Instructions to use tchbcb/samai-9b 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 tchbcb/samai-9b 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 tchbcb/samai-9b:Q4_K_M # Run inference directly in the terminal: llama cli -hf tchbcb/samai-9b:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf tchbcb/samai-9b:Q4_K_M # Run inference directly in the terminal: llama cli -hf tchbcb/samai-9b: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 tchbcb/samai-9b:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf tchbcb/samai-9b: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 tchbcb/samai-9b:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf tchbcb/samai-9b:Q4_K_M
Use Docker
docker model run hf.co/tchbcb/samai-9b:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use tchbcb/samai-9b with Ollama:
ollama run hf.co/tchbcb/samai-9b:Q4_K_M
- Unsloth Desktop
- Pi
How to use tchbcb/samai-9b with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf tchbcb/samai-9b: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": "tchbcb/samai-9b:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use tchbcb/samai-9b with Docker Model Runner:
docker model run hf.co/tchbcb/samai-9b:Q4_K_M
- Lemonade
How to use tchbcb/samai-9b with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tchbcb/samai-9b:Q4_K_M
Run and chat with the model
lemonade run user.samai-9b-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use tchbcb/samai-9b with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf tchbcb/samai-9b: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 tchbcb/samai-9b:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use tchbcb/samai-9b with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf tchbcb/samai-9b: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 "tchbcb/samai-9b: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"
File size: 4,267 Bytes
5073c4a 10953c9 5073c4a | 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 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""colab_compile.py — Colab T4 专用 llama.cpp CUDA 编译链 (sm_75)
编译 -> 打包 llama_cpp_cuda_colab_t4.tar.gz -> 第一时间推仓 (tchbcb/samai-9b)
幂等旗标: /content/k8b/CC_*
日志: /content/k8b/colab_compile.log"""
import os, subprocess, sys, time, traceback
K8B = "/content/k8b"
BIN = "/content/lcbin"
LCSRC = "/content/lcbuild/llama.cpp"
LOG = K8B + "/colab_compile.log"
os.makedirs(K8B, exist_ok=True)
log = open(LOG, "a", buffering=1)
def P(m):
log.write("[%s] %s\n" % (time.strftime("%m-%d %H:%M:%S"), m))
def sh(c, t=7200):
p = subprocess.run(c, shell=True, capture_output=True, text=True, timeout=t, errors="replace")
return ((p.stdout or "") + (p.stderr or ""))[-1500:]
def flag(n, c="1"):
open(K8B + "/" + n, "w").write(str(c)[:400])
def have(n):
return os.path.exists(K8B + "/" + n)
def hf_token():
return open("/root/.cache/huggingface/token").read().strip()
def main():
t0 = time.time()
P("==== colab compile start ====")
try:
# CC1: clone
if not have("CC_CLONE"):
if not os.path.exists(LCSRC + "/CMakeLists.txt"):
r = sh("rm -rf /content/lcbuild && mkdir -p /content/lcbuild && "
"git clone --depth 1 https://github.com/ggml-org/llama.cpp %s" % LCSRC, 900)
if not os.path.exists(LCSRC + "/CMakeLists.txt"):
raise RuntimeError("clone failed: " + r[-300:])
flag("CC_CLONE", sh("cd %s && git rev-parse --short HEAD" % LCSRC, 15))
P("clone ok")
# CC2: cmake + build (sm_75, 2 核 -j2)
if not have("CC_BUILD"):
r = sh("cd /content/lcbuild && cmake -S %s -B %s/build -DGGML_CUDA=ON "
"-DCMAKE_CUDA_ARCHITECTURES=75 -DLLAMA_CURL=OFF -DCMAKE_BUILD_TYPE=Release "
"> %s/cmake.log 2>&1; echo RC=$?; tail -3 %s/cmake.log" % (LCSRC, LCSRC, LCSRC, LCSRC), 600)
P("cmake: " + r[-200:])
if "RC=0" not in r:
raise RuntimeError("cmake failed")
r = sh("cd %s/build && cmake --build . --config Release -j2 > %s/build.log 2>&1; echo RC=$?" % (LCSRC, LCSRC), 7200)
P("build: " + r[-200:])
if "RC=0" not in r:
raise RuntimeError("build failed: " + sh("tail -20 %s/build.log" % LCSRC)[-600:])
flag("CC_BUILD")
P("build ok (%.0fs)" % (time.time() - t0))
# CC3: 平铺 bin+so -> /content/lcbin
if not have("CC_FLAT"):
os.makedirs(BIN, exist_ok=True)
sh("cp -a %s/build/bin/* %s/ 2>/dev/null" % (LCSRC, BIN), 300)
sh("find %s/build -name '*.so*' -exec cp -a {} %s/ \\; 2>/dev/null" % (LCSRC, BIN), 300)
need = ["llama-server", "llama-quantize", "llama-cli"]
miss = [b for b in need if not os.path.exists(BIN + "/" + b)]
if miss:
raise RuntimeError("binaries missing: %s" % miss)
r = sh("ls -la %s | head -20" % BIN, 15)
P("flat ok:\n" + r)
flag("CC_FLAT")
# CC4: 打包 + 第一时间推仓
if not have("CC_PUSH"):
tar = K8B + "/llama_cpp_cuda_colab_t4.tar.gz"
sh("tar czf %s -C %s . > /dev/null 2>&1" % (tar, BIN), 600)
if os.path.getsize(tar) < 3e7:
raise RuntimeError("tar too small: %d" % os.path.getsize(tar))
from huggingface_hub import HfApi
api = HfApi(token=hf_token())
api.upload_file(path_or_fileobj=tar, path_in_repo="llama_cpp_cuda_colab_t4.tar.gz",
repo_id="tchbcb/samai-9b", repo_type="model",
commit_message="colab T4 (sm_75) CUDA build, first-push")
flag("CC_PUSH", "%.0fs" % (time.time() - t0))
P("CUDA tar pushed (%.0f MB, %.0fs)" % (os.path.getsize(tar) / 1e6, time.time() - t0))
P("==== COMPILE_ALL_DONE in %.0fs ====" % (time.time() - t0))
flag("COMPILE_ALL_DONE", "%.0fs" % (time.time() - t0))
except Exception as e:
traceback.print_exc(file=log)
flag("COMPILE_FAIL", repr(e)[:200])
P("==== COMPILE_FAIL: %s ====" % repr(e)[:200])
if __name__ == "__main__":
main()
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