Instructions to use bfuzzy1/Gunny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bfuzzy1/Gunny with Transformers:

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

pipe = pipeline("text-generation", model="bfuzzy1/Gunny")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bfuzzy1/Gunny")
model = AutoModelForCausalLM.from_pretrained("bfuzzy1/Gunny")
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 bfuzzy1/Gunny with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="bfuzzy1/Gunny",
	filename="gunny_q4_k_m.gguf",
)

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

Inference
Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use bfuzzy1/Gunny with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf bfuzzy1/Gunny:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf bfuzzy1/Gunny:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf bfuzzy1/Gunny:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf bfuzzy1/Gunny: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 bfuzzy1/Gunny:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf bfuzzy1/Gunny: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 bfuzzy1/Gunny:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bfuzzy1/Gunny:Q4_K_M

Use Docker

docker model run hf.co/bfuzzy1/Gunny:Q4_K_M

LM Studio
Jan

vLLM

How to use bfuzzy1/Gunny with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bfuzzy1/Gunny"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bfuzzy1/Gunny",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bfuzzy1/Gunny:Q4_K_M

SGLang

How to use bfuzzy1/Gunny 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 "bfuzzy1/Gunny" \
    --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": "bfuzzy1/Gunny",
		"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 "bfuzzy1/Gunny" \
        --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": "bfuzzy1/Gunny",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use bfuzzy1/Gunny with Ollama:
```
ollama run hf.co/bfuzzy1/Gunny:Q4_K_M
```

Unsloth Studio new

How to use bfuzzy1/Gunny 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 bfuzzy1/Gunny 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 bfuzzy1/Gunny to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for bfuzzy1/Gunny to start chatting

Pi new

How to use bfuzzy1/Gunny with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf bfuzzy1/Gunny:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "bfuzzy1/Gunny:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use bfuzzy1/Gunny with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf bfuzzy1/Gunny:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default bfuzzy1/Gunny:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use bfuzzy1/Gunny with Docker Model Runner:
```
docker model run hf.co/bfuzzy1/Gunny:Q4_K_M
```

Lemonade

How to use bfuzzy1/Gunny with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bfuzzy1/Gunny:Q4_K_M

Run and chat with the model

lemonade run user.Gunny-Q4_K_M

List all available models

lemonade list

bfuzzy1 commited on Dec 19, 2024

Commit

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@@ -1,199 +1,56 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
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-## How to Get Started with the Model
-Use the code below to get started with the model.
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 ---
 library_name: transformers
+datasets:
+- bfuzzy1/gunny_v2_solo_dolo
+- bfuzzy1/gunny_x
 ---
+# Gunny
+Fine tuned on the Gunny_x and Gunny_v2_Solo_Dolo datasets
+- Model: Llama-3.2-3B-Instruct
+  - Quant: https://huggingface.co/bfuzzy1/Gunny/blob/main/gunny_q4_k_m.gguf
+## Disclaimer:
+AI is NOT a substitute for actual real help and should by no means be used as medical advice.
+## Going through some shit as a veteran? You're not alone...
+Resource infomation direct from source links.
+US:
+https://www.militaryonesource.mil/benefits/veterans-crisis-line/
+```
+The Veterans/Military Crisis Line is a free, confidential resource that provides Department of Veterans Affairs support for all service members, including members of the National Guard and reserve, and all veterans and their families, even if they are not registered with the VA or enrolled in VA health care. The caring, qualified responders at the Veterans/Military Crisis Line are specially trained and experienced in helping service members and veterans of all ages and circumstances.
+If you or someone you know is in a crisis, there is help – contact the Veterans/Military Crisis Line. Dial 988 then press 1 or text 838255.
+Or you can call:
+In Europe, 844-702-5495 or DSN 988
+In Southwest Asia, 855-422-7719 or DSN 988
+In the Pacific, 844-702-5493 or DSN 988
+```
+UK:
+https://www.gov.uk/guidance/managing-your-mental-health-as-a-veteran
+https://combatstress.org.uk/
+```
+If you have served or are currently serving in the UK Armed Forces, you can call the Combat Stress’ 24-hour mental health helplines.
+Veterans and their families can call
+0800 138 1619
+You can also text on 07537 173683 and email helpline@combatstress.org.uk Standard charges may apply for texts, please check with your provider.
+THE HELPLINE IS AVAILABLE 24 HOURS A DAY, 365 DAYS A YEAR.
+```