Text Generation
Transformers
GGUF
English
Ganda
quantized
llama.cpp
ollama
ugandan-languages
translation
qwen
conversational
Instructions to use Sunbird/Sunflower-14B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sunbird/Sunflower-14B-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Sunbird/Sunflower-14B-GGUF") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Sunbird/Sunflower-14B-GGUF", dtype="auto") - llama-cpp-python
How to use Sunbird/Sunflower-14B-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Sunbird/Sunflower-14B-GGUF", filename="sunflower-14B-f16.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use Sunbird/Sunflower-14B-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Sunbird/Sunflower-14B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Sunbird/Sunflower-14B-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 Sunbird/Sunflower-14B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Sunbird/Sunflower-14B-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 Sunbird/Sunflower-14B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Sunbird/Sunflower-14B-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 Sunbird/Sunflower-14B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Sunbird/Sunflower-14B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/Sunbird/Sunflower-14B-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Sunbird/Sunflower-14B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Sunbird/Sunflower-14B-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Sunbird/Sunflower-14B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Sunbird/Sunflower-14B-GGUF:Q4_K_M
- SGLang
How to use Sunbird/Sunflower-14B-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 "Sunbird/Sunflower-14B-GGUF" \ --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": "Sunbird/Sunflower-14B-GGUF", "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 "Sunbird/Sunflower-14B-GGUF" \ --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": "Sunbird/Sunflower-14B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use Sunbird/Sunflower-14B-GGUF with Ollama:
ollama run hf.co/Sunbird/Sunflower-14B-GGUF:Q4_K_M
- Unsloth Studio new
How to use Sunbird/Sunflower-14B-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 Sunbird/Sunflower-14B-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 Sunbird/Sunflower-14B-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Sunbird/Sunflower-14B-GGUF to start chatting
- Pi new
How to use Sunbird/Sunflower-14B-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf Sunbird/Sunflower-14B-GGUF: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": "Sunbird/Sunflower-14B-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Sunbird/Sunflower-14B-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf Sunbird/Sunflower-14B-GGUF: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 Sunbird/Sunflower-14B-GGUF:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use Sunbird/Sunflower-14B-GGUF with Docker Model Runner:
docker model run hf.co/Sunbird/Sunflower-14B-GGUF:Q4_K_M
- Lemonade
How to use Sunbird/Sunflower-14B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Sunbird/Sunflower-14B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Sunflower-14B-GGUF-Q4_K_M
List all available models
lemonade list
Add GGUF quantized Sunflower models
Browse files- .gitattributes +7 -0
- Modelfile +22 -0
- README.md +91 -0
- sunflower-imatrix.dat +3 -0
- sunflower-merged-f16.gguf +3 -0
- sunflower-q4_k_m.gguf +3 -0
- sunflower-q5_k_m.gguf +3 -0
- sunflower-q5_k_s.gguf +3 -0
- sunflower-q6_k.gguf +3 -0
- sunflower-q8_0.gguf +3 -0
.gitattributes
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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sunflower-imatrix.dat filter=lfs diff=lfs merge=lfs -text
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sunflower-merged-f16.gguf filter=lfs diff=lfs merge=lfs -text
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sunflower-q4_k_m.gguf filter=lfs diff=lfs merge=lfs -text
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sunflower-q5_k_m.gguf filter=lfs diff=lfs merge=lfs -text
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sunflower-q5_k_s.gguf filter=lfs diff=lfs merge=lfs -text
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sunflower-q6_k.gguf filter=lfs diff=lfs merge=lfs -text
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sunflower-q8_0.gguf filter=lfs diff=lfs merge=lfs -text
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Modelfile
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FROM sunflower-q4_k_m.gguf
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# System message
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SYSTEM """You are a linguist and translator specialising in Ugandan languages, made by Sunbird AI."""
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TEMPLATE """<|im_start|>system
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{{ .System }}<|im_end|>
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<|im_start|>user
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{{ .Prompt }}<|im_end|>
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<|im_start|>assistant
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{{ .Response }}<|im_end|>"""
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# Stop tokens
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PARAMETER stop "<|im_start|>"
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PARAMETER stop "<|im_end|>"
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# Quality parameters
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PARAMETER temperature 0.3
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PARAMETER top_p 0.95
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PARAMETER top_k 40
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PARAMETER repeat_penalty 1.1
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PARAMETER num_ctx 4096
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README.md
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---
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license: apache-2.0
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base_model: Sunbird/qwen3-14b-sunflower-merged
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tags:
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- quantized
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- gguf
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- llama.cpp
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- ollama
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- ugandan-languages
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- translation
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- qwen
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library_name: transformers
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pipeline_tag: text-generation
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language:
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- en
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- lg
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---
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# Sunflower 14B - GGUF
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GGUF quantized versions of the Sunflower model for Ugandan language translation tasks.
