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-
sorted imports
8f2eab9 -
Merge branch 'master' into hf_branch
28fdd67 -
added more tasks
3b7f446
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updated open env yaml config
4f52a38 -
Merge branch 'master' into hf_branch
6998881 -
refactor: move training code to scripts/, add train/eval split, tune GRPO hyperparams
- Relocate NeuralTunerOpenEnv and training utilities from server/ to scripts/ - Add 80/20 train/eval scenario split (TRAIN_FRACTION, SPLIT_SEED) for proper held-out evaluation - Refactor post_training_logs to evaluate held-out set against random/baseline/heuristic baselines - Switch oracle from run_baseline_episode to run_heuristic_episode (stronger ceiling) - Tune GRPO: lr 1e-6→2e-6, max_completion_length 384→256, NUM_STEPS 1→20, TRAIN_EPISODES 100→80 - Add prune_layer to ALLOWED_ACTIONS in inference.py - Replace weave with standard W&B; add WANDB_PROJECT env var and conditional report_to - Add device smoke check cell for MPS/CPU validation before trainingfad16c9 -
feat: add OpenEnv TRL wrapper, expand dataset, and add W&B eval tracking
- Add server/neural_tuner.py: OpenEnv-compatible wrapper for TRL environment_factory with scenario scheduling and step dispatch - Expand training dataset from 16 to 100 scenarios/prompts covering more model/difficulty combinations - Re-run training: non-zero loss achieved (-0.094), runtime down to 141s from 425s - Add wandb + weave dependencies for experiment tracking - Add eval artifacts: comparison_metrics.json, eval_metrics.json, and training plots - Update .gitignore to exclude wandb/ run directories6fa4fbd -
feat: add structured pruning action and random baseline policy
Extends NeuralTunerAction with a new prune_layer op (LOW/MEDIUM/HIGH sparsity) alongside quantization, so the LLM agent can now compress models via both axes simultaneously. Pruning and quantization effects stack multiplicatively for latency/memory and additively for accuracy penalty in the simulator, matching real Snapdragon HTP behaviour. Key changes: - models.py: add prune_layer action_type and sparsity field (LOW=25%, MEDIUM=50%, HIGH=75%) - server/simulator.py: PRUNE_CONFIGS table, simulate() and get_benchmark_report() now accept a prune_map; profile report includes per-layer pruning advice based on sensitivity - server/neural_tuner_env_environment.py: _prune_map state, _handle_prune() handler, revert_layer resets both dtype and sparsity, benchmark passes prune_map through to simulator - client.py: forward sparsity field in action payload - rollout_eval.py: add run_random_episode() (zero-knowledge baseline, expected reward 0.20–0.45), run_policy_sweep() to run all thr…d064b19 -
refactor: harden imports, add training extras, and rewrite README
- Wrap all openenv and relative imports in try/except so the package works both as an installed module and when run from the repo root without openenv present (models.py, client.py, server/app.py, server/neural_tuner_env_environment.py, __init__.py) - Add optional `training` dependency group to pyproject.toml (trl, transformers, datasets, accelerate, jupyter, matplotlib, pandas) - Wire main.py CLI entrypoint to rollout_eval harness - Rewrite README to describe the actual NeuralTuner RL environment (loop, reward design, quick-start, test commands) instead of the generic echo-env template - Extend .gitignore with .env.local, .hf_cache, .pytest_cache5dd60b9 -
fix: resolve merge conflict markers in openenv.yaml and uv.lock
c524b25 -
updated readme for hf deployment
3e57db4 -
Merge branch 'master' into hf_branch
0575199 -
implement NeuralTuner RL environment for Snapdragon quantization
Replace the echo stub with a full quantization optimization environment where an LLM agent profiles neural network layers and applies FP16/INT8/INT4 dtype decisions to meet Snapdragon HTP latency, memory, and accuracy budgets. - Add five agent actions: profile_layer, quantize_layer, revert_layer, benchmark, submit — dispatched via a handler table in NeuralTunerEnvironment - Introduce NeuralTunerState (extends State) and redesign Action/Observation models to carry structured fields (action_type, layer_id, dtype, output, success, error) instead of the old echo message contract - Integrate model_zoo, scenarios, and HardwareSimulator for realistic episode generation with difficulty tiers - Wire multi-component reward: latency improvement (40%), memory constraint (30%), accuracy retention (20%), efficiency bonus (10%) - Add black and isort as dev dependencies782222a -
merge hf space
c2f781b -
first commit
15c3238