Instructions to use joshycodes/fve-msm-exp2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use joshycodes/fve-msm-exp2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("joshycodes/fve-msm-exp2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Restore release docs, data and per-run configs without the duplicated weights (part 2)
53b43be verified Download training/scripts/submit_s2_big.sh from joshycodes/fve-msm-exp2: direct link, hf CLI and curl.
- Browser
- Download file 2.84 kB
-
https://huggingface.co/joshycodes/fve-msm-exp2/resolve/main/training/scripts/submit_s2_big.sh
- Command line
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hf download hf://joshycodes/fve-msm-exp2/training/scripts/submit_s2_big.sh
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curl -L -o submit_s2_big.sh https://huggingface.co/joshycodes/fve-msm-exp2/resolve/main/training/scripts/submit_s2_big.sh
2.84 kB
| # Second training runs (seed 20261010) for every Gemma and Qwen arm: same recipe, data revisions and base revisions as run 1 | |
| # (train_run_config.json of each run-1 repo); only the seed and the output repo differ. Usage: submit_s2_big.sh dry|submit [arm ...] | |
| set -u | |
| MODE=$1; shift | |
| cd ~/dev/flourishing-training | |
| G=(--gpu-count 8 --fsdp --no-intermediate-ckpts --no-affect-rollouts --backend slurm --slurm-qos high --slurm-mem-per-gpu 128 --no-follow | |
| --chat-data joshycodes/fve-msm-chat-sft-13k@7cba8972c8aefdc46552a9f73395d0de484ffeea --chat-template-file train/templates/fve-msm-chatml.jinja | |
| --seed 20261010 --no-push-intermediate) | |
| GEM=(--model google/gemma-4-31B --transformers-spec transformers==5.17.0 --fsdp-layer-cls Gemma4TextDecoderLayer --midtrain-lr 1e-5 | |
| --micro-batch 1 --grad-accum 4 --chat-micro-batch 1 --chat-grad-accum 8) | |
| QWE=(--model Qwen/Qwen3.8-27B --transformers-spec transformers==5.16.1 --fsdp-layer-cls Qwen3_5DecoderLayer --midtrain-lr 1e-5 | |
| --micro-batch 2 --grad-accum 2 --chat-micro-batch 2 --chat-grad-accum 4) | |
| SPEC=joshycodes/fve-msm-llama | |
| declare -A ARGS=( | |
| [gemma-control]="${GEM[*]} --steps chat --output-repo joshycodes/gemma-4-31b-fve-msm-control-s2" | |
| [gemma-flourishing]="${GEM[*]} --steps midtrain,chat --midtrain-data $SPEC:midtrain-gemma-flourishing@c510b9e5321c9792fdb0cc0b1d9e2bf85a9105f7 --output-repo joshycodes/gemma-4-31b-fve-msm-flourishing-s2" | |
| [gemma-equanimity]="${GEM[*]} --steps midtrain,chat --midtrain-data $SPEC:midtrain-gemma-equanimity@c510b9e5321c9792fdb0cc0b1d9e2bf85a9105f7 --output-repo joshycodes/gemma-4-31b-fve-msm-equanimity-s2" | |
| [gemma-replay]="${GEM[*]} --steps midtrain,chat --midtrain-data $SPEC:midtrain-gemma-replay@c3535ebfc8293ca4a85d3be933ca44f9d9c8eeae --output-repo joshycodes/gemma-4-31b-fve-msm-replay-s2" | |
| [qwen-control]="${QWE[*]} --steps chat --output-repo joshycodes/qwen3.8-27b-fve-msm-control-s2" | |
| [qwen-flourishing]="${QWE[*]} --steps midtrain,chat --midtrain-data $SPEC:midtrain-qwen-flourishing@c510b9e5321c9792fdb0cc0b1d9e2bf85a9105f7 --output-repo joshycodes/qwen3.8-27b-fve-msm-flourishing-s2" | |
| [qwen-equanimity]="${QWE[*]} --steps midtrain,chat --midtrain-data $SPEC:midtrain-qwen-equanimity@c510b9e5321c9792fdb0cc0b1d9e2bf85a9105f7 --output-repo joshycodes/qwen3.8-27b-fve-msm-equanimity-s2" | |
| ) | |
| ARMS=("$@"); [ ${#ARMS[@]} -eq 0 ] && ARMS=(gemma-control gemma-flourishing gemma-equanimity gemma-replay qwen-control qwen-flourishing qwen-equanimity) | |
| for a in "${ARMS[@]}"; do | |
| out=~/dev/fve-msm/train/s2_big/${a}.${MODE}.log | |
| if [ "$MODE" = dry ]; then | |
| timeout 600 uv run train.py "${G[@]}" ${ARGS[$a]} --dry-run > "$out" 2>&1; echo "$a dry exit $?" | |
| else | |
| timeout 600 uv run train.py "${G[@]}" ${ARGS[$a]} > "$out" 2>&1; echo "$a submit exit $?: $(grep -E 'submitted job' "$out" | tail -1)" | |
| fi | |
| done | |