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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Badgids/Gonzo-Chat-7B 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"
# 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",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

How to use Badgids/Gonzo-Chat-7B 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" \
    --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",
		"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" \
        --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",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Badgids/Gonzo-Chat-7B with Docker Model Runner:
```
docker model run hf.co/Badgids/Gonzo-Chat-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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