Update README.md
Browse files
README.md
CHANGED
|
@@ -1,5 +1,4 @@
|
|
| 1 |
---
|
| 2 |
-
license name: deepseek
|
| 3 |
library_name: transformers
|
| 4 |
tags:
|
| 5 |
- code
|
|
@@ -12,11 +11,13 @@ datasets:
|
|
| 12 |
metrics:
|
| 13 |
- code_eval
|
| 14 |
pipeline_tag: text-generation
|
|
|
|
|
|
|
| 15 |
---
|
| 16 |
## AIGCodeGeek-DS-6.7B
|
| 17 |
|
| 18 |
### Introduction
|
| 19 |
-
AIGCodeGeek-DS-6.7B is
|
| 20 |
|
| 21 |
### Model Details
|
| 22 |
#### Model Description
|
|
@@ -26,7 +27,7 @@ AIGCodeGeek-DS-6.7B is the first released version of our Code-LLM family with co
|
|
| 26 |
|
| 27 |
### Training data
|
| 28 |
A mixture of samples from high-quality open-source (read *Acknowledgements*) and our private datasets.
|
| 29 |
-
We have made contamination detection as Magicoder/Bigcode did.
|
| 30 |
|
| 31 |
### Evaluation
|
| 32 |
results to be added.
|
|
@@ -48,11 +49,12 @@ attrdict
|
|
| 48 |
### QuickStart
|
| 49 |
|
| 50 |
```python
|
|
|
|
| 51 |
from transformers import AutoTokenizer, AutoModelForCausalLM
|
| 52 |
tokenizer = AutoTokenizer.from_pretrained("aigcode/AIGCodeGeek-DS-6.7B", trust_remote_code=True)
|
| 53 |
model = AutoModelForCausalLM.from_pretrained("aigcode/AIGCodeGeek-DS-6.7B", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
|
| 54 |
messages=[
|
| 55 |
-
{ 'role': 'user', 'content': "write a
|
| 56 |
]
|
| 57 |
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
|
| 58 |
# tokenizer.eos_token_id is the id of <|EOT|> token
|
|
@@ -60,8 +62,6 @@ outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50,
|
|
| 60 |
print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
|
| 61 |
```
|
| 62 |
|
| 63 |
-
### Limits
|
| 64 |
-
|
| 65 |
|
| 66 |
### Acknowledgements
|
| 67 |
We gain a lot of knowledge and resources from the open-source community:
|
|
|
|
| 1 |
---
|
|
|
|
| 2 |
library_name: transformers
|
| 3 |
tags:
|
| 4 |
- code
|
|
|
|
| 11 |
metrics:
|
| 12 |
- code_eval
|
| 13 |
pipeline_tag: text-generation
|
| 14 |
+
license: other
|
| 15 |
+
license name: deepseek
|
| 16 |
---
|
| 17 |
## AIGCodeGeek-DS-6.7B
|
| 18 |
|
| 19 |
### Introduction
|
| 20 |
+
AIGCodeGeek-DS-6.7B is our first released version of a Code-LLM family with competitive performance on public and private benchmarks.
|
| 21 |
|
| 22 |
### Model Details
|
| 23 |
#### Model Description
|
|
|
|
| 27 |
|
| 28 |
### Training data
|
| 29 |
A mixture of samples from high-quality open-source (read *Acknowledgements*) and our private datasets.
|
| 30 |
+
We have made contamination detection as Magicoder/Bigcode did (https://github.com/ise-uiuc/magicoder/blob/main/src/magicoder/decontamination/find_substrings.py).
|
| 31 |
|
| 32 |
### Evaluation
|
| 33 |
results to be added.
|
|
|
|
| 49 |
### QuickStart
|
| 50 |
|
| 51 |
```python
|
| 52 |
+
import torch
|
| 53 |
from transformers import AutoTokenizer, AