Text Generation
GGUF
English
quantized
llama.cpp
scorecard
governance
validated
local-llm
on-device
agentic
tool-calling
function-calling
agents
ai-agents
rag
q4_k_m
q8_0
conversational
Instructions to use smarttasks/Yi-Coder-9B-Chat-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use smarttasks/Yi-Coder-9B-Chat-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf smarttasks/Yi-Coder-9B-Chat-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf smarttasks/Yi-Coder-9B-Chat-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf smarttasks/Yi-Coder-9B-Chat-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf smarttasks/Yi-Coder-9B-Chat-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 smarttasks/Yi-Coder-9B-Chat-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf smarttasks/Yi-Coder-9B-Chat-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 smarttasks/Yi-Coder-9B-Chat-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf smarttasks/Yi-Coder-9B-Chat-GGUF:Q4_K_M
Use Docker
docker model run hf.co/smarttasks/Yi-Coder-9B-Chat-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use smarttasks/Yi-Coder-9B-Chat-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "smarttasks/Yi-Coder-9B-Chat-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": "smarttasks/Yi-Coder-9B-Chat-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/smarttasks/Yi-Coder-9B-Chat-GGUF:Q4_K_M
- Ollama
How to use smarttasks/Yi-Coder-9B-Chat-GGUF with Ollama:
ollama run hf.co/smarttasks/Yi-Coder-9B-Chat-GGUF:Q4_K_M
- Unsloth Studio
How to use smarttasks/Yi-Coder-9B-Chat-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 smarttasks/Yi-Coder-9B-Chat-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 smarttasks/Yi-Coder-9B-Chat-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for smarttasks/Yi-Coder-9B-Chat-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use smarttasks/Yi-Coder-9B-Chat-GGUF with Docker Model Runner:
docker model run hf.co/smarttasks/Yi-Coder-9B-Chat-GGUF:Q4_K_M
- Lemonade
How to use smarttasks/Yi-Coder-9B-Chat-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull smarttasks/Yi-Coder-9B-Chat-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Yi-Coder-9B-Chat-GGUF-Q4_K_M
List all available models
lemonade list
| { | |
| "generated": "2026-07-18T12:30:55", | |
| "modes": [ | |
| "cpu", | |
| "gpu0:NVIDIA_GeForce_RTX_3090", | |
| "gpu1:NVIDIA_RTX_A4000", | |
| "gpu2:NVIDIA_RTX_A4000" | |
| ], | |
| "results": [ | |
| { | |
| "file": "Yi-Coder-9B-Chat-Q4_K_M.gguf", | |
| "size_mb": 5082.1, | |
| "cpu": { | |
| "load_seconds": 2.4, | |
| "gen_tps_avg": 7.8, | |
| "prefill_tps_avg": 32.1 | |
| }, | |
| "gpu0:NVIDIA_GeForce_RTX_3090": { | |
| "load_seconds": 2.0, | |
| "gen_tps_avg": 120.1, | |
| "prefill_tps_avg": 681.2 | |
| }, | |
| "gpu1:NVIDIA_RTX_A4000": { | |
| "load_seconds": 2.0, | |
| "gen_tps_avg": 64.2, | |
| "prefill_tps_avg": 490.9 | |
| }, | |
| "gpu2:NVIDIA_RTX_A4000": { | |
| "load_seconds": 2.0, | |
| "gen_tps_avg": 64.8, | |
| "prefill_tps_avg": 493.6 | |
| }, | |
| "gen_tps_avg": 120.1, | |
| "load_seconds": 2.4, | |
| "prefill_tps_avg": 32.1, | |
| "gpu_speedup": 15.4 | |
| }, | |
| { | |
| "file": "Yi-Coder-9B-Chat-Q5_K_M.gguf", | |
| "size_mb": 5968.3, | |
| "cpu": { | |
| "load_seconds": 2.4, | |
| "gen_tps_avg": 6.8, | |
| "prefill_tps_avg": 27.8 | |
| }, | |
| "gpu0:NVIDIA_GeForce_RTX_3090": { | |
| "load_seconds": 2.0, | |
| "gen_tps_avg": 107.9, | |
| "prefill_tps_avg": 646.9 | |
| }, | |
| "gpu1:NVIDIA_RTX_A4000": { | |
| "load_seconds": 3.2, | |
| "gen_tps_avg": 56.4, | |
| "prefill_tps_avg": 449.2 | |
| }, | |
| "gpu2:NVIDIA_RTX_A4000": { | |
| "load_seconds": 2.0, | |
| "gen_tps_avg": 56.9, | |
| "prefill_tps_avg": 470.7 | |
| }, | |
| "gen_tps_avg": 107.9, | |
| "load_seconds": 2.4, | |
| "prefill_tps_avg": 27.8, | |
| "gpu_speedup": 15.9 | |
| }, | |
| { | |
| "file": "Yi-Coder-9B-Chat-Q6_K.gguf", | |
| "size_mb": 6910.0, | |
| "cpu": { | |
| "load_seconds": 2.5, | |
| "gen_tps_avg": 5.9, | |
| "prefill_tps_avg": 24.1 | |
| }, | |
| "gpu0:NVIDIA_GeForce_RTX_3090": { | |
| "load_seconds": 3.1, | |
| "gen_tps_avg": 93.2, | |
| "prefill_tps_avg": 634.9 | |
| }, | |
| "gpu1:NVIDIA_RTX_A4000": { | |
| "load_seconds": 3.1, | |
| "gen_tps_avg": 47.1, | |
| "prefill_tps_avg": 451.8 | |
| }, | |
| "gpu2:NVIDIA_RTX_A4000": { | |
| "load_seconds": 3.1, | |
| "gen_tps_avg": 48.6, | |
| "prefill_tps_avg": 452.2 | |
| }, | |
| "gen_tps_avg": 93.2, | |
| "load_seconds": 2.5, | |
| "prefill_tps_avg": 24.1, | |
| "gpu_speedup": 15.8 | |
| }, | |
| { | |
| "file": "Yi-Coder-9B-Chat-Q8_0.gguf", | |
| "size_mb": 8949.2, | |
| "cpu": { | |
| "load_seconds": 2.7, | |
| "gen_tps_avg": 4.8, | |
| "prefill_tps_avg": 18.7 | |
| }, | |
| "gpu0:NVIDIA_GeForce_RTX_3090": { | |
| "load_seconds": 3.2, | |
| "gen_tps_avg": 80.5, | |
| "prefill_tps_avg": 662.1 | |
| }, | |
| "gpu1:NVIDIA_RTX_A4000": { | |
| "load_seconds": 3.2, | |
| "gen_tps_avg": 40.2, | |
| "prefill_tps_avg": 447.2 | |
| }, | |
| "gpu2:NVIDIA_RTX_A4000": { | |
| "load_seconds": 3.6, | |
| "gen_tps_avg": 40.3, | |
| "prefill_tps_avg": 437.5 | |
| }, | |
| "gen_tps_avg": 80.5, | |
| "load_seconds": 2.7, | |
| "prefill_tps_avg": 18.7, | |
| "gpu_speedup": 16.8 | |
| } | |
| ] | |
| } |