Instructions to use HelpingAI/HelpingAI-15B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HelpingAI/HelpingAI-15B with Transformers:

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

pipe = pipeline("text-generation", model="HelpingAI/HelpingAI-15B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("HelpingAI/HelpingAI-15B")
model = AutoModelForCausalLM.from_pretrained("HelpingAI/HelpingAI-15B")
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-15B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="HelpingAI/HelpingAI-15B",
	filename="helpingai-15b-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 HelpingAI/HelpingAI-15B 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-15B:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf HelpingAI/HelpingAI-15B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf HelpingAI/HelpingAI-15B:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf HelpingAI/HelpingAI-15B: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 HelpingAI/HelpingAI-15B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf HelpingAI/HelpingAI-15B: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 HelpingAI/HelpingAI-15B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf HelpingAI/HelpingAI-15B:Q4_K_M

Use Docker

docker model run hf.co/HelpingAI/HelpingAI-15B:Q4_K_M

LM Studio
Jan

vLLM

How to use HelpingAI/HelpingAI-15B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HelpingAI/HelpingAI-15B"
# 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-15B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/HelpingAI/HelpingAI-15B:Q4_K_M

SGLang

How to use HelpingAI/HelpingAI-15B 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-15B" \
    --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-15B",
		"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-15B" \
        --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-15B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use HelpingAI/HelpingAI-15B with Ollama:
```
ollama run hf.co/HelpingAI/HelpingAI-15B:Q4_K_M
```

Unsloth Studio new

How to use HelpingAI/HelpingAI-15B 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-15B 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-15B 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-15B to start chatting

Docker Model Runner
How to use HelpingAI/HelpingAI-15B with Docker Model Runner:
```
docker model run hf.co/HelpingAI/HelpingAI-15B:Q4_K_M
```

Lemonade

How to use HelpingAI/HelpingAI-15B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull HelpingAI/HelpingAI-15B:Q4_K_M

Run and chat with the model

lemonade run user.HelpingAI-15B-Q4_K_M

List all available models

lemonade list

Adding Evaluation Results

by leaderboard-pr-bot - opened Jul 25, 2024

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+112

-4

Files changed (1) hide show

README.md +112 -4

README.md CHANGED Viewed

@@ -1,8 +1,5 @@
 ---
 license: other
-license_name: helpingai
-license_link: LICENSE.md
-pipeline_tag: text-generation
 tags:
 - HelpingAI
 - Emotionally Intelligent
@@ -11,6 +8,104 @@ datasets:
 - OEvortex/SentimentSynth
 - OEvortex/EmotionalIntelligence-75k
 - Abhaykoul/Emotion
 ---
 # HelpingAI-15B: Emotionally Intelligent Conversational AI
@@ -166,4 +261,17 @@ Remember, it's important to choose devices that fit your specific needs and budg
 Also, make sure to test and maintain these devices regularly to ensure they're in working order. 🔧
-If you have any specific questions about any of these devices, feel free to ask! I'm here to help you stay safe and secure! 🌈

 ---
 license: other
 tags:
 - HelpingAI
 - Emotionally Intelligent
 - OEvortex/SentimentSynth
 - OEvortex/EmotionalIntelligence-75k
 - Abhaykoul/Emotion
+license_name: helpingai
+license_link: LICENSE.md
+pipeline_tag: text-generation
+model-index:
+- name: HelpingAI-15B
+  results:
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: IFEval (0-Shot)
+      type: HuggingFaceH4/ifeval
+      args:
+        num_few_shot: 0
+    metrics:
+    - type: inst_level_strict_acc and prompt_level_strict_acc
+      value: 20.3
+      name: strict accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=OEvortex/HelpingAI-15B
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: BBH (3-Shot)
+      type: BBH
+      args:
+        num_few_shot: 3
+    metrics:
+    - type: acc_norm
+      value: 1.82
+      name: normalized accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=OEvortex/HelpingAI-15B
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: MATH Lvl 5 (4-Shot)
+      type: hendrycks/competition_math
+      args:
+        num_few_shot: 4
+    metrics:
+    - type: exact_match
+      value: 0.0
+      name: exact match
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=OEvortex/HelpingAI-15B
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: GPQA (0-shot)
+      type: Idavidrein/gpqa
+      args:
+        num_few_shot: 0
+    metrics:
+    - type: acc_norm
+      value: 1.01
+      name: acc_norm
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=OEvortex/HelpingAI-15B
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: MuSR (0-shot)
+      type: TAUR-Lab/MuSR
+      args:
+        num_few_shot: 0
+    metrics:
+    - type: acc_norm
+      value: 2.73
+      name: acc_norm
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=OEvortex/HelpingAI-15B
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: MMLU-PRO (5-shot)
+      type: TIGER-Lab/MMLU-Pro
+      config: main
+      split: test
+      args:
+        num_few_shot: 5
+    metrics:
+    - type: acc
+      value: 1.24
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=OEvortex/HelpingAI-15B
+      name: Open LLM Leaderboard
 ---
 # HelpingAI-15B: Emotionally Intelligent Conversational AI
 Also, make sure to test and maintain these devices regularly to ensure they're in working order. 🔧
+If you have any specific questions about any of these devices, feel free to ask! I'm here to help you stay safe and secure! 🌈
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_OEvortex__HelpingAI-15B)
+|      Metric       |Value|
+|-------------------|----:|
+|Avg.               | 4.52|
+|IFEval (0-Shot)    |20.30|
+|BBH (3-Shot)       | 1.82|
+|MATH Lvl 5 (4-Shot)| 0.00|
+|GPQA (0-shot)      | 1.01|
+|MuSR (0-shot)      | 2.73|
+|MMLU-PRO (5-shot)  | 1.24|