Text Generation
GGUF
Chinese
English
llama.cpp
lora
gemma-4
transfer-learning
dialogue
chinese
english
conversational
Instructions to use ZichenAI/Baihu-V1-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 ZichenAI/Baihu-V1-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 ZichenAI/Baihu-V1-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf ZichenAI/Baihu-V1-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 ZichenAI/Baihu-V1-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf ZichenAI/Baihu-V1-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 ZichenAI/Baihu-V1-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf ZichenAI/Baihu-V1-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 ZichenAI/Baihu-V1-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf ZichenAI/Baihu-V1-GGUF:Q4_K_M
Use Docker
docker model run hf.co/ZichenAI/Baihu-V1-GGUF:Q4_K_M
- Llama new
- LM Studio
- Jan
- vLLM
How to use ZichenAI/Baihu-V1-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ZichenAI/Baihu-V1-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": "ZichenAI/Baihu-V1-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ZichenAI/Baihu-V1-GGUF:Q4_K_M
- Ollama
How to use ZichenAI/Baihu-V1-GGUF with Ollama:
ollama run hf.co/ZichenAI/Baihu-V1-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use ZichenAI/Baihu-V1-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ZichenAI/Baihu-V1-GGUF:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "ZichenAI/Baihu-V1-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use ZichenAI/Baihu-V1-GGUF with Docker Model Runner:
docker model run hf.co/ZichenAI/Baihu-V1-GGUF:Q4_K_M
- Lemonade
How to use ZichenAI/Baihu-V1-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ZichenAI/Baihu-V1-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Baihu-V1-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use ZichenAI/Baihu-V1-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ZichenAI/Baihu-V1-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 ZichenAI/Baihu-V1-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use ZichenAI/Baihu-V1-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ZichenAI/Baihu-V1-GGUF:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "ZichenAI/Baihu-V1-GGUF:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Download benchmarks/summary.json from ZichenAI/Baihu-V1-GGUF: direct link, hf CLI and curl.
- Browser
- Download file 4.09 kB
-
https://huggingface.co/ZichenAI/Baihu-V1-GGUF/resolve/main/benchmarks/summary.json
- Command line
-
hf download hf://ZichenAI/Baihu-V1-GGUF/benchmarks/summary.json
-
curl -L -o summary.json https://huggingface.co/ZichenAI/Baihu-V1-GGUF/resolve/main/benchmarks/summary.json
4.09 kB
| { | |
| "benchmark": "Baihu-V1 vs google/gemma-4-E2B-it", | |
| "hardware": "1x NVIDIA GeForce RTX 4090 D (24 GB), driver 580.119.02", | |
| "held_out_transfer_hf_bf16": { | |
| "label": "321 held-out conversations, HF bf16 checkpoints", | |
| "gemma_4_e2b_it_base": { | |
| "n": 321, | |
| "rouge_l": 0.2359, | |
| "plain_text_rate": 0.0156, | |
| "reply_len_ratio": 3.8645, | |
| "num_recall": 0.5795, | |
| "num_recall_n": 240, | |
| "reply_chars": 432.3, | |
| "gen_seconds": 203.9 | |
| }, | |
| "baihu_v1": { | |
| "n": 321, | |
| "rouge_l": 0.4321, | |
| "plain_text_rate": 1.0, | |
| "reply_len_ratio": 0.9678, | |
| "num_recall": 0.5282, | |
| "num_recall_n": 240, | |
| "reply_chars": 113.7, | |
| "gen_seconds": 81.7 | |
