Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch
Paper • 2311.03099 • Published • 36
How to use Badgids/Gonzo-Code-7B-GGUF with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="Badgids/Gonzo-Code-7B-GGUF") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Badgids/Gonzo-Code-7B-GGUF")
model = AutoModelForCausalLM.from_pretrained("Badgids/Gonzo-Code-7B-GGUF", device_map="auto")How to use Badgids/Gonzo-Code-7B-GGUF with llama.cpp:
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf Badgids/Gonzo-Code-7B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Badgids/Gonzo-Code-7B-GGUF:Q4_K_M
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Badgids/Gonzo-Code-7B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Badgids/Gonzo-Code-7B-GGUF:Q4_K_M
# 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-Code-7B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Badgids/Gonzo-Code-7B-GGUF:Q4_K_M
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-Code-7B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Badgids/Gonzo-Code-7B-GGUF:Q4_K_M
docker model run hf.co/Badgids/Gonzo-Code-7B-GGUF:Q4_K_M
How to use Badgids/Gonzo-Code-7B-GGUF with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Badgids/Gonzo-Code-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-Code-7B-GGUF",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/Badgids/Gonzo-Code-7B-GGUF:Q4_K_M
How to use Badgids/Gonzo-Code-7B-GGUF with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "Badgids/Gonzo-Code-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-Code-7B-GGUF",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'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-Code-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-Code-7B-GGUF",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use Badgids/Gonzo-Code-7B-GGUF with Ollama:
ollama run hf.co/Badgids/Gonzo-Code-7B-GGUF:Q4_K_M
How to use Badgids/Gonzo-Code-7B-GGUF with Unsloth Studio:
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-Code-7B-GGUF to start chatting
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-Code-7B-GGUF to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Badgids/Gonzo-Code-7B-GGUF to start chatting
How to use Badgids/Gonzo-Code-7B-GGUF with Docker Model Runner:
docker model run hf.co/Badgids/Gonzo-Code-7B-GGUF:Q4_K_M
How to use Badgids/Gonzo-Code-7B-GGUF with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Badgids/Gonzo-Code-7B-GGUF:Q4_K_M
lemonade run user.Gonzo-Code-7B-GGUF-Q4_K_M
lemonade list
This is a merge of pre-trained language models created using mergekit.
This model was merged using the DARE TIES merge method using eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO as a base.
The following models were included in the merge:
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: xingyaoww/CodeActAgent-Mistral-7b-v0.1
parameters:
density: 0.53
weight: 0.4
- model: Nondzu/Mistral-7B-Instruct-v0.2-code-ft
parameters:
density: 0.53
weight: 0.3
- model: beowolx/MistralHermes-CodePro-7B-v1
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
4-bit