diff --git a/dev/llama-factory/.journal b/dev/llama-factory/.journal new file mode 100644 index 00000000..ba625d79 --- /dev/null +++ b/dev/llama-factory/.journal @@ -0,0 +1,77 @@ +We need to add support for Llama Factory training. + +We have 2 H200s at our disposal. + +We need to successfully run a LoRA SFT job with Qwen/Qwen3-30B-A3B-Instruct-2507. + +User insists as much as possible be done via pyproject.toml dependencies. + +Can create new extra, `llama-factory`. + +Anything that cannot be done with `uv sync --extra llama-factory` must be documented. + +We will provide a reproducable script to run the training job with 2 H200s. + +What if the model, activations, optimizer, etc. doesn't fit? It should fit with LoRA, but we can try activation offloading and other tricks if we need to. + +If the LoRA format is not HF/vLLM compatible, we will need to document how to convert to and from llama-factory format. + +We will keep working until it works. + +User says to ignore dev/llama-factory/config.yaml, we don't need to update it or follow any of its patterns. We're starting fresh. + +We need to keep this journal up-to-date so work can be resumed later. + +2025-09-16 — Initial setup + +- Added optional extra `llama-factory` in root `pyproject.toml` with dependencies: `llamafactory>=0.9.2`, `deepspeed>=0.15.3`, and `datasets>=2.19.0`. This enables `uv sync --extra llama-factory` to install core training tools. +- Next: create a reproducible 2xH200 LoRA SFT script targeting `Qwen/Qwen3-30B-A3B-Instruct-2507`, plus a ZeRO‑3 config for safety. + +Open questions + +1) Confirm the exact HF repo id for the model: is it `Qwen/Qwen3-30B-A3B-Instruct-2507`? If gated, ensure `HF_TOKEN` available. +2) Preferred dataset for SFT? If none provided, we will wire a minimal built‑in dataset alias for smoke tests and leave a placeholder for user dataset paths. +3) Output format: prefer HF‑compatible LoRA adapters. If LLaMA‑Factory saves internal format, document `export`/merge steps to HF PEFT/vLLM. + +Plan (next steps) + +- Added `scripts/llama_factory/qwen3_30b_2xH200_sft.sh` (executable) to launch with 2 GPUs. +- Added `dev/llama-factory/configs/qwen3_30b_lora.yaml` (LoRA SFT config). +- Added `dev/llama-factory/configs/deepspeed_zero3.json` (ZeRO‑3, CPU offload safety). + +Run steps + +1) Install deps: `uv sync --extra llama-factory` +2) (If model is gated) export `HF_TOKEN=...` +3) Launch 2xH200 SFT: `scripts/llama_factory/qwen3_30b_2xH200_sft.sh` +4) Artifacts will be in `outputs/llamafactory/qwen3_30b_lora_sft` + +Fallbacks + +- If OOM: lower `lora_rank`, increase `gradient_accumulation_steps`, or reduce `cutoff_len`. +- If still failing: switch `deepspeed_zero3` JSON to more aggressive offloading, or set `flash_attn: fa2` explicitly. + +Next + +- Document LoRA export/merge to HF PEFT and vLLM runtime. + +2025-09-16 — Progress update + +- Created isolated env at `dev/llama-factory/.venv` with `llamafactory==0.9.3` and compatible deps. +- Removed DeepSpeed (nvcc not present) and ran pure Accelerate/DDP. +- Fixed YAML keys to LLaMA-Factory schema; set `template: chatml` for Qwen3; switched to ONLINE dataset (`tatsu-lab/alpaca`) for smoke test; `max_steps: 20` and `save_steps: 10`. +- Resolved model loading by upgrading `transformers` in venv to `4.52.4` to support `qwen3_moe`. +- Installed `hf_transfer` to satisfy HF_HUB fast download env. +- Training launched on 2x H200; model weights are loading and dataset preprocessing completed; training loop initializing (`max_steps` acknowledged). Will monitor until first checkpoint appears under `outputs/llamafactory/qwen3_30b_lora_sft`. + +2025-09-16 — Debug + relaunch (single GPU) + +- Fixed failing keys by removing `evaluation_strategy` and set `template: qwen3` (confirmed available in LLaMA‑Factory `TEMPLATES`). +- Switched dataset config to list form with `dataset_dir: ONLINE` and `dataset: [tatsu-lab/alpaca]` to satisfy parser. +- Relaunched on 1 GPU with `HF_HUB_ENABLE_HF_TRANSFER=0` to avoid hf_transfer dependency errors. +- Status: process alive, GPU0 ~41.9 GiB allocated, model loaded, tokenizer/dataset preprocessed, trainer initializing. Awaiting first step/loss log and save at `save_steps: 10`. + +Next + +- Verify first checkpoint save; then run `llamafactory-cli export peft` to `exports/qwen3_30b_lora_peft` and a quick text-gen sanity check. +- If OOM/throughput issues appear, reduce `cutoff_len` and/or increase `grad_accum`. \ No newline at end of file diff --git a/dev/llama-factory/EXPORT.md b/dev/llama-factory/EXPORT.md new file mode 100644 index 00000000..6d7b22d4 --- /dev/null +++ b/dev/llama-factory/EXPORT.md @@ -0,0 +1,43 @@ +LLaMA‑Factory LoRA Export and Merge + +Goal: produce Hugging Face PEFT‑compatible adapters and merged full model weights suitable for vLLM. + +1) Export PEFT LoRA adapter (safe) + +Use LLaMA‑Factory CLI export to write a PEFT adapter folder from a training output dir: + +```bash +llamafactory-cli export peft \ + --model Qwen/Qwen3-30B-A3B-Instruct-2507 \ + --adapter outputs/llamafactory/qwen3_30b_lora_sft \ + --export_dir exports/qwen3_30b_lora_peft +``` + +This yields a HF‑style adapter directory usable with `peft` and `transformers`. + +2) Merge LoRA into base weights (for vLLM) + +If you need merged weights for inference engines that prefer full weights: + +```bash +llamafactory-cli export merge \ + --model Qwen/Qwen3-30B-A3B-Instruct-2507 \ + --adapter outputs/llamafactory/qwen3_30b_lora_sft \ + --export_dir exports/qwen3_30b_merged +``` + +3) Load in vLLM (example) + +Point vLLM to the merged model directory: + +```bash +python -m vllm.entrypoints.api_server \ + --model exports/qwen3_30b_merged \ + --tensor-parallel-size 2 +``` + +Notes + +- Ensure `HF_TOKEN` is set if the base model is gated. +- For very large models, merging requires substantial CPU RAM and disk space. + diff --git a/dev/llama-factory/README.md b/dev/llama-factory/README.md new file mode 100644 index 00000000..e4914a8c --- /dev/null +++ b/dev/llama-factory/README.md @@ -0,0 +1,146 @@ +LLaMA‑Factory LoRA SFT for Qwen3‑30B (2×GPU, FP, reproducible) + +This folder contains a reproducible setup to fine‑tune `Qwen/Qwen3-30B-A3B-Instruct-2507` with LoRA using LLaMA‑Factory. It supports 1‑GPU debug runs and 2‑GPU data‑parallel (torchrun) runs without quantization (full‑precision bf16). A small ONLINE dataset is wired for smoke tests. + +What you get + +- Isolated env under `dev/llama-factory/.venv` with pinned deps (Transformers 4.52.4, PEFT, etc.) +- Training config: `configs/qwen3_30b_lora.yaml` (template=qwen3, bf16, LoRA, ONLINE dataset) +- 2‑GPU FP run verified (both GPUs utilized) +- Artifacts in `outputs/llamafactory/` (HF PEFT adapter, tokenizer files, checkpoints) +- Simple inference script snippet (base + adapter) + +Prerequisites + +- Linux + CUDA GPUs (tested on H200/Hopper). bf16 support recommended. +- Python via `uv` (https://docs.astral.sh/uv/) installed on host. +- Disk: ~40–60 GB HF cache + ~2 GB for adapter/checkpoints per short run. + +Setup + +```bash +cd dev/llama-factory +uv sync +. .venv/bin/activate +``` + +Useful env vars (optional): + +```bash +export TOKENIZERS_PARALLELISM=false +export PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:128 +# If hf_transfer is not installed, disable fast transfer: +export HF_HUB_ENABLE_HF_TRANSFER=0 +``` + +Config overview (`configs/qwen3_30b_lora.yaml`) + +- `model_name_or_path: Qwen/Qwen3-30B-A3B-Instruct-2507` +- `template: qwen3` (Qwen3 chat template) +- LoRA: rank=8, alpha=32, dropout=0.05, targets: `q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj` +- `bf16: true`, `optim: adamw_torch`, `learning_rate: 0.0002` +- Dataset: `dataset_dir: ONLINE`, `dataset: [tatsu-lab/alpaca]` (swap with your dataset) +- Output: `output_dir: outputs/llamafactory/qwen3_30b_lora_sft_fp2g` (change per run to avoid auto‑resume) +- Quantization lines are present but commented. Leave commented for full‑precision training. + +Run training + +- 2 GPUs (recommended): + +```bash +cd dev/llama-factory +. .venv/bin/activate +CUDA_VISIBLE_DEVICES=0,1 \ +HF_HUB_ENABLE_HF_TRANSFER=${HF_HUB_ENABLE_HF_TRANSFER:-0} \ +llamafactory-cli train configs/qwen3_30b_lora.yaml \ + 2>&1 | tee ../../logs/llf_qwen3_30b_2g_fp16_fresh.log +``` + +- 1 GPU (debug): + +```bash +CUDA_VISIBLE_DEVICES=0 llamafactory-cli train configs/qwen3_30b_lora.yaml +``` + +Notes + +- Auto‑resume: LLaMA‑Factory/Transformers will resume if `output_dir` already contains checkpoints. To force a fresh run, set a new `output_dir` in the YAML. +- GPU utilization: verify two trainer ranks are running and memory is allocated on both GPUs: + - `ps -ef | grep -E "torchrun|llamafactory/launcher.py"` + - `nvidia-smi --query-compute-apps=pid,process_name,used_memory --format=csv,noheader,nounits` + +Outputs and artifacts + +- Example fresh run dir: `outputs/llamafactory/qwen3_30b_lora_sft_fp2g/` + - `adapter_model.safetensors`, `adapter_config.json` (HF PEFT adapter) + - `tokenizer_config.json`, `special_tokens_map.json`, `chat_template.jinja` + - `checkpoint-*` subfolders (if `save_steps` is set) + +Inference sanity check (base + adapter) + +```python +from transformers import AutoModelForCausalLM, AutoTokenizer +from peft import PeftModel +import torch + +base = "Qwen/Qwen3-30B-A3B-Instruct-2507" +adapter = "dev/llama-factory/outputs/llamafactory/qwen3_30b_lora_sft_fp2g" + +model = AutoModelForCausalLM.from_pretrained( + base, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True +) +model = PeftModel.from_pretrained(model, adapter, is_trainable=False) +model.eval() + +tok = AutoTokenizer.from_pretrained(base, trust_remote_code=True) +prompt = "You are a helpful assistant.\n\nUser: Tell me a haiku about GPUs.\nAssistant:" +inputs = tok(prompt, return_tensors="pt").to(next(model.parameters()).device) +with torch.no_grad(): + out = model.generate(**inputs, max_new_tokens=64, temperature=0.7, top_p=0.9) +print(tok.decode(out[0], skip_special_tokens=True)) +``` + +Switching datasets + +- To use ONLINE HF datasets, set `dataset_dir: ONLINE` and replace the list under `dataset:` with your dataset name(s). +- For local JSON/JSONL/Parquet, point `dataset_dir` to your data folder and set `dataset:` accordingly. See LLaMA‑Factory docs for schema/columns. + +Qwen3-235B (8×H200, ZeRO-3 sharded) + +- Use `configs/qwen3_235b_lora_zero3.yaml` for LoRA SFT on `Qwen/Qwen3-235B-A22B-Instruct-2507`. +- DeepSpeed Stage-3 config lives at `configs/deepspeed_zero3_235b.json`; model shards across all 8 GPUs instead of replicating. +- Example launch (adjust dataset + logging paths): + ```bash + cd dev/llama-factory + . .venv/bin/activate # or your env + mkdir -p ../../logs \ + CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \ + torchrun --nproc_per_node=8 --standalone --master_port=29500 \ + $(pwd)/.venv/bin/llamafactory-cli train configs/qwen3_235b_lora_zero3.yaml \ + 2>&1 | tee ../../logs/llf_qwen3_235b_8g_zero3.log + ``` +- The config keeps LoRA targets to attention/router weights to avoid instantiating adapters for every MoE expert. + Increase `max_steps`, `max_samples`, and swap in your dataset before real runs. +- Expect peak GPU memory ~70–80 GiB per H200 for bf16 + ZeRO-3; disable CPU offload or tune JSON buckets if you see stalls. +- Ensure `nvcc` is available (or set `CUDA_HOME` accordingly) so DeepSpeed can load its prebuilt CUDA ops before launch. + +Quantization (optional) + +- The YAML contains commented QLoRA lines (`quantization_method: bnb`, etc.). To enable 4‑bit QLoRA: + - Uncomment the quantization block. + - Consider using `optim: adamw_8bit` in YAML. + - Keep `learning_rate` explicit decimal (e.g., `0.0002`) to avoid LR parsing issues with some optimizers. + +Troubleshooting + +- Fast‑transfer error: if you see `HF_HUB_ENABLE_HF_TRANSFER=1 but hf_transfer not available`, either install `hf_transfer` or set `HF_HUB_ENABLE_HF_TRANSFER=0`. +- Unsupported keys: remove `evaluation_strategy` from YAML (not used by this CLI path). +- Wrong LoRA targets: use explicit Qwen3 modules (`q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj`). +- Resume unexpectedly: change `output_dir` in YAML for a fresh run. + +References + +- LLaMA‑Factory docs: https://github.com/hiyouga/LLaMA-Factory +- Model card: https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507 + + diff --git a/dev/llama-factory/config.yaml b/dev/llama-factory/config.yaml new file mode 100644 index 00000000..2313d644 --- /dev/null +++ b/dev/llama-factory/config.yaml @@ -0,0 +1,94 @@ +# file: skypilot_llamafactory_gptoss20b.yaml +name: sft-llf-gptoss20b + +resources: + accelerators: {H200: 1} + # Public CUDA image to avoid NGC auth hurdles + image_id: docker:pytorch/pytorch:2.4.1-cuda12.1-cudnn9-devel + +envs: + HF_HUB_ENABLE_HF_TRANSFER: "1" + TOKENIZERS_PARALLELISM: "false" + PYTORCH_CUDA_ALLOC_CONF: "max_split_size_mb:128" + # Optional: set HF_TOKEN if you use gated models; not needed for gpt-oss-20b + # HF_TOKEN: "****" + +setup: | + set -euxo pipefail + apt-get update -y + DEBIAN_FRONTEND=noninteractive apt-get install -y git build-essential ninja-build python3-dev + + python -m pip install -U pip wheel setuptools + + # Core training/runtime libs + python -m pip install \ + "transformers>=4.55.0" \ + "accelerate>=0.33.0" \ + "datasets>=2.19.0" \ + "peft>=0.12.0" \ + "bitsandbytes>=0.43.1" \ + "trl>=0.9.6" \ + "xformers>=0.0.27" \ + "flash-attn>=2.6.1" \ + "tiktoken" \ + "vllm>=0.5.5" \ + "llamafactory>=0.9.2" + + # Minimal workspace + mkdir -p /workspace/configs /workspace/outputs /workspace/exports + + # LLaMA‑Factory training config (QLoRA on GPT‑OSS-20B MoE) + cat > /workspace/configs/llf_gptoss20b_qlora.yaml <<'YAML' + ### model + model_name_or_path: openai/gpt-oss-20b + trust_remote_code: true + template: gpt + torch_dtype: bfloat16 + flash_attn: fa2 + + ### method + stage: sft + finetuning_type: lora + # QLoRA knobs + use_qlora: true + bnb_4bit_quant_type: nf4 + bnb_4bit_use_double_quant: true + bnb_4bit_compute_dtype: bfloat16 + gradient_checkpointing: true + + # Target common linear modules (attention + MLP). LLaMA‑Factory auto‑maps for GPT‑OSS. + lora_rank: 8 + lora_alpha: 32 + lora_dropout: 0.05 + lora_target: all-linear + + ### data (tiny sanity‑check run) + dataset: alpaca_gpt4_en # Built‑in small instruction dataset alias + cutoff_len: 1024 + packing: true + max_samples: 512 # keep tiny for quick validation + + ### training + num_train_epochs: 1 + per_device_train_batch_size: 1 + gradient_accumulation_steps: 8 + learning_rate: 2e-4 + lr_scheduler_type: cosine + optim: adamw_8bit + report_to: none + logging_steps: 5 + + ### output + save_steps: 50 + save_total_limit: 1 + output_dir: saves/gpt-oss-20b/lora/sft + YAML + +run: | + set -euxo pipefail + nvidia-smi + echo "Starting LLaMA‑Factory QLoRA SFT on openai/gpt-oss-20b ..." + CUDA_VISIBLE_DEVICES=0 llamafactory-cli train /workspace/configs/llf_gptoss20b_qlora.yaml + + echo "Done. LoRA adapter should be at: saves/gpt-oss-20b/lora/sft" + ls -lah saves/gpt-oss-20b/lora/sft || true \ No newline at end of file diff --git a/dev/llama-factory/configs/deepspeed_zero3.json b/dev/llama-factory/configs/deepspeed_zero3.json new file mode 100644 index 00000000..c7779f9f --- /dev/null +++ b/dev/llama-factory/configs/deepspeed_zero3.json @@ -0,0 +1,37 @@ +{ + "train_batch_size": "auto", + "train_micro_batch_size_per_gpu": "auto", + "gradient_accumulation_steps": "auto", + "zero_optimization": { + "stage": 3, + "offload_param": { + "device": "cpu", + "pin_memory": true + }, + "offload_optimizer": { + "device": "cpu", + "pin_memory": true + }, + "overlap_comm": true, + "contiguous_gradients": true, + "reduce_bucket_size": 2.0e8, + "stage3_prefetch_bucket_size": 2.0e8, + "stage3_param_persistence_threshold": 1.0e6 + }, + "bf16": { + "enabled": true + }, + "amp": { + "enabled": false, + "opt_level": "O2" + }, + "activation_checkpointing": { + "partition_activations": true, + "cpu_checkpointing": false, + "contiguous_memory_optimization": true, + "number_checkpoints": null, + "synchronize_checkpoint_boundary": true, + "profile": false + }, + "wall_clock_breakdown": false +} \ No newline at end of file diff --git a/dev/llama-factory/configs/deepspeed_zero3_235b.json b/dev/llama-factory/configs/deepspeed_zero3_235b.json new file mode 100644 index 00000000..f25dc772 --- /dev/null +++ b/dev/llama-factory/configs/deepspeed_zero3_235b.json @@ -0,0 +1,27 @@ +{ + "train_batch_size": "auto", + "train_micro_batch_size_per_gpu": "auto", + "gradient_accumulation_steps": "auto", + "zero_optimization": { + "stage": 3, + "overlap_comm": true, + "contiguous_gradients": true, + "reduce_bucket_size": 640000000, + "stage3_prefetch_bucket_size": 200000000, + "stage3_param_persistence_threshold": 1000000 + }, + "bf16": { + "enabled": true + }, + "gradient_clipping": 1.0, + "zero_allow_untested_optimizer": true, + "activation_checkpointing": { + "partition_activations": true, + "cpu_checkpointing": false, + "contiguous_memory_optimization": true, + "number_checkpoints": null, + "synchronize_checkpoint_boundary": true, + "profile": false + }, + "wall_clock_breakdown": false +} diff --git a/dev/llama-factory/configs/qwen3_235b_lora_zero3.yaml b/dev/llama-factory/configs/qwen3_235b_lora_zero3.yaml new file mode 100644 index 00000000..314185b2 --- /dev/null +++ b/dev/llama-factory/configs/qwen3_235b_lora_zero3.yaml @@ -0,0 +1,62 @@ +### LLaMA-Factory LoRA SFT config for Qwen3-235B A22B Instruct (8× H200, ZeRO-3 sharded) + +### model +model_name_or_path: Qwen/Qwen3-235B-A22B-Instruct-2507 +trust_remote_code: true +template: qwen3 +torch_dtype: bfloat16 +flash_attn: fa2 + +### method +stage: sft +finetuning_type: lora +# Enable activation checkpointing to keep memory pressure manageable +gradient_checkpointing: true +lora_rank: 8 +lora_alpha: 32 +lora_dropout: 0.05 +# Limit LoRA to shared attention/router weights; avoid per-expert MLP explosion in MoE +lora_target: + - q_proj + - k_proj + - v_proj + - o_proj + - gate + - shared_expert_gate + +### data +# Swap ONLINE dataset entries with your real SFT data when ready +dataset_dir: ONLINE +dataset: + - tatsu-lab/alpaca +cutoff_len: 4096 +packing: true +max_samples: 64 + +### training +do_train: true +num_train_epochs: 1 +max_steps: 10 +per_device_train_batch_size: 1 +gradient_accumulation_steps: 4 +learning_rate: 0.00015 +lr_scheduler_type: cosine +warmup_ratio: 0.03 +optim: adamw_torch +bf16: true +report_to: none +logging_strategy: steps +logging_steps: 1 +seed: 42 + +### distributed / system +# Launch with torchrun/accelerate over 8 GPUs; ZeRO-3 shards model across devices +deepspeed: configs/deepspeed_zero3_235b.json +ddp_timeout: 36000 +dataloader_num_workers: 4 + +### output +save_strategy: steps +save_steps: 2 +save_total_limit: 1 +output_dir: outputs/llamafactory/qwen3_235b_lora_sft_zero3 diff --git a/dev/llama-factory/configs/qwen3_30b_lora.yaml b/dev/llama-factory/configs/qwen3_30b_lora.yaml new file mode 100644 index 00000000..1379ef3e --- /dev/null +++ b/dev/llama-factory/configs/qwen3_30b_lora.yaml @@ -0,0 +1,65 @@ +### LLaMA-Factory LoRA SFT config for Qwen3-30B A3B Instruct (2x H200) + +### model +model_name_or_path: Qwen/Qwen3-30B-A3B-Instruct-2507 +trust_remote_code: true +template: qwen3 + +# Quantization (QLoRA via BitsAndBytes) +# quantization_method: bnb +# quantization_bit: 4 +# quantization_type: nf4 +# double_quantization: true + +### method +stage: sft +finetuning_type: lora +gradient_checkpointing: true + +# Target common linear modules (attention + MLP). LLaMA‑Factory resolves per-arch +lora_rank: 8 +lora_alpha: 32 +lora_dropout: 0.05 +lora_target: + - q_proj + - k_proj + - v_proj + - o_proj + - gate_proj + - up_proj + - down_proj + +### data +# Use HF dataset directly via ONLINE mode +dataset_dir: ONLINE +dataset: + - tatsu-lab/alpaca +cutoff_len: 1024 +packing: true +max_samples: 16 + +### training +do_train: true +num_train_epochs: 1 +max_steps: 10 +per_device_train_batch_size: 1 +gradient_accumulation_steps: 1 +learning_rate: 0.0002 +lr_scheduler_type: cosine +optim: adamw_torch +bf16: true +report_to: none +logging_strategy: steps +logging_steps: 1 +seed: 42 + +### distributed / system +ddp_timeout: 36000 +dataloader_num_workers: 0 + +### output +save_strategy: steps +save_steps: 2 +save_total_limit: 1 +output_dir: outputs/llamafactory/qwen3_30b_lora_sft_fp2g + diff --git a/dev/llama-factory/pyproject.toml b/dev/llama-factory/pyproject.toml new file mode 100644 index 00000000..ee5c0394 --- /dev/null +++ b/dev/llama-factory/pyproject.toml @@ -0,0 +1,32 @@ +[project] +name = "llama-factory-job" +version = "0.0.1" +description = "Isolated env to run LLaMA-Factory SFT" +requires-python = ">=3.10,<3.13" +dependencies = [ + "llamafactory==0.9.3", + "datasets>=2.19.0", + "torch>=2.4.0", + "accelerate==1.7.0", + "transformers==4.52.4", + "peft>=0.12.0", + "bitsandbytes>=0.43.1", + "tiktoken", + "huggingface-hub>=0.24.0", + "safetensors", + "deepspeed>=0.17.5", +] + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.uv] +environments = ["sys_platform == 'linux'"] +required-version = ">=0.6.15" + +[tool.hatch.build] +sources = ["src"] + +[tool.hatch.build.targets.wheel] +packages = ["llama_factory_job"] diff --git a/dev/llama-factory/src/llama_factory_job/__init__.py b/dev/llama-factory/src/llama_factory_job/__init__.py new file mode 100644 index 00000000..fe16459e --- /dev/null +++ b/dev/llama-factory/src/llama_factory_job/__init__.py @@ -0,0 +1,2 @@ +__all__ 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