Instructions to use tchbcb/samai-8b-M8 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-8b-M8 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-8b-M8:Q4_K_M # Run inference directly in the terminal: llama cli -hf tchbcb/samai-8b-M8: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-8b-M8:Q4_K_M # Run inference directly in the terminal: llama cli -hf tchbcb/samai-8b-M8: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-8b-M8:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf tchbcb/samai-8b-M8: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-8b-M8:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf tchbcb/samai-8b-M8:Q4_K_M
Use Docker
docker model run hf.co/tchbcb/samai-8b-M8:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use tchbcb/samai-8b-M8 with Ollama:
ollama run hf.co/tchbcb/samai-8b-M8:Q4_K_M
- Unsloth Desktop
- Pi
How to use tchbcb/samai-8b-M8 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-8b-M8: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-8b-M8:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use tchbcb/samai-8b-M8 with Docker Model Runner:
docker model run hf.co/tchbcb/samai-8b-M8:Q4_K_M
- Lemonade
How to use tchbcb/samai-8b-M8 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tchbcb/samai-8b-M8:Q4_K_M
Run and chat with the model
lemonade run user.samai-8b-M8-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use tchbcb/samai-8b-M8 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-8b-M8: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-8b-M8:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use tchbcb/samai-8b-M8 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-8b-M8: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-8b-M8: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: 2,530 Bytes
5ac6fe3 | 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 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""patch_imx_adapter.py — 远端 r13_chain/r13_refine 的 imatrix 新格式适配补丁
新 llama.cpp imatrix = <name>.weight.in_sum2 (1-D f32 列激活能量) + .counts
S3/S5 原代码只认 (in,in) 全矩阵 → 全部落 identity, imatrix 加权失效。
补丁: 查找链加 .in_sum2 候选 + 1-D→torch.diag 对角矩阵 (数学上等价 GPTQ 对角 Hessian)。
锚点断言 count==1 + compile 语法验证, 双保险。
"""
A_OLD = ''' hk = base if base in imx else (base + ".weight" if base + ".weight" in imx else None)
if hk is not None:
hn = np.ascontiguousarray(imx[hk].data).astype(np.float32)
H = None
if hn.shape == (shape[1], shape[1]):
H = torch.from_numpy(hn).to(dev)'''
A_NEW = ''' hk = None
for cd in (base, base + ".weight", base + ".weight.in_sum2", base + ".in_sum2"):
if cd in imx:
hk = cd
break
if hk is not None:
hn = np.ascontiguousarray(imx[hk].data).astype(np.float32)
H = None
if hn.ndim == 1 and hn.shape[0] == shape[1]:
H = torch.diag(torch.from_numpy(hn).to(dev) + 1e-8)
elif hn.shape == (shape[1], shape[1]):
H = torch.from_numpy(hn).to(dev)'''
B_OLD = ''' hn = np.ascontiguousarray(imx_t.data).astype(np.float32)
if hn.shape != (in_dim, in_dim):
return None'''
B_NEW = ''' hn = np.ascontiguousarray(imx_t.data).astype(np.float32)
if hn.ndim == 1 and hn.shape[0] == in_dim:
hn = np.diag(hn + 1e-8)
if hn.shape != (in_dim, in_dim):
return None'''
C_OLD = ''' hk = base if base in imx else (base + ".weight" if base + ".weight" in imx else None)'''
C_NEW = ''' hk = None
for cd in (base, base + ".weight", base + ".weight.in_sum2", base + ".in_sum2"):
if cd in imx:
hk = cd
break'''
def main():
for path, olds, news in (
("/tmp/k8b/r13_chain.py", [A_OLD], [A_NEW]),
("/tmp/k8b/r13_refine.py", [B_OLD, C_OLD], [B_NEW, C_NEW]),
):
src = open(path).read()
for o, n in zip(olds, news):
cnt = src.count(o)
assert cnt == 1, "anchor not unique in %s: count=%d" % (path, cnt)
src = src.replace(o, n, 1)
compile(src, path, "exec")
open(path, "w").write(src)
print("PATCHED", path)
print("PATCH_OK imx in_sum2 diag adapter")
main()
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