Update README.md

README.md

@@ -1,3 +1,143 @@
----
-license: mit
----
+---
+license: mit
+---
+
+This repository contains the Model Checkpoint of GLM-4-32B-0414-GPTQ-4bits
+
+Base model: GLM-4-32B-0414
+
+Quantization method: GPTQ
+
+Repository of quantization method: https://github.com/modelcloud/gptqmodel
+
+## Eora Method Dataset
+ 
+
+```python 
+
+from datasets import load_dataset
+
+
+def question_answering_format(question, answer):
+
+    return f"Question: {question}\nAnswer: {answer}"
+
+def multiple_choices_question_answering_format(question, choices, answer):
+    return f"{question.strip()}\nA. {choices[0]}\nB. {choices[1]}\nC. {choices[2]}\nD. {choices[3]}\nAnswer: {answer}"
+
+## An example of using ARC for construting the EoRA calibration set
+
+def construct_c4():
+    calibration_dataset = load_dataset(
+      "/mnt/ceph/develop/jiawei/code_dataset/c4",
+      data_files="en.noblocklist/c4-train.00001-of-01024.json.gz",
+      split="train", download_mode="force_redownload"
+    ).select(range(1024))["text"]
+    return calibration_dataset
+
+def construct_ARC():
+    nsamples = 1024
+    arc_easy_calibration_dataset = load_dataset('ai2_arc', 'ARC-Easy', split='train').select(range(nsamples))
+    arc_challenge_calibration_dataset = load_dataset('ai2_arc', 'ARC-Challenge', split='train').select(range(nsamples))
+    dataset = []
+
+    for example in arc_easy_calibration_dataset:
+        answer = example['choices']['text'][example['choices']['label'].index(example['answerKey'])]
+        question = example['question']
+        dataset.append(question_answering_format(question=question,answer=answer))
+
+    for example in arc_challenge_calibration_dataset:
+        answer = example['choices']['text'][example['choices']['label'].index(example['answerKey'])]
+        question = example['question']
+        dataset.append(question_answering_format(question=question,answer=answer))
+
+    ## we recommend also include some examples from C4 to avoid overfitting to the downstream data
+    c4_dataset = load_dataset(
+        "allenai/c4",
+        data_files="en/c4-train.00001-of-01024.json.gz",
+        split="train"
+    ).select(range(nsamples))["text"]
+
+    return dataset + c4_dataset
+
+def multiple_identity_format(instruction, input_q, output):
+    return f"{instruction.strip()} {input_q}\n {output}"
+def construct_mmlu():
+
+    mmlu_calibration_dataset = load_dataset('/mnt/ceph/develop/jiawei/code_dataset/mmlu', 'all', split='validation')
+    dataset = []
+    for example in mmlu_calibration_dataset:
+        question = example['question']
+        choices = example['choices']
+        answer = ['A','B','C','D'][example['answer']]
+        dataset.append(multiple_choices_question_answering_format(question, choices, answer))
+    identity_dataset = load_dataset(
+        "json", 
+        data_files="/mnt/ceph/develop/jiawei/GPTQModel/examples/eora/identity.json",
+        split="train"
+    )
+
+    for example in identity_dataset:
+        instruction = example['instruction']
+        input_q = example['input']
+        output = example['output']
+        dataset.append(multiple_identity_format(instruction, input_q, output))
+
+    ## we recommend also include some examples from C4 to avoid overfitting to the downstream data
+    c4_dataset = load_dataset(
+        "/mnt/ceph/develop/jiawei/code_dataset/c4",
+        data_files="en.noblocklist/c4-train.00001-of-01024.json.gz",
+        split="train"
+    ).select(range(1024))["text"] 
+    
+    return dataset + c4_dataset
+
+
+```
+
+
+2. quantization
+
+```python
+
+python examples/eora/eora_generation.py THUDM/GLM-4-32B-0414 --bits 4 --quant_save_path glide-the/GLM-4-32B-0414-GPTQ-4bits --eora_dataset mmlu --eora_save_path glide-the/GLM-4-32B-0414-GPTQ-4bits-eora_rank64_c4 --eora_rank 64 
+
+```
+
+3. inference
+
+```python
+
+
+python examples/eora/eora_load_and_inference.py --quantized_model glide-the/GLM-4-32B-0414-GPTQ-4bits   
+
+```
+
+
+
+# Usage transformers
+
+
+```python
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("glide-the/GLM-4-32B-0414-GPTQ-4bits")
+quantized_model = AutoModelForCausalLM.from_pretrained("glide-the/GLM-4-32B-0414-GPTQ-4bits")
+
+print(tokenizer.decode(quantized_model.generate(**tokenizer("""从以下文本中提取 (公司, 创始人) 对：
+
+示例1：
+文本："马云创立了阿里巴巴。"
+输出：[("阿里巴巴", "马云")]
+
+示例2：
+文本："比尔·盖茨是微软的联合创始人。"
+输出：[("微软", "比尔·盖茨")]
+
+待抽取：
+文本："乔布斯创办了苹果公司。"
+输出：""", return_tensors="pt").to(quantized_model.device))[0]))
+
+```
+