Instructions to use QuantFactory/Chewy-Lemon-Cookie-11B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use QuantFactory/Chewy-Lemon-Cookie-11B-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="QuantFactory/Chewy-Lemon-Cookie-11B-GGUF")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("QuantFactory/Chewy-Lemon-Cookie-11B-GGUF", dtype="auto") - llama-cpp-python
How to use QuantFactory/Chewy-Lemon-Cookie-11B-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="QuantFactory/Chewy-Lemon-Cookie-11B-GGUF", filename="Chewy-Lemon-Cookie-11B.Q2_K.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 QuantFactory/Chewy-Lemon-Cookie-11B-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf QuantFactory/Chewy-Lemon-Cookie-11B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf QuantFactory/Chewy-Lemon-Cookie-11B-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 QuantFactory/Chewy-Lemon-Cookie-11B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf QuantFactory/Chewy-Lemon-Cookie-11B-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 QuantFactory/Chewy-Lemon-Cookie-11B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf QuantFactory/Chewy-Lemon-Cookie-11B-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 QuantFactory/Chewy-Lemon-Cookie-11B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf QuantFactory/Chewy-Lemon-Cookie-11B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/QuantFactory/Chewy-Lemon-Cookie-11B-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use QuantFactory/Chewy-Lemon-Cookie-11B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "QuantFactory/Chewy-Lemon-Cookie-11B-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "QuantFactory/Chewy-Lemon-Cookie-11B-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/QuantFactory/Chewy-Lemon-Cookie-11B-GGUF:Q4_K_M
- SGLang
How to use QuantFactory/Chewy-Lemon-Cookie-11B-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 "QuantFactory/Chewy-Lemon-Cookie-11B-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": "QuantFactory/Chewy-Lemon-Cookie-11B-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 "QuantFactory/Chewy-Lemon-Cookie-11B-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": "QuantFactory/Chewy-Lemon-Cookie-11B-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Ollama
How to use QuantFactory/Chewy-Lemon-Cookie-11B-GGUF with Ollama:
ollama run hf.co/QuantFactory/Chewy-Lemon-Cookie-11B-GGUF:Q4_K_M
- Unsloth Studio new
How to use QuantFactory/Chewy-Lemon-Cookie-11B-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 QuantFactory/Chewy-Lemon-Cookie-11B-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 QuantFactory/Chewy-Lemon-Cookie-11B-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for QuantFactory/Chewy-Lemon-Cookie-11B-GGUF to start chatting
- Docker Model Runner
How to use QuantFactory/Chewy-Lemon-Cookie-11B-GGUF with Docker Model Runner:
docker model run hf.co/QuantFactory/Chewy-Lemon-Cookie-11B-GGUF:Q4_K_M
- Lemonade
How to use QuantFactory/Chewy-Lemon-Cookie-11B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QuantFactory/Chewy-Lemon-Cookie-11B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Chewy-Lemon-Cookie-11B-GGUF-Q4_K_M
List all available models
lemonade list
QuantFactory/Chewy-Lemon-Cookie-11B-GGUF
This is quantized version of FallenMerick/Chewy-Lemon-Cookie-11B created using llama.cpp
Original Model Card
Chewy-Lemon-Cookie-11B
This is a merge of pre-trained language models created using mergekit.
GGUF quants:
- https://huggingface.co/backyardai/Chewy-Lemon-Cookie-11B-GGUF
- https://huggingface.co/mradermacher/Chewy-Lemon-Cookie-11B-GGUF
- https://huggingface.co/mradermacher/Chewy-Lemon-Cookie-11B-i1-GGUF
Merge Details
Merge Method
This model was merged using the following methods:
- passthrough
- task arithmetic
Models Merged
The following models were included in the merge:
- SanjiWatsuki/Kunoichi-7B
- SanjiWatsuki/Silicon-Maid-7B
- KatyTheCutie/LemonadeRP-4.5.3
- Sao10K/Fimbulvetr-11B-v2
Configuration
The following YAML configurations were used to produce this model:
slices:
- sources:
- model: SanjiWatsuki/Kunoichi-7B
layer_range: [0, 24]
- sources:
- model: SanjiWatsuki/Silicon-Maid-7B
layer_range: [8, 24]
- sources:
- model: KatyTheCutie/LemonadeRP-4.5.3
layer_range: [24, 32]
merge_method: passthrough
dtype: bfloat16
name: Big-Lemon-Cookie-11B-BF16
---
models:
- model: Big-Lemon-Cookie-11B-BF16
parameters:
weight: 0.85
- model: Sao10K/Fimbulvetr-11B-v2
parameters:
weight: 0.15
merge_method: task_arithmetic
base_model: Big-Lemon-Cookie-11B-BF16
dtype: bfloat16
name: Chewy-Lemon-Cookie-11B
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 21.91 |
| IFEval (0-Shot) | 48.75 |
| BBH (3-Shot) | 33.01 |
| MATH Lvl 5 (4-Shot) | 4.61 |
| GPQA (0-shot) | 3.91 |
| MuSR (0-shot) | 15.95 |
| MMLU-PRO (5-shot) | 25.19 |
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Evaluation results
- strict accuracy on IFEval (0-Shot)Open LLM Leaderboard48.750
- normalized accuracy on BBH (3-Shot)Open LLM Leaderboard33.010
- exact match on MATH Lvl 5 (4-Shot)Open LLM Leaderboard4.610
- acc_norm on GPQA (0-shot)Open LLM Leaderboard3.910
- acc_norm on MuSR (0-shot)Open LLM Leaderboard15.950
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard25.190