| } | |
| }, | |
| "held_out_transfer_gguf_q4_k_m": { | |
| "label": "321 held-out conversations, published Q4_K_M GGUFs", | |
| "gemma_4_e2b_it_base": { | |
| "n": 321, | |
| "rouge_l": 0.2345, | |
| "plain_text_rate": 0.0125, | |
| "reply_len_ratio": 3.8524, | |
| "num_recall": 0.57, | |
| "num_recall_n": 240, | |
| "reply_chars": 432.2, | |
| "gen_seconds": 192.1 | |
| }, | |
| "baihu_v1": { | |
| "n": 321, | |
| "rouge_l": 0.4194, | |
| "plain_text_rate": 1.0, | |
| "reply_len_ratio": 0.9592, | |
| "num_recall": 0.5121, | |
| "num_recall_n": 240, | |
| "reply_chars": 113.9, | |
| "gen_seconds": 62.1 | |
| } | |
| }, | |
| "held_out_perplexity_q4_k_m": { | |
| "corpus": "321 held-out conversations, 60 x 2048-token chunks (llama-perplexity)", | |
| "gemma_4_e2b_it_q4_k_m": 13.7372, | |
| "baihu_v1_q4_k_m": 5.2833, | |
| "note": "llama-perplexity reports a standard error of +/-0.26 / +/-0.07" | |
| }, | |
| "mmlu_5shot": { | |
| "n": 500, | |
| "n_shot": 5, | |
| "gemma_4_e2b_it": 0.534, | |
| "baihu_v1": 0.526, | |
| "delta": -0.008, | |
| "by_subject": { | |
| "astronomy": { | |
| "base": 0.54, | |
| "baihu_v1": 0.52 | |
| }, | |
| "computer_security": { | |
| "base": 0.44, | |
| "baihu_v1": 0.5 | |
| }, | |
| "elementary_mathematics": { | |
| "base": 0.48, | |
| "baihu_v1": 0.48 | |
| }, | |
| "formal_logic": { | |
| "base": 0.58, | |
| "baihu_v1": 0.6 | |
| }, | |
| "high_school_biology": { | |
| "base": 0.54, | |
| "baihu_v1": 0.54 | |
| }, | |
| "high_school_mathematics": { | |
| "base": 0.44, | |
| "baihu_v1": 0.4 | |
| }, | |
| "high_school_physics": { | |
| "base": 0.38, | |
| "baihu_v1": 0.48 | |
| }, | |
| "high_school_world_history": { | |
| "base": 0.64, | |
| "baihu_v1": 0.64 | |
| }, | |
| "logical_fallacies": { | |
| "base": 0.64, | |
| "baihu_v1": 0.54 | |
| }, | |
| "world_religions": { | |
| "base": 0.66, | |
| "baihu_v1": 0.56 | |
| } | |
| } | |
| }, | |
| "llama_bench": [ | |
| { | |
| "model": "gemma-4-E2B-it-Q4_K_M", | |
| "n_prompt": 512, | |
| "n_gen": 0, | |
| "avg_ts": 15094.8, | |
| "stddev_ts": 2731.5, | |
| "backends": "CUDA", | |
| "gpu": "NVIDIA GeForce RTX 4090 D", | |
| "n_gpu_layers": 99 | |
| }, | |
| { | |
| "model": "gemma-4-E2B-it-Q4_K_M", | |
| "n_prompt": 0, | |
| "n_gen": 128, | |
| "avg_ts": 307.8, | |
| "stddev_ts": 1.7, | |
| "backends": "CUDA", | |
| "gpu": "NVIDIA GeForce RTX 4090 D", | |
| "n_gpu_layers": 99 | |
| }, | |
| { | |
| "model": "Baihu-V1-Q4_K_M", | |
| "n_prompt": 512, | |
| "n_gen": 0, | |
| "avg_ts": 15643.3, | |
| "stddev_ts": 2944.9, | |
| "backends": "CUDA", | |
| "gpu": "NVIDIA GeForce RTX 4090 D", | |
| "n_gpu_layers": 99 | |
| }, | |
| { | |
| "model": "Baihu-V1-Q4_K_M", | |
| "n_prompt": 0, | |
| "n_gen": 128, | |
| "avg_ts": 307.1, | |
| "stddev_ts": 3.0, | |
| "backends": "CUDA", | |
| "gpu": "NVIDIA GeForce RTX 4090 D", | |
| "n_gpu_layers": 99 | |
| } | |
| ], | |
| "training": { | |
| "base_model": "google/gemma-4-E2B-it", | |
| "method": "LoRA r=16 alpha=32 dropout=0.05 on language_model q/k/v/o/gate/up/down", | |
| "trainable_params": 24158208, | |
| "total_params": 5128455712, | |
| "train_conversations": 973, | |
| "val_conversations": 321, | |
| "epochs": 3, | |
| "optimiser_steps": 183, | |
| "batch": "4 x grad_accum 4", | |
| "learning_rate": 0.0002, | |
| "max_seq_len": 768, | |
| "precision": "bf16", | |
| "wall_time_minutes": 6.17, | |
| "train_loss": 1.165, | |
| "eval_loss": 1.536, | |
| "seed": 20260930 | |
| } | |
| } |