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"
train.md v4.6: pitfalls #67-#70 + R13/R14 records + serving recipe
Browse files
train.md
CHANGED
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@@ -464,6 +464,10 @@ open(K8B+("/BURST_R7_DONE" if ok else "/BURST_R7_FAIL"),"w").write("r7")
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| 64 | bash 长命令 sleep>~600s 被杀且同调用 heredoc 回滚 | 长操作 nohup 分片 + Write 工具中转落盘, 绝不单条 bash 长阻塞 |
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| 65 | 部署脚本旗标双前缀 bug(r12_pipeline.py S3 的 DEPLOY_ 重复)→ 秒退/误判 | 部署只用修正版 r12_deploy_r10.py; 重入前 rm -f /tmp/k8b/DEPLOY_*(#49 复核) |
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| 66 | R11 长窗训练 OOM(W_TOK 放开后) | 梯子 6144→3400 降窗口; 长窗按长度分桶/降 batch; OOM 不退课程只降窗口 |
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## 十、历轮战绩
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| R10 | 混合精度梯子 | **量化无罪终审**(Q4/f16 双对照+长短双维探针); 4x8 冠军档上线; think-行为解离实锤 | — |
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| R11 | zagent PR×2+视频工具 | PR#188/#189 合并; 视频工具✅逐字读帧; computer-use❌拦截免疫→转训练靶 | R11: 210 长窗 (m13_r8→r9) |
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| R12 | 五靶点 T1-T5 | 行为判据/诚实窗/纯文本收束/录屏 recipe/框架补丁(Go 57.3MB 单测 4 项过)全落地 | R12: 58 窗, **总计 268 窗 (m13_r9→r10)** |
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## 十一、
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R11/R12 长窗纪律轮已收官,m13_r10 落地:
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- **R11 训练**:210 长窗(r11_curriculum 课程:W1 拦截后换路/W2 拦截后收束/W3 双振换路 三窗型 × 2K-40K 深度 + 视频窗),W_TOK 放开,OOM 梯子 6144→3400(坑#66);血统 m13_r8→m13_r9
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@@ -519,3 +525,55 @@ R11/R12 长窗纪律轮已收官,m13_r10 落地:
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- 部署完成后:换装 m13_r7 → lproj1 基线(65min, 目标 <10min)+ GAIA 同型题复测(观察死环根治效果: 环发生率/早断率/配额服从率)+ #13 补发验证
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| 520 |
- v4.2 后续实测(死环根治轮收官):m13_r7 换装上线(262144 池/12s 加载)→ #13 实测暴露 R7 微调未根治(g3c2 复合周期-2 环复发)→ **zagent 框架级反环补丁上线**(坑#47/#48 两坑三修,web_stream.go+agent.go 四处)→ g3c3 实战 6 连拦 → g3c4 短上下文收官(0.03488,fail 但闭环)→ M15 判分终局 0/3(如实记录)
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- 待办移交:lproj1 基线未跑;#13 方向性偏差根因(分母应取 Wikipedia 全种群非 CSV 344)待复盘;反环补丁参数(阈值 3/重置规则)可调优
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| 64 | bash 长命令 sleep>~600s 被杀且同调用 heredoc 回滚 | 长操作 nohup 分片 + Write 工具中转落盘, 绝不单条 bash 长阻塞 |
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| 465 |
| 65 | 部署脚本旗标双前缀 bug(r12_pipeline.py S3 的 DEPLOY_ 重复)→ 秒退/误判 | 部署只用修正版 r12_deploy_r10.py; 重入前 rm -f /tmp/k8b/DEPLOY_*(#49 复核) |
|
| 466 |
| 66 | R11 长窗训练 OOM(W_TOK 放开后) | 梯子 6144→3400 降窗口; 长窗按长度分桶/降 batch; OOM 不退课程只降窗口 |
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+
| 67 | aitun 免 token 隧道同机单身份: 第二个客户端实例顶掉第一个(Connection replaced by newer connection of this client) | 真双隧道需 -k 账户 token; 单隧道双功能走 kaggle_server /v1 代理补丁(备选 router_8102.py 独立复用器) |
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| 68 | kaggle_server 与 aitun 客户端都有监督环(jupyter kernel 自动重生)→ 抢端口/改 -p 必被弹回 | 补丁本体文件 + kill 让监督环带补丁重生; 兜底自检自启(oneclick restart_ksguarded) |
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| 69 | 信封内无界 grep/find 扫 /tmp(40G 模型盘)→ 30s 超时误判 | 远端搜索必须 maxdepth + -size -2M + 排除模型目录 |
