Create README.md
Browse files
README.md
ADDED
@@ -0,0 +1,37 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
license: other
|
3 |
+
tags:
|
4 |
+
- yi
|
5 |
+
- moe
|
6 |
+
license_name: yi-license
|
7 |
+
license_link: https://huggingface.co/01-ai/Yi-34B-200K/blob/main/LICENSE
|
8 |
+
---
|
9 |
+
|
10 |
+
|
11 |
+
|
12 |
+
* this is 4bit 60B MoE model trained by SFTTrainer based on [cloudyu/4bit_quant_TomGrc_FusionNet_34Bx2_MoE_v0.1_DPO]
|
13 |
+
* nampdn-ai/tiny-codes sampling about 2000 cases
|
14 |
+
* Metrics not Test
|
15 |
+
|
16 |
+
code example
|
17 |
+
```
|
18 |
+
import torch
|
19 |
+
from transformers import AutoTokenizer, AutoModelForCausalLM
|
20 |
+
import math
|
21 |
+
|
22 |
+
model_path = "cloudyu/60B-MoE-Coder-v2"
|
23 |
+
|
24 |
+
tokenizer = AutoTokenizer.from_pretrained(model_path, use_default_system_prompt=False)
|
25 |
+
model = AutoModelForCausalLM.from_pretrained(
|
26 |
+
model_path, torch_dtype=torch.bfloat16, device_map='auto',local_files_only=False, load_in_4bit=True
|
27 |
+
)
|
28 |
+
print(model)
|
29 |
+
prompt = input("please input prompt:")
|
30 |
+
while len(prompt) > 0:
|
31 |
+
input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to("cuda")
|
32 |
+
|
33 |
+
generation_output = model.generate(
|
34 |
+
input_ids=input_ids, max_new_tokens=1500,repetition_penalty=1.1
|
35 |
+
)
|
36 |
+
print(tokenizer.decode(generation_output[0]))
|
37 |
+
prompt = input("please input prompt:")
|