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f1b0176 06feb89 f1b0176 06feb89 f1b0176 a1e6f13 f1b0176 06feb89 f1b0176 06feb89 f1b0176 06feb89 f1b0176 06feb89 f1b0176 06feb89 f1b0176 06feb89 f1b0176 06feb89 f1b0176 a1e6f13 06feb89 f1b0176 a1e6f13 f1b0176 06feb89 f1b0176 a1e6f13 06feb89 a1e6f13 06feb89 a1e6f13 06feb89 a1e6f13 06feb89 f1b0176 06feb89 f1b0176 06feb89 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 | from functools import lru_cache
import logging
import os
from time import perf_counter
from training_coach.parser import (
PARSER_MODEL,
build_parser_messages,
log_parser_messages,
log_parser_response_text,
parse_model_response,
)
from training_coach.models import ParsedCheckIn
MODEL_CACHE_ENV_VAR = "PARSER_MODEL_CACHE_DIR"
DEFAULT_TRANSFORMERS_MODEL = "Qwen/Qwen3-1.7B"
DEFAULT_MAX_NEW_TOKENS = 384
DEFAULT_ZEROGPU_DURATION_SECONDS = 120
logger = logging.getLogger(__name__)
class ParserRuntimeUnavailableError(RuntimeError):
pass
def _gpu_decorator():
try:
import spaces
except ImportError:
return lambda function: function
duration = int(
os.getenv("ZEROGPU_DURATION_SECONDS", str(DEFAULT_ZEROGPU_DURATION_SECONDS))
)
return spaces.GPU(duration=duration)
def _load_transformers():
try:
from transformers import AutoModelForCausalLM, AutoTokenizer
except ImportError as error:
raise ParserRuntimeUnavailableError(
"Install transformers, torch, and accelerate to run the parser model."
) from error
return AutoModelForCausalLM, AutoTokenizer
@lru_cache(maxsize=1)
def load_parser_model(model_name: str = DEFAULT_TRANSFORMERS_MODEL):
start_time = perf_counter()
logger.info("event=parser_model_load_start model=%s", model_name)
AutoModelForCausalLM, AutoTokenizer = _load_transformers()
cache_dir = os.getenv(MODEL_CACHE_ENV_VAR) or None
tokenizer = AutoTokenizer.from_pretrained(model_name, cache_dir=cache_dir)
model = AutoModelForCausalLM.from_pretrained(
model_name,
cache_dir=cache_dir,
device_map="auto",
torch_dtype="auto",
)
logger.info(
"event=parser_model_load_complete model=%s cache_dir_configured=%s elapsed_ms=%s",
model_name,
cache_dir is not None,
round((perf_counter() - start_time) * 1000),
)
return tokenizer, model
@_gpu_decorator()
def generate_parser_response(
raw_text: str,
model_name: str = DEFAULT_TRANSFORMERS_MODEL,
) -> str:
start_time = perf_counter()
logger.info(
"event=parser_generate_start model=%s text_chars=%s",
model_name,
len(raw_text),
)
tokenizer, model = load_parser_model(model_name)
messages = build_parser_messages(raw_text)
log_parser_messages(
backend="transformers",
model_name=model_name,
messages=messages,
)
chat_template_kwargs = {
"tokenize": False,
"add_generation_prompt": True,
"enable_thinking": False,
}
try:
prompt = tokenizer.apply_chat_template(messages, **chat_template_kwargs)
except TypeError:
chat_template_kwargs.pop("enable_thinking")
prompt = tokenizer.apply_chat_template(messages, **chat_template_kwargs)
inputs = tokenizer([prompt], return_tensors="pt").to(model.device)
input_token_count = inputs.input_ids.shape[-1]
output_ids = model.generate(
**inputs,
max_new_tokens=int(os.getenv("PARSER_MAX_NEW_TOKENS", str(DEFAULT_MAX_NEW_TOKENS))),
do_sample=False,
pad_token_id=tokenizer.eos_token_id,
)
generated_ids = output_ids[:, inputs.input_ids.shape[-1] :]
generated_token_count = generated_ids.shape[-1]
response_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
logger.info(
"event=parser_generate_complete model=%s prompt_chars=%s "
"input_tokens=%s generated_tokens=%s response_chars=%s elapsed_ms=%s",
model_name,
len(prompt),
input_token_count,
generated_token_count,
len(response_text),
round((perf_counter() - start_time) * 1000),
)
log_parser_response_text(
backend="transformers",
model_name=model_name,
response_text=response_text,
)
return response_text
def parse_check_in_with_model(
raw_text: str,
model_name: str | None = None,
) -> ParsedCheckIn:
model_name = model_name or os.getenv("PARSER_MODEL_ID", DEFAULT_TRANSFORMERS_MODEL)
logger.info("event=parser_model_parse_start model=%s", model_name)
response_text = generate_parser_response(raw_text, model_name=model_name)
parsed = parse_model_response(response_text)
logger.info(
"event=parser_model_parse_complete model=%s missing_fields=%s follow_up_questions=%s",
model_name,
len(parsed.missing_fields),
len(parsed.follow_up_questions),
)
return parsed
|