first commit

README.txt

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+mamba env create -f hf-train-cu118.yaml --strict-channel-priority
+mamba activate hf-train-cu118
+
+pip install --upgrade pip
+pip install --no-deps -r requirements-hf.txt --constraint constraints-cu118.txt
+
+# 需要 deepspeed 时再装：
+DS_BUILD_OPS=0 pip install "deepspeed==0.14.*"  # 先不编译 CUDA 内核

check_core_cuda.sh

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+python - <<'PY'
+import torch
+print("PyTorch 版本:", torch.__version__)
+print("CUDA runtime 版本:", torch.version.cuda)
+print("GPU 可用:", torch.cuda.is_available())
+if torch.cuda.is_available():
+    print("GPU 数量:", torch.cuda.device_count())
+    for i in range(torch.cuda.device_count()):
+        print(f"  GPU {i}:", torch.cuda.get_device_name(i))
+PY

check_hf.sh

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+python - <<'PY'
+import transformers, accelerate, datasets, safetensors, sentencepiece, peft, bitsandbytes
+print("Transformers:", transformers.__version__)
+print("Accelerate:", accelerate.__version__)
+print("Datasets:", datasets.__version__)
+print("Safetensors:", safetensors.__version__)
+print("SentencePiece:", sentencepiece.__version__)
+print("PEFT:", peft.__version__)
+print("BitsAndBytes:", bitsandbytes.__version__)
+PY
+

check_train.sh

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+python - <<'PY'
+from datasets import load_dataset
+from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments, DataCollatorForLanguageModeling
+import torch
+
+model_id = "sshleifer/tiny-gpt2"  # 极小模型
+tok = AutoTokenizer.from_pretrained(model_id)
+tok.pad_token = tok.eos_token
+
+ds = load_dataset("wikitext", "wikitext-2-raw-v1", split="train[:1%]")
+def tok_fn(ex): return tok(ex["text"], truncation=True, padding="max_length", max_length=64)
+ds = ds.map(tok_fn, batched=True, remove_columns=["text"])
+
+mdl = AutoModelForCausalLM.from_pretrained(model_id)
+collator = DataCollatorForLanguageModeling(tok, mlm=False)
+args = TrainingArguments(
+    output_dir="out-mini",
+    per_device_train_batch_size=2,
+    num_train_epochs=1,
+    fp16=torch.cuda.is_available(),
+    logging_steps=2,
+    save_steps=10,
+    report_to="none",
+)
+
+trainer = Trainer(model=mdl, args=args, train_dataset=ds, data_collator=collator)
+trainer.train()
+print("✅ 训练链路 OK")
+PY
+

constraints-cu118.txt

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+torch==2.1.2
+torchvision==0.16.2
+torchaudio==2.1.2

hf-train-cu118.yaml

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+name: hf-train-cu118
+channels:
+  - pytorch
+  - nvidia
+  - conda-forge
+dependencies:
+  - python=3.10
+  - pip
+  # ---- Torch 栈：固定 2.1.2 + cu118 ----
+  - pytorch=2.1.2
+  - torchvision=0.16.2
+  - torchaudio=2.1.2
+  - pytorch-cuda=11.8
+  # ---- 避坑：Numpy 钉在 1.26.* ----
+  - numpy=1.26.*
+  # ---- 常用科学/系统库 ----
+  - pandas
+  - scipy
+  - pyarrow
+  - uvicorn
+  - git
+  # ---- HF 主栈 + 其运行时依赖（全部走 conda，不让 pip 动依赖）----
+  - transformers>=4.40
+  - accelerate>=0.30
+  - datasets>=2.18
+  - evaluate>=0.4
+  - safetensors>=0.4
+  - sentencepiece>=0.1.99
+  - tokenizers=0.19.*
+  - huggingface_hub>=0.23
+  - tqdm>=4.66
+  - scikit-learn>=1.4
+  - tensorboard>=2.16
+  - packaging
+  - regex
+  - pyyaml
+  - requests
+  - fsspec
+  - dill
+  - multiprocess
+  - xxhash
+  - aiohttp
+  - psutil
+

requirements-hf.txt

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+# requirements-hf.txt  （HF 生态）
+transformers>=4.40,<5.0
+accelerate>=0.30
+datasets>=2.18
+evaluate>=0.4
+safetensors>=0.4
+sentencepiece>=0.1.99
+tokenizers>=0.19,<0.21
+huggingface_hub>=0.23
+tqdm>=4.66
+peft>=0.11
+bitsandbytes>=0.43
+tensorboard>=2.16
+scikit-learn>=1.4
+# deepspeed 单独装，别放进来