta
Merged Model: mistral-community/Mistral-7B-v0.2

3. Training Data

The model was fine-tuned on a custom dataset of conversations between an AI assistant and a user. The dataset format followed a specific structure:

<|system|> (system prompt, e.g.: You are a helpful AI language model called ChatGPT, your goal is helping users with their questions) </s> <|user|> (user prompt) </s>

4. Intended Uses

ConvAI-9b is intended for use in conversational AI applications, such as:

Chatbots
Virtual assistants
Interactive storytelling
Educational tools

5. Limitations

Like any other language model, ConvAI-9b may generate incorrect or misleading responses.
It may exhibit biases present in the training data.
The model's performance can be affected by the quality and format of the input text.