Instructions to use Badgids/Gonzo-Chat-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Badgids/Gonzo-Chat-7B-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="Badgids/Gonzo-Chat-7B-GGUF")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Badgids/Gonzo-Chat-7B-GGUF")
model = AutoModelForCausalLM.from_pretrained("Badgids/Gonzo-Chat-7B-GGUF")

llama-cpp-python

How to use Badgids/Gonzo-Chat-7B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Badgids/Gonzo-Chat-7B-GGUF",
	filename="Gonzo-Chat-7B-Q4_K_M.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Badgids/Gonzo-Chat-7B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Badgids/Gonzo-Chat-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Badgids/Gonzo-Chat-7B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Badgids/Gonzo-Chat-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Badgids/Gonzo-Chat-7B-GGUF: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 Badgids/Gonzo-Chat-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Badgids/Gonzo-Chat-7B-GGUF: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 Badgids/Gonzo-Chat-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Badgids/Gonzo-Chat-7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Badgids/Gonzo-Chat-7B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Badgids/Gonzo-Chat-7B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Badgids/Gonzo-Chat-7B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Badgids/Gonzo-Chat-7B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Badgids/Gonzo-Chat-7B-GGUF:Q4_K_M

SGLang

How to use Badgids/Gonzo-Chat-7B-GGUF 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 "Badgids/Gonzo-Chat-7B-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Badgids/Gonzo-Chat-7B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Badgids/Gonzo-Chat-7B-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Badgids/Gonzo-Chat-7B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use Badgids/Gonzo-Chat-7B-GGUF with Ollama:
```
ollama run hf.co/Badgids/Gonzo-Chat-7B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use Badgids/Gonzo-Chat-7B-GGUF 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 Badgids/Gonzo-Chat-7B-GGUF 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 Badgids/Gonzo-Chat-7B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Badgids/Gonzo-Chat-7B-GGUF to start chatting

Docker Model Runner
How to use Badgids/Gonzo-Chat-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/Badgids/Gonzo-Chat-7B-GGUF:Q4_K_M
```

Lemonade

How to use Badgids/Gonzo-Chat-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Badgids/Gonzo-Chat-7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Gonzo-Chat-7B-GGUF-Q4_K_M

List all available models

lemonade list

Gonzo-Chat-7B

Gonzo-Chat-7B is a merged LLM based on Mistral v0.01 with a 8192 Context length that likes to chat, roleplay, work with agents, do some lite programming, and then beat the brakes off you in the back alley...

The BEST Open Source 7B Street Fighting LLM of 2024!!!

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	66.63
AI2 Reasoning Challenge (25-Shot)	65.02
HellaSwag (10-Shot)	85.40
MMLU (5-Shot)	63.75
TruthfulQA (0-shot)	60.23
Winogrande (5-shot)	77.74
GSM8k (5-shot)	47.61

LLM-Colosseum Results

All contestents fought using the same LLM-Colosseum default settings. Each contestant fought 25 rounds with every other contestant.

https://github.com/OpenGenerativeAI/llm-colosseum

Gonzo-Chat-7B .vs Mistral v0.2, Dolphon-Mistral v0.2, Deepseek-Coder-6.7b-instruct

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the DARE TIES merge method using eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO
    # No parameters necessary for base model
  - model: cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser
    parameters:
      density: 0.53
      weight: 0.4
  - model:  NousResearch/Nous-Hermes-2-Mistral-7B-DPO
    parameters:
      density: 0.53
      weight: 0.3
  - model: Nondzu/Mistral-7B-Instruct-v0.2-code-ft
    parameters:
      density: 0.53
      weight: 0.3
merge_method: dare_ties
base_model: eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO
parameters:
  int8_mask: true
dtype: bfloat16