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db82745 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 | """Cross-Model Learning — Bee learns from multiple teacher LLMs simultaneously.
Queries OpenAI, Anthropic, and local models for the same prompt,
distills their consensus into Bee through multi-teacher distillation.
This is how Bee learns from Claude, GPT-4, Gemini, etc. without
needing their weights.
Requires OPENAI_API_KEY and/or ANTHROPIC_API_KEY env vars.
Falls back to local models if APIs unavailable.
"""
import argparse
import json
import logging
import os
import sys
import time
from pathlib import Path
import torch
import torch.nn.functional as F
from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer
sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
from bee.register import register
from bee.config import BeeConfig
from bee.modeling_bee import BeeForCausalLM
register()
logging.basicConfig(level=logging.INFO, format="%(asctime)s | %(levelname)s | %(name)s | %(message)s")
logger = logging.getLogger("bee.cross_model")
def query_openai(prompt, model="gpt-3.5-turbo"):
api_key = os.environ.get("OPENAI_API_KEY")
if not api_key:
return None
try:
import openai
client = openai.OpenAI(api_key=api_key)
resp = client.chat.completions.create(
model=model,
messages=[{"role": "user", "content": prompt}],
temperature=0.7,
max_tokens=256,
)
return resp.choices[0].message.content
except Exception as e:
logger.warning("OpenAI query failed: %s", e)
return None
def query_anthropic(prompt, model="claude-3-haiku-20240307"):
api_key = os.environ.get("ANTHROPIC_API_KEY")
if not api_key:
return None
try:
import anthropic
client = anthropic.Anthropic(api_key=api_key)
resp = client.messages.create(
model=model,
max_tokens=256,
messages=[{"role": "user", "content": prompt}],
)
return resp.content[0].text
except Exception as e:
logger.warning("Anthropic query failed: %s", e)
return None
def query_local(prompt, model_id="HuggingFaceTB/SmolLM2-135M", device="cpu"):
"""Query a local model as a teacher."""
try:
tok = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True).to(device).eval()
inputs = tok(prompt, return_tensors="pt").to(device)
with torch.no_grad():
out = model.generate(**inputs, max_new_tokens=128, do_sample=True, temperature=0.7)
return tok.decode(out[0], skip_special_tokens=True)
except Exception as e:
logger.warning("Local model query failed: %s", e)
return None
def distill_from_texts(student, tokenizer, texts, device, learning_rate=5e-4, steps_per_text=5):
"""Distill from teacher-generated text strings into student."""
optimizer = torch.optim.AdamW(student.parameters(), lr=learning_rate)
student.train()
total_loss = 0.0
n = 0
for text in texts:
if not text:
continue
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=256).to(device)
if inputs["input_ids"].shape[1] < 4:
continue
for _ in range(steps_per_text):
optimizer.zero_grad()
out = student(**inputs)
logits = out.logits if hasattr(out, "logits") else out[0]
shift_logits = logits[:, :-1, :].contiguous().view(-1, logits.size(-1))
shift_labels = inputs["input_ids"][:, 1:].contiguous().view(-1)
loss = F.cross_entropy(shift_logits, shift_labels)
loss.backward()
torch.nn.utils.clip_grad_norm_(student.parameters(), 1.0)
optimizer.step()
total_loss += loss.item()
n += 1
return total_loss / max(n, 1)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--student_config", type=str, default="nano",
choices=["nano", "tiny"], help="Student size")
parser.add_argument("--num_queries", type=int, default=20)
parser.add_argument("--output_dir", type=str, required=True)
parser.add_argument("--device", type=str, default="mps" if torch.backends.mps.is_available() else "cpu")
parser.add_argument("--local_teacher", type=str, default="HuggingFaceTB/SmolLM2-135M")
parser.add_argument("--use_openai", action="store_true")
parser.add_argument("--use_anthropic", action="store_true")
args = parser.parse_args()
os.makedirs(args.output_dir, exist_ok=True)
# Init student
if args.student_config == "nano":
cfg = BeeConfig(vocab_size=49152, hidden_size=512, num_hidden_layers=8,
num_attention_heads=8, intermediate_size=1024, max_position_embeddings=2048)
else:
cfg = BeeConfig(vocab_size=49152, hidden_size=1024, num_hidden_layers=16,
num_attention_heads=16, intermediate_size=2816, max_position_embeddings=4096)
student = BeeForCausalLM(cfg).to(args.device)
n_params = sum(p.numel() for p in student.parameters())
logger.info("Student params: %.2fM", n_params / 1e6)
# Use SmolLM tokenizer (vocab compatible)
tok = AutoTokenizer.from_pretrained(args.local_teacher, trust_remote_code=True)
if tok.pad_token is None:
tok.pad_token = tok.eos_token
# Load prompts from TinyStories
ds = load_dataset("roneneldan/TinyStories", split="train", streaming=True)
ds = ds.take(args.num_queries)
results = []
all_teacher_texts = []
for i, ex in enumerate(ds):
prompt = ex["text"][:128] # Use first 128 chars as prompt
logger.info("Query %d/%d: prompt='%s...'", i + 1, args.num_queries, prompt[:40])
responses = {}
if args.use_openai:
r = query_openai(prompt)
if r:
responses["openai"] = r
if args.use_anthropic:
r = query_anthropic(prompt)
if r:
responses["anthropic"] = r
# Always query local teacher
r = query_local(prompt, args.local_teacher, args.device)
if r:
responses["local"] = r
logger.info(" Got %d teacher responses", len(responses))
for src, txt in responses.items():
all_teacher_texts.append(txt)
results.append({"step": i, "source": src, "prompt": prompt, "response": txt})
# Incremental distillation every 5 queries
if (i + 1) % 5 == 0 and all_teacher_texts:
logger.info(" Distilling from %d teacher texts...", len(all_teacher_texts))
avg_loss = distill_from_texts(student, tok, all_teacher_texts, args.device)
logger.info(" Avg loss: %.4f", avg_loss)
all_teacher_texts = [] # Clear to avoid re-distilling
# Final save
student.save_pretrained(args.output_dir)
tok.save_pretrained(args.output_dir)
with open(os.path.join(args.output_dir, "cross_model_log.json"), "w") as f:
json.dump(results, f, indent=2)
logger.info("Cross-model learning complete. Model saved to %s", args.output_dir)
logger.info("Total teacher responses collected: %d", len(results))
if __name__ == "__main__":
main()
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