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| 70 | 双 /health 同名: kaggle_server /health(富信息)vs llama /health({"status":"ok"}) | 经代理后验 llama 存活用 /v1/models; /health 富信息=执行桥活的标志 |
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## 十、历轮战绩
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| R10 | 混合精度梯子 | **量化无罪终审**(Q4/f16 双对照+长短双维探针); 4x8 冠军档上线; think-行为解离实锤 | — |
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| 487 |
| R11 | zagent PR×2+视频工具 | PR#188/#189 合并; 视频工具✅逐字读帧; computer-use❌拦截免疫→转训练靶 | R11: 210 长窗 (m13_r8→r9) |
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| 488 |
| R12 | 五靶点 T1-T5 | 行为判据/诚实窗/纯文本收束/录屏 recipe/框架补丁(Go 57.3MB 单测 4 项过)全落地 | R12: 58 窗, **总计 268 窗 (m13_r9→r10)** |
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| R13 | GSQ-RCO-lite 四段链(链重建→imatrix→DP 背包逐张量→容器量化; 参 GSQ 2604.18556 + RCO 2605.00649) | mixbit 8.11 → **gsqrco_v1 7.99 ppl**; container_q40 7.84; imatrix q8 参考 5.07; serving 35.9 tok/s | — |
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| R14 | 代码仓同步 + 隧道六死 + 单隧道复用 | FNH0ITO4 判死(坑#50 六现); 8RF5ST7K 公网 /v1 全绿(models/chat/SSE 流式, key=1234); lora 按 git 史新者为准覆盖 | — |
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## 十一、R12 收官状态存档(2026-09-24 · Colab T4 部署轮)
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R11/R12 长窗纪律轮已收官,m13_r10 落地:
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- **R11 训练**:210 长窗(r11_curriculum 课程:W1 拦截后换路/W2 拦截后收束/W3 双振换路 三窗型 × 2K-40K 深度 + 视频窗),W_TOK 放开,OOM 梯子 6144→3400(坑#66);血统 m13_r8→m13_r9
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| 525 |
- 部署完成后:换装 m13_r7 → lproj1 基线(65min, 目标 <10min)+ GAIA 同型题复测(观察死环根治效果: 环发生率/早断率/配额服从率)+ #13 补发验证
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| 526 |
- v4.2 后续实测(死环根治轮收官):m13_r7 换装上线(262144 池/12s 加载)→ #13 实测暴露 R7 微调未根治(g3c2 复合周期-2 环复发)→ **zagent 框架级反环补丁上线**(坑#47/#48 两坑三修,web_stream.go+agent.go 四处)→ g3c3 实战 6 连拦 → g3c4 短上下文收官(0.03488,fail 但闭环)→ M15 判分终局 0/3(如实记录)
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| 527 |
- 待办移交:lproj1 基线未跑;#13 方向性偏差根因(分母应取 Wikipedia 全种群非 CSV 344)待复盘;反环补丁参数(阈值 3/重置规则)可调优
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## 十二、R13/R14 战报与当前状态(2026-09-25 · 8RF5ST7K 公网 serving 全绿)
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### 十二甲、R13 量化战报(GSQ-RCO-lite)
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用户否决 mixbit_4x8("量化技术不好")后, 参考 GSQ(Gumbel-Softmax Quantization, arXiv 2604.18556)
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与 RCO(arXiv 2605.00649, ISTA-DASLab)自适配为 **GSQ-RCO-lite 四段链**:
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链重建 → imatrix 校准(q8 路线, ppl 5.0709)→ DP 背包逐张量类型分配 → --imatrix+类型表容器量化。
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成品(tchbcb/samai-9b, 全量/抽块 sha256 与容器盘内 serving 副本比对一致):
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| 文件 | 体积 | ppl | sha256 前 16 |
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|---|---|---|---|
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| m13_r10_gsqrco_v1.gguf(精炼版, **serving 现役**) | 6.482G | **7.99**(旧 mixbit 8.11) | 23a73197243cb9bd |
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| m13_r10_gsqrco_container_q40.gguf(A/B 副本) | 6.482G | 7.84 | 28ad41308f373cc3 |
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| mmproj_m11.gguf(视觉塔, 血统链视觉冻结故持续兼容) | 921.7M | — | d89c4bc142d02ed6 |
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| 545 |
