Text Generation
GGUF
English
Mixture of Experts
code
pruning
distillation
mixed-precision
local-inference
experimental
conversational
Instructions to use WeeAIResearch/Phoenix-69B-A6.6B 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 WeeAIResearch/Phoenix-69B-A6.6B 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 WeeAIResearch/Phoenix-69B-A6.6B # Run inference directly in the terminal: llama cli -hf WeeAIResearch/Phoenix-69B-A6.6B
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf WeeAIResearch/Phoenix-69B-A6.6B # Run inference directly in the terminal: llama cli -hf WeeAIResearch/Phoenix-69B-A6.6B
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf WeeAIResearch/Phoenix-69B-A6.6B # Run inference directly in the terminal: ./build/bin/llama cli -hf WeeAIResearch/Phoenix-69B-A6.6B
Use Docker
docker model run hf.co/WeeAIResearch/Phoenix-69B-A6.6B
- Llama new
- LM Studio
- Jan
- vLLM
How to use WeeAIResearch/Phoenix-69B-A6.6B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "WeeAIResearch/Phoenix-69B-A6.6B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "WeeAIResearch/Phoenix-69B-A6.6B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/WeeAIResearch/Phoenix-69B-A6.6B
- Ollama
How to use WeeAIResearch/Phoenix-69B-A6.6B with Ollama:
ollama run hf.co/WeeAIResearch/Phoenix-69B-A6.6B
- Unsloth Desktop
- Pi
How to use WeeAIResearch/Phoenix-69B-A6.6B with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf WeeAIResearch/Phoenix-69B-A6.6B
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": "WeeAIResearch/Phoenix-69B-A6.6B" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use WeeAIResearch/Phoenix-69B-A6.6B with Docker Model Runner:
docker model run hf.co/WeeAIResearch/Phoenix-69B-A6.6B
- Lemonade
How to use WeeAIResearch/Phoenix-69B-A6.6B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull WeeAIResearch/Phoenix-69B-A6.6B
Run and chat with the model
lemonade run user.Phoenix-69B-A6.6B-{{QUANT_TAG}}List all available models
lemonade list
- Hermes Agent
How to use WeeAIResearch/Phoenix-69B-A6.6B with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf WeeAIResearch/Phoenix-69B-A6.6B
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 WeeAIResearch/Phoenix-69B-A6.6B
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use WeeAIResearch/Phoenix-69B-A6.6B with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf WeeAIResearch/Phoenix-69B-A6.6B
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 "WeeAIResearch/Phoenix-69B-A6.6B" \ --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"
File size: 1,855 Bytes
7428366 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | """Build a machine-local exact-expert map from GGUF tensor metadata."""
from pathlib import Path
import argparse,struct,sys
def main():
p=argparse.ArgumentParser();p.add_argument('model',type=Path);p.add_argument('output',type=Path);p.add_argument('--llama-source',type=Path,default=Path('llama.cpp'));a=p.parse_args()
sys.path.insert(0,str(a.llama_source.resolve()/'gguf-py'))
import gguf
model=a.model.resolve();st=model.stat();reader=gguf.GGUFReader(model);ts={t.name:t for t in reader.tensors}
payload=bytearray(b'WEEPF001'+struct.pack('<QQQQI',st.st_size,st.st_dev,st.st_ino,st.st_mtime_ns,48));count=0
for layer in range(48):
experts=[]
for tier in range(4):
suffix='' if tier==0 else f'_tier{tier}'
names=[f'blk.{layer}.ffn_{kind}_exps{suffix}.weight' for kind in ('up','gate','down')]
if names[0] not in ts:
assert all(n not in ts for n in names);continue
tensors=[ts[n] for n in names];n=int(tensors[0].shape[2])
assert all(len(t.shape)==3 and int(t.shape[2])==n and t.n_bytes%n==0 for t in tensors)
for e in range(n):
ranges=[]
for t in tensors:
length=int(t.n_bytes)//n;offset=int(t.data_offset)+e*length
assert 0<length<=8*2**20 and 0<=offset<offset+length<=st.st_size
ranges.append((offset,length))
experts.append(ranges)
assert len(experts)==int(ts[f'blk.{layer}.ffn_gate_inp.weight'].shape[1]);count+=len(experts)
payload+=struct.pack('<I',len(experts))
for expert in experts:
for offset,length in expert:payload+=struct.pack('<QQ',offset,length)
a.output.write_bytes(payload);print(f'Mapped {count} expert slots into {a.output}')
if __name__=='__main__':main()
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