Upload README.md

README.md

@@ -5,4 +5,73 @@ datasets:
 - BelleGroup/train_2M_CN
 language:
 - zh
----
+---
+# Langboat_bloom-6b4-zh-instruct_finetune-chat
+是基于Langboat_bloom-6b4-zh模型，在firefly-train-1.1M和Belle-train_2m_cn数据集上采用的QLoRA方法微调的对话模型。  
+在CEVAL上的评测结果：  
+
+| STEM | Social Sciences | Humanities | Others | Average | AVG(Hard) |  
+|------|-----------------|------------|--------|---------|-----------|  
+| 27.9 | 27.2            | 24.8       | 26.4   | 26.8    | 28.0      |
+
+# 使用
+## 单轮指令生成
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+device = "cuda"
+model = AutoModelForCausalLM.from_pretrained("SmilePanda/Langboat_bloom-6b4-zh-instruct_finetune-chat", device_map=device)
+tokenizer = AutoTokenizer.from_pretrained("SmilePanda/Langboat_bloom-6b4-zh-instruct_finetune-chat", use_fast=False)
+
+source_prefix = "human"
+target_prefix = "assistant"
+query = "你好"
+sentence = f"{source_prefix}: \n{query}\n\n{target_prefix}: \n"
+print("query: ", sentence)
+input_ids = tokenizer(sentence, return_tensors='pt').input_ids.to(device)
+outputs = model.generate(input_ids=input_ids, max_new_tokens=500,
+                         do_sample=True,
+                         top_p=0.8,
+                         temperature=0.35,
+                         repetition_penalty=1.2,
+                         eos_token_id=tokenizer.eos_token_id)
+rets = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0].strip()
+response = rets.replace(sentence, "")
+print(response)
+```
+
+## 多轮对话
+```python
+import os
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+device = "cuda"
+model = AutoModelForCausalLM.from_pretrained("SmilePanda/Langboat_bloom-6b4-zh-instruct_finetune-chat", device_map=device)
+tokenizer = AutoTokenizer.from_pretrained("SmilePanda/Langboat_bloom-6b4-zh-instruct_finetune-chat", use_fast=False)
+
+source_prefix = "human"
+target_prefix = "assistant"
+
+history = ""
+
+while True:
+    query = input("user: ").strip()
+    if not query:
+        continue
+    if query == 'q' or query == 'stop':
+        break
+    if history:
+        sentence = history + f"\n{source_prefix}: \n{query}\n\n{target_prefix}: \n"
+    else:
+        sentence = f"{source_prefix}: \n{query}\n\n{target_prefix}: \n"
+    input_ids = tokenizer(sentence, return_tensors='pt').input_ids.to(device)
+    outputs = model.generate(input_ids=input_ids, max_new_tokens=1024,
+                             do_sample=True,
+                             top_p=0.90,
+                             temperature=0.1,
+                             repetition_penalty=1.0,
+                             eos_token_id=tokenizer.eos_token_id)
+    rets = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0].strip()
+    print("bloom: {}".format(rets.replace(sentence, "")))
+    history = rets
+```