Add Aime2026Dataset class loading from MathArena/aime_2026 on
HuggingFace. 30 problems (two sets of 15), single config/split.
Usage: --dataset aime2026
Assisted-by: llama.cpp:local pi
* working llama-eval mc and math suite
* multi source llama-eval
* Add readme
* add checkpointing
* examples: add llama-server simulator for testing eval scripts
Add a standalone Python script that simulates a llama-server HTTP endpoint
for testing the eval script. The simulator:
- Implements /v1/chat/completions endpoint with OpenAI-compatible format
- Loads AIME dataset from HuggingFace with local caching
- Uses Levenshtein distance for intelligent question matching
- Supports configurable success rate for correct/wrong answer generation
- Provides debug logging for troubleshooting
Also includes test scripts and documentation for testing and understanding
the simulator functionality.
* examples: refactor test-simulator.sh for better readability
Extract repeating question string into TEST_QUESTION variable and
create make_request() helper function to reduce code duplication.
Add proper error handling for error responses.
* docs: update llama-eval-discussion.md with session work summary
Add summary of llama-server-simulator implementation work including
features, testing results, technical decisions, and refactoring.
* examples: add simplified llama-eval-new.py for AIME evaluation
- Create new simplified evaluation script focused only on AIME
- Implement EvalState and Processor dataclasses for structured state management
- Add real-time feedback showing correct/incorrect status per case
- Abstract grading interface for external grader support
- Use structured JSON output for eval state
- Apply HuggingFace dataset caching to avoid repeated downloads
- Remove Levenshtein matching - eval script only sends requests and validates answers
* docs: remove README.md from llama-eval
* examples: implement flexible grader system for answer validation
- Add Grader class supporting regex and CLI-based grading
- Implement built-in regex patterns for AIME, GSM8K, MMLU, HellaSwag, ARC, WinoGrande
- Add CLI grader interface: python script.py --answer <pred> --expected <gold>
- Add HF telemetry disable to avoid warnings
- Support exact match requirement for regex patterns
- Add 30-second timeout for CLI grader
- Handle both boxed and plain text formats for AIME answers
* examples: use HF_HUB_OFFLINE to avoid HF Hub warnings
* examples: remove HF_HUB_OFFLINE to allow dataset download
* examples: use cached dataset path to avoid HF Hub requests
* examples: use cached dataset path in simulator to avoid HF Hub requests
* docs: update llama-eval-discussion.md with session work summary
* examples: add threading support and model parameter to llama-eval-new.py
- Add ThreadPoolExecutor for parallel request processing controlled by --threads
- Add --model argument to specify model name in request data
- Refactor process() to use thread-safe _process_single_case() method
- Update progress tracking to work with concurrent execution
* docs: update llama-eval-discussion.md with threading and model parameter updates
- Add threading support implementation details
- Document ThreadPoolExecutor usage and thread safety
- Add model parameter implementation details
- Include testing results for both features
* examples: add task summary table to llama-eval-new.py
* eval : print progress
* eval : add prompts
* test : fix path
* sim : fix answer matching
* eval : support multiple dataset runs
* minor
* improve grader
* docs
* remove old files
* datasets : add gsm8k
* add gpqa + sampling + docs
* rename
* grader : improve example answers
* cont
* datasets : add aime2025
* grader : update prompt
* grade : improve regex + logs
* datasets : fix aime2025
* cleanup
* add AGENTS.md
* ignore errors
* resume eval
* cleanup
* fix counts
* simplify
* fix prompts
* add html
* store full response
* add tokens
* resoning and error handling
* refactor
* track total time
* remove junk
* eval : unify "judge" terminology to "grader"
Replace all occurrences of "judge" with "grader" for consistency
across the codebase (CLI args, Grader class fields, help text).
Assisted-by: llama.cpp:local pi
* eval : add Wilson score confidence interval to results
Compute 95% CI on-the-fly from completed cases. Displayed in
terminal output, HTML report, and JSON state.
* llama-eval : add per-task generation speed from server timings
Extract predicted_per_second from the server timings response and store
it as tps_gen per task. Display in console progress, print_all_tasks,
and HTML report.
Assisted-by: llama.cpp:local pi
* llama-eval : add per-task generation time from server timings
Extract predicted_ms from the server timings response and store it as
t_gen_ms per task. Display in seconds with one decimal digit in console
progress, print_all_tasks, and HTML report.
Assisted-by: llama.cpp:local pi
* llama-eval : rename display, escaped, and count variables to use prefix convention
- _display suffix → display_ prefix (answer, tokens, tps, t_gen)
- _escaped suffix → escaped_ prefix (response, prompt, reasoning)
- _count suffix → n_ prefix (correct, incorrect, pending)
Assisted-by: llama.cpp:local pi
* llama-eval : support multiple evaluation endpoints with dynamic task distribution
- Add ServerConfig dataclass (url, threads, name)
- Accept comma-separated --server, --threads, --server-name CLI args
- Dynamic shared-queue task distribution across servers (fast servers do more work)
- One ThreadPoolExecutor per server, workers pull from shared Queue
- Track which server processed each task (server_name in results)
- Thread-safe EvalState with threading.Lock for concurrent mutations
- Server column in HTML report and console output
- Backward compatible: single server works as before
Assisted-by: llama.cpp:local pi
* llama-server-simulator : replace Flask with stdlib http.server
- Use HTTPServer + BaseHTTPRequestHandler instead of Flask
- RequestHandler handles POST /v1/chat/completions
- Server runs in daemon thread with clean Ctrl+C shutdown
- Remove flask and unused asdict imports
Assisted-by: llama.cpp:local pi
* llama-eval : update README with PR link and quick-start examples
Assisted-by: llama.cpp:local pi
* llama-eval : track model name in eval state and verify on resume
- Store model_name in EvalState and JSON output
- Display model in HTML summary table
- Verify --model matches stored model when resuming
Assisted-by: llama.cpp:local pi
* llama-server-simulator : fix comment - Dice coefficient, not Levenshtein
Assisted-by: llama.cpp:local pi
* llama-eval : require --grader-model or --model when using --grader-type llm
Assisted-by: llama.cpp:local pi
* llama-eval : protect dump() with lock for thread safety
Assisted-by: llama.cpp:local pi
* llama-eval : compact HTML report output
- Replace verbose summary table with single inline bar
- Shorten status text: '✓'/'✗'/'–'/'!' instead of full words
- Flatten CSS: remove box-shadows, border-radius, reduce padding
- Use system-ui font, 13px table, 12px details
- Conditional reasoning section (only shown when present)
- Single toggle JS function instead of two
- Shorter column headers
Assisted-by: llama.cpp:local pi
* llama-eval : check server connectivity on startup
- Hit /v1/models for each server before evaluation
- Exit with error if any server is unreachable
- Print comma-separated model IDs per server in startup output
- Sequential checks, no retries, no timeout override
Assisted-by: llama.cpp:local pi
* llama-eval : use server1/server2 instead of gpu1/gpu2 in README
Assisted-by: llama.cpp:local pi
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Co-authored-by: gatbontonpc <gatbontonpc@gmail.com>
