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Running on Zero
Running on Zero
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0dd6c2f | 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 | import unsloth # noqa: I001, F401
import torch
from datasets import load_dataset
from peft import PeftModel
from transformers import AutoTokenizer
from unsloth import FastLanguageModel
from linalg_zero.sft.tool_calling_accuracy import ToolCallingAccuracyCallback
def load_unmerged():
path = "results/LinalgZero-SFT-LoRA/checkpoint-400-best"
# path = "results/LinalgZero-SFT-LoRA-110/checkpoint-110"
tokenizer = AutoTokenizer.from_pretrained(path)
print(f"Tokenizer vocab size: {len(tokenizer)}")
model, _ = FastLanguageModel.from_pretrained(
model_name="Qwen/Qwen2.5-3B",
max_seq_length=8192,
load_in_4bit=False,
fast_inference=False,
)
model = PeftModel.from_pretrained(
model,
path,
is_trainable=False,
)
# tokenizer.push_to_hub("atomwalk12/LinalgZero-SFT-LoRA")
# model.push_to_hub("atomwalk12/LinalgZero-SFT-LoRA")
FastLanguageModel.for_inference(model)
return model, tokenizer
def load_merged():
# Best models
# Notice that best LoRA is checkpoint 400, while best merged is 300
checkpoint_path = "results/LinalgZero-SFT/checkpoint-300-best"
# checkpoint_path = "results/LinalgZero-SFT-merged"
# checkpoint_path = "atomwalk12/LinalgZero-SFT-merged"
# checkpoint_path = "atomwalk12/LinalgZero-SFT"
# GRPO prep.
# DONE
# checkpoint_path = "results/LinalgZero-SFT-110/checkpoint-110"
# checkpoint_path = "results/LinalgZero-SFT-105/checkpoint-105"
# DONE
# checkpoint_path = "results/LinalgZero-SFT-110-checkpoint-300/checkpoint-300"
tokenizer = AutoTokenizer.from_pretrained(checkpoint_path)
print(f"Tokenizer vocab size: {len(tokenizer)}")
model, tok2 = FastLanguageModel.from_pretrained(
model_name=checkpoint_path,
max_seq_length=8192,
load_in_4bit=False,
fast_inference=False,
)
assert len(tok2) == len(tokenizer)
# Best models
model.push_to_hub("atomwalk12/LinalgZero-SFT")
tokenizer.push_to_hub("atomwalk12/LinalgZero-SFT")
# model.push_to_hub("atomwalk12/LinalgZero-SFT-merged")
# tokenizer.push_to_hub("atomwalk12/LinalgZero-SFT-merged")
# GRPO prep.
# DONE
# model.push_to_hub("atomwalk12/LinalgZero-SFT-105")
# tokenizer.push_to_hub("atomwalk12/LinalgZero-SFT-105")
# DONE
# model.push_to_hub("atomwalk12/LinalgZero-SFT-110")
# tokenizer.push_to_hub("atomwalk12/LinalgZero-SFT-110")
# DONE
# model.push_to_hub("atomwalk12/LinalgZero-SFT-110-checkpoint-300")
# tokenizer.push_to_hub("atomwalk12/LinalgZero-SFT-110-checkpoint-300")
# model.push_to_hub("atomwalk12/LinalgZero-SFT")
# tokenizer.push_to_hub("atomwalk12/LinalgZero-SFT")
FastLanguageModel.for_inference(model)
return model, tokenizer
model, tokenizer = load_unmerged()
eval_ds = load_dataset("atomwalk12/linalgzero-sft", split="test") # or whatever split you used
cb = ToolCallingAccuracyCallback(
model_name="atomwalk12/LinAlgZero-SFT-merged",
dataset_name="atomwalk12/linalgzero",
eval_dataset=eval_ds,
)
gen_config = cb.get_generation_config(max_new_tokens=800, tokenizer=tokenizer)
def generate_like_sft_eval(sample_idx: int = 0):
sample = eval_ds[sample_idx]
context = list(sample["messages"])
tools = sample["tools"]
print(f"Query is: {sample['messages'][-1]['content']}")
prompt = tokenizer.apply_chat_template(
context,
tools=tools,
tokenize=False,
add_generation_prompt=True,
)
prompt = prompt
inputs = tokenizer(prompt, return_tensors="pt", truncation=True, padding=True)
inputs = {k: v.to(model.device) for k, v in inputs.items()}
with torch.no_grad():
outputs = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
tokenizer=tokenizer,
**gen_config,
)
# Decode only the generated continuation (optional: mimic callback's decoding)
prompt_len = inputs["input_ids"].shape[1]
gen_tokens = outputs[:, prompt_len:]
text = tokenizer.decode(gen_tokens[0], skip_special_tokens=False)
print(text)
return text
result = generate_like_sft_eval(0)
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