Instructions to use OEvortex/HelpingAI-9B-200k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OEvortex/HelpingAI-9B-200k with Transformers:

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

pipe = pipeline("text-generation", model="OEvortex/HelpingAI-9B-200k")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OEvortex/HelpingAI-9B-200k")
model = AutoModelForCausalLM.from_pretrained("OEvortex/HelpingAI-9B-200k")
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]:]))

llama-cpp-python

How to use OEvortex/HelpingAI-9B-200k with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="OEvortex/HelpingAI-9B-200k",
	filename="helpingai-9b-200k-q4_k_m.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use OEvortex/HelpingAI-9B-200k with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf OEvortex/HelpingAI-9B-200k:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf OEvortex/HelpingAI-9B-200k:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf OEvortex/HelpingAI-9B-200k:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf OEvortex/HelpingAI-9B-200k:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf OEvortex/HelpingAI-9B-200k:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf OEvortex/HelpingAI-9B-200k:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf OEvortex/HelpingAI-9B-200k:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf OEvortex/HelpingAI-9B-200k:Q4_K_M

Use Docker

docker model run hf.co/OEvortex/HelpingAI-9B-200k:Q4_K_M

LM Studio
Jan

vLLM

How to use OEvortex/HelpingAI-9B-200k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OEvortex/HelpingAI-9B-200k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OEvortex/HelpingAI-9B-200k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/OEvortex/HelpingAI-9B-200k:Q4_K_M

SGLang

How to use OEvortex/HelpingAI-9B-200k 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 "OEvortex/HelpingAI-9B-200k" \
    --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": "OEvortex/HelpingAI-9B-200k",
		"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 "OEvortex/HelpingAI-9B-200k" \
        --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": "OEvortex/HelpingAI-9B-200k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use OEvortex/HelpingAI-9B-200k with Ollama:
```
ollama run hf.co/OEvortex/HelpingAI-9B-200k:Q4_K_M
```

Unsloth Studio new

How to use OEvortex/HelpingAI-9B-200k with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for OEvortex/HelpingAI-9B-200k to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for OEvortex/HelpingAI-9B-200k to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for OEvortex/HelpingAI-9B-200k to start chatting

Docker Model Runner
How to use OEvortex/HelpingAI-9B-200k with Docker Model Runner:
```
docker model run hf.co/OEvortex/HelpingAI-9B-200k:Q4_K_M
```

Lemonade

How to use OEvortex/HelpingAI-9B-200k with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull OEvortex/HelpingAI-9B-200k:Q4_K_M

Run and chat with the model

lemonade run user.HelpingAI-9B-200k-Q4_K_M

List all available models

lemonade list

Abhaykoul commited on Jul 17, 2024

Commit

8a7055d

verified ·

1 Parent(s): f41155c

Update README.md

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@@ -10,6 +10,8 @@ tags:
 datasets:
 - OEvortex/SentimentSynth
 - OEvortex/EmotionalIntelligence-10K
 ---
 # HelpingAI-9B-200k: Emotionally Intelligent Conversational AI with 200k Context Window
@@ -143,4 +145,4 @@ Installing security devices in your home can bring peace of mind and keep you sa
   > How can your own feelings affect your ability to empathize with someone else?
-Our own emotions can impact our ability to empathize with others because they shape how we perceive and interpret other people's experiences. If you're feeling happy or positive, it may be easier to see the good in others and offer support. On the other hand, if you're feeling down or stressed, it might be harder to put yourself in someone else's shoes. It's important to acknowledge your own feelings and work on being open and understanding towards others! 🌟

 datasets:
 - OEvortex/SentimentSynth
 - OEvortex/EmotionalIntelligence-10K
+language:
+- en
 ---
 # HelpingAI-9B-200k: Emotionally Intelligent Conversational AI with 200k Context Window
   > How can your own feelings affect your ability to empathize with someone else?
+Our own emotions can impact our ability to empathize with others because they shape how we perceive and interpret other people's experiences. If you're feeling happy or positive, it may be easier to see the good in others and offer support. On the other hand, if you're feeling down or stressed, it might be harder to put yourself in someone else's shoes. It's important to acknowledge your own feelings and work on being open and understanding towards others! 🌟