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## Model Details
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- **Base Model**: [Sunbird/qwen3-14b-sunflower-merged](https://huggingface.co/Sunbird/qwen3-14b-sunflower-merged)
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- **Model Size**: 14B parameters
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- **Architecture**: Qwen2.5
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- **Quantization**: K-means quantization with importance matrix
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- **Languages**: English, Luganda, and other Ugandan languages
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## Available Files
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| Filename | Quant type | File Size | Description |
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| -------- | ---------- | --------- | ----------- |
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| sunflower-merged-f16.gguf | F16 | 28GB | Original precision |
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| sunflower-q8_0.gguf | Q8_0 | 15GB | Highest quality quantized |
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| sunflower-q6_k.gguf | Q6_K | 12GB | High quality |
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| sunflower-q5_k_m.gguf | Q5_K_M | 9.8GB | Balanced quality/size |
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| sunflower-q5_k_s.gguf | Q5_K_S | 9.6GB | Smaller Q5 variant |
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| sunflower-q4_k_m.gguf | Q4_K_M | 8.4GB | Recommended for most users |
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| sunflower-imatrix.dat | - | 7.4MB | Importance matrix data |
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## Usage
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### llama.cpp
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```bash
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# Download model
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huggingface-cli download Sunbird/Sunflower-14B-GGUF sunflower-q4_k_m.gguf --local-dir .
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# Run inference
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./llama-cli -m sunflower-q4_k_m.gguf -p "Translate to Luganda: Hello, how are you today?"
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```
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### Ollama
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```bash
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# Create Modelfile
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echo 'FROM ./sunflower-q4_k_m.gguf' > Modelfile
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# Create model
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ollama create sunflower-14b -f Modelfile
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# Use model
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ollama run sunflower-14b "Translate to Luganda: Good morning"
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```
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### Python (llama-cpp-python)
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```python
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from llama_cpp import Llama
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llm = Llama(model_path="sunflower-q4_k_m.gguf")
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result = llm("Translate to Luganda: How are you?")
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print(result['choices'][0]['text'])
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```
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## Performance Notes
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| 79 |
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- **Q4_K_M**: Recommended for most use cases
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- **Q5_K_M**: Better quality with moderate size increase
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- **Q6_K**: High quality for production use
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- **Q8_0**: Near-lossless quality
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## Technical Details
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| 86 |
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Quantized using llama.cpp with importance matrix calibration for optimal quality preservation.
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## License
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| 90 |
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Apache 2.0
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sunflower-imatrix.dat
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version https://git-lfs.github.com/spec/v1
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oid sha256:9717ce1e0643a19b3686e86162ce04db8bd01635954eb85284826a3834ebc0f8
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size 7743552
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sunflower-merged-f16.gguf
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version https://git-lfs.github.com/spec/v1
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oid sha256:eded257f1acad597e1ddda3f6e69cd387b58bfe8f6ef45dbcae724b68f0b1ba5
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size 29543423264
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sunflower-q4_k_m.gguf
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version https://git-lfs.github.com/spec/v1
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oid sha256:9d548245e1b7d4d52e9dad363e3f0076dbf8ee8a4e9eaf66332c8b856cfd9bbc
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size 9001753088
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sunflower-q5_k_m.gguf
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version https://git-lfs.github.com/spec/v1
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oid sha256:23c419c7560a205cecc991ca95bd0f7d55b641d1d250efe92172b58c39fb3ee6
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size 10514569728
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sunflower-q5_k_s.gguf
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version https://git-lfs.github.com/spec/v1
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oid sha256:2d1aa7f358e345bf0bad2ce21f82fb7e830e9650ab5ce6ad2d54dd6362bd9cb6
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size 10263894528
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sunflower-q6_k.gguf
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version https://git-lfs.github.com/spec/v1
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oid sha256:5b2074f4414b47b05c7d468cebc9c9ed2a21443cdd8e58cb489d20be4693b942
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size 12121937408
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sunflower-q8_0.gguf
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version https://git-lfs.github.com/spec/v1
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oid sha256:e50d50e7cfd15af8ad6ae0accdf13a8fe740d7daca95d38a36c78034ee8d4f9b
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size 15698533888
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