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| m13_r10_lora.gguf(R13 重烘焙; 按 git 史 9/24 新者为准覆盖 9/23 旧版) | 232.8M | — | f31236e81f547c95 |
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两成品为不同文件(全量 sha256 相异, 稀疏差异=精炼只覆写部分张量), A/B 对比有效;
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ppl 差异(7.99 vs 7.84)存在度量对齐问题, 以用户实测为准。
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### 十二乙、R14 serving 与公网���用(现役配方)
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- **serving**: llama-server(/tmp/lcfin)`-m m13_r10_gsqrco_v1.gguf --mmproj mmproj_m11.gguf
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--host 0.0.0.0 --port 8101 --api-key 1234 -c 131072 -fa on -ctk q8_0 -ctv q8_0 -t 2 -ngl 99`
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(双 T4; 单卡 ctx 梯子 65536→32768); 冒烟 35.9 tok/s, 租金额域题 48,000 元正确
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- **公网调用(单隧道双功能, key=1234)**: `https://aitun.cc/8RF5ST7K/v1/chat/completions`
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(OpenAI 兼容, /v1/models、chat、SSE 流式全绿)
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- 原理: kaggle_server.py 注入 `/v1/*` → 127.0.0.1:8101 代理补丁
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(补丁源: tchbcb/samai-8b-M8:artifacts/r14_scripts/r14_v1_proxy.py; 坑#67/#68)
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- 隧道本体不动: aitun-client -s aitun.cc:6639 -p 5000 → kaggle_server:5000
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(/execute 信封协议 v=1 不变, §四)
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- 备选形态: 无 kaggle_server 的容器 → `aitun -p 8101` 直连隧道(oneclick 自动选择)
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- **一键运行**: `python3 oneclick_gsqrco_serve.py --token hf_xxx`(本仓根目录)
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→ 拉 6.48G+921M+tar → llama-server → 隧道适配 → 打印 curl/openai 调用示例;
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日志 /tmp/k8b/oneclick.log, 旗标 ONECLICK_DONE/ONECLICK_FAIL
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- **A/B 切换**: 盘内 /tmp/k8b/ 两成品均在(r13_container_q40.gguf 为容器版盘内名),
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pkill llama-server 后换 -m 路径重启即可(ctx 梯子照旧)
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- 推迟项: S4 探针(s8_sprobe_r12.py A-E)+ S5(GAIA#13 复测 + testB 重放)按用户令顺延
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### 十二丙、隧道运维要点(aitun.cc)
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- 免 token 隧道: `aitun-client -s aitun.cc:6639 -p <本地端口>` → 自动分配码
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(同机重连保码; 跨机/新实例换码); **真双隧道需 -k 账户 token**(坑#67)
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- 监督环: jupyter kernel 自动重生 aitun 客户端与 kaggle_server(勿手工抢端口, 坑#68)
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- 测试信封(隧道通用): crc=9e30c354, payload 行 `cHJpbnQoNDArMik`(print(40+2)),
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回显 42=全链通
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- 隧道死亡史: GVMHKJCU→044WF3NQ→QCF8P0OM→FNH0ITO4(判死, 坑#50 六现)→
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**8RF5ST7K 现役**(同容器复活, 资产零损)
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- 远端弹药库: tchbcb/samai-8b-M8:artifacts/r14_scripts/(r14_boot / p_r14_fire /
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p_r14_poll / router_8102 / r14_v1_proxy / oneclick 同款)
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