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

Libraries

How to use HelpingAI/HelpingAI-9B with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

llama-cpp-python

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

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="HelpingAI/HelpingAI-9B",
	filename="helpingai-9b.Q4_0.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 HelpingAI/HelpingAI-9B with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf HelpingAI/HelpingAI-9B:Q4_0
# Run inference directly in the terminal:
llama-cli -hf HelpingAI/HelpingAI-9B:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf HelpingAI/HelpingAI-9B:Q4_0
# Run inference directly in the terminal:
llama-cli -hf HelpingAI/HelpingAI-9B:Q4_0

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 HelpingAI/HelpingAI-9B:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf HelpingAI/HelpingAI-9B:Q4_0

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 HelpingAI/HelpingAI-9B:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf HelpingAI/HelpingAI-9B:Q4_0

Use Docker

docker model run hf.co/HelpingAI/HelpingAI-9B:Q4_0

LM Studio
Jan

vLLM

How to use HelpingAI/HelpingAI-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HelpingAI/HelpingAI-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": "HelpingAI/HelpingAI-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/HelpingAI/HelpingAI-9B:Q4_0

SGLang

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

Ollama
How to use HelpingAI/HelpingAI-9B with Ollama:
```
ollama run hf.co/HelpingAI/HelpingAI-9B:Q4_0
```

Unsloth Studio new

How to use HelpingAI/HelpingAI-9B 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 HelpingAI/HelpingAI-9B 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 HelpingAI/HelpingAI-9B to start chatting

Using HuggingFace Spaces for Unsloth

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

Docker Model Runner
How to use HelpingAI/HelpingAI-9B with Docker Model Runner:
```
docker model run hf.co/HelpingAI/HelpingAI-9B:Q4_0
```

Lemonade

How to use HelpingAI/HelpingAI-9B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull HelpingAI/HelpingAI-9B:Q4_0

Run and chat with the model

lemonade run user.HelpingAI-9B-Q4_0

List all available models

lemonade list

Abhaykoul commited on Jun 10, 2024

Commit

2878205

verified ·

1 Parent(s): 35c626c

Update README.md

Browse files

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README.md +33 -31

README.md CHANGED Viewed

@@ -36,41 +36,43 @@ HelpingAI-9B has achieved an impressive Emotional Quotient (EQ) of 89.23, surpas
 ## Usage code
 ```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
-device = "cuda" # the device to load the model onto
-model = AutoModelForCausalLM.from_pretrained(
-    "OEvortex/HelpingAI-9B",
-    torch_dtype='auto',
-    device_map="auto"
-)
 tokenizer = AutoTokenizer.from_pretrained("OEvortex/HelpingAI-9B")
-prompt = "Express joy and excitement about visiting a new place"
-messages = [
-    # {"role": "system", "content": "You are a helpful AI assistant."},
-    {"role": "user", "content": prompt}
-]
-text = tokenizer.apply_chat_template(
-    messages,
-    tokenize=False,
-    add_generation_prompt=True
-)
-model_inputs = tokenizer([text], return_tensors="pt").to(device)
-generated_ids = model.generate(
-    model_inputs.input_ids,
-    max_new_tokens=1024,
-    eos_token_id=tokenizer.eos_token_id,
-    temperature=0.25,
-)
-generated_ids = [
-    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
-]
-response = tokenizer.batch_decode(generated_ids)[0]
-print(response)
 ```
 *Directly using this model from GGUF*

 ## Usage code
 ```python
 import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
+# Let's bring in the big guns! Our super cool HelpingAI-9B model
+model = AutoModelForCausalLM.from_pretrained("OEvortex/HelpingAI-9B").to("cuda")
+# We also need the special HelpingAI translator to understand our chats
 tokenizer = AutoTokenizer.from_pretrained("OEvortex/HelpingAI-9B")
+# This TextStreamer thingy is our secret weapon for super smooth conversation flow
+streamer = TextStreamer(tokenizer)
+# Now, here comes the magic! ✨ This is the basic template for our chat
+prompt = """
+<|im_start|>system: {system}
+<|im_end|>
+<|im_start|>user: {insaan}
+<|im_end|>
+<|im_start|>assistant:
+"""
+# Okay, enough chit-chat, let's get down to business!  Here's what will be our system prompt
+system = "You are HelpingAI a emotional AI always answer my question in HelpingAI style"
+# And the insaan is curious (like you!) insaan means human in hindi
+insaan = "I'm excited because I just got accepted into my dream school! I wanted to share the good news with someone."
+# Now we combine system and user messages into the template, like adding sprinkles to our conversation cupcake
+prompt = prompt.format(system=system, insaan=insaan)
+# Time to chat! We'll use the tokenizer to translate our text into a language the model understands
+inputs = tokenizer(prompt, return_tensors="pt", return_attention_mask=False).to("cuda")
+# Here comes the fun part!  Let's unleash the power of HelpingAI-3B to generate some awesome text
+generated_text = model.generate(**inputs, max_length=3084, top_p=0.95, do_sample=True, temperature=0.6, use_cache=True, streamer=streamer)
 ```
 *Directly using this model from GGUF*