72 lines
1.7 KiB
Markdown
72 lines
1.7 KiB
Markdown
# Workflow Template
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本流程用于新模型接入，默认在仓库根目录执行。
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## Step 1: HF -> BF16 GGUF
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使用 `ik_llama.cpp` 的转换脚本：
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```bash
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python convert_hf_to_gguf.py \
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<hf_model_dir> \
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--outtype bf16 \
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--outfile artifacts/<model_name>/base_gguf/<model_name>-bf16.gguf
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```
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## Step 2: 准备校准数据
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```bash
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./.venv/bin/python scripts/prepare_calib_data.py --force-refresh
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```
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输出：
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- `calibration/calibration_data_v5_rc.txt`
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- `calibration/calibration_data_v5_rc_code.txt`
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固定组成：1152 + 2000 + 1000 = 4152 blocks。
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## Step 3: 生成 imatrix
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```bash
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docker run --gpus all --rm \
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--entrypoint sh \
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-v <repo_root>:/workspace/models \
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-v <repo_root>/calibration/calibration_data_v5_rc_code.txt:/workspace/calib_data.txt \
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hotwa/ik:latest \
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-c "/llama-imatrix -m <bf16_gguf> -f /workspace/calib_data.txt -o <imatrix_out> --ctx-size 512 -ngl 99 --threads 16"
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```
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## Step 4: 量化导出
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分别执行：
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```bash
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docker run --gpus all --rm \
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--entrypoint sh \
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-v <repo_root>:/workspace/models \
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hotwa/ik:latest \
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-c "/llama-quantize --imatrix <imatrix_out> <bf16_gguf> <out_gguf> IQ4_KS"
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```
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将量化结果放入：`artifacts/<model_name>/quantized_gguf/`。
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## Step 5: 组织上传目录
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```bash
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cp templates/modelscope/README.template.md modelscope_upload/README.md
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cp templates/modelscope/configuration.template.json modelscope_upload/configuration.json
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cp templates/modelscope/.gitattributes modelscope_upload/.gitattributes
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```
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然后把目标发布文件复制到 `modelscope_upload/`。
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## Step 6: 上传
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```bash
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./scripts/upload_to_modelscope.sh <repo_id> <token> modelscope_upload direct "Upload quantized GGUF"
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```
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- `direct`：关闭代理上传
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- `proxy`：保留代理上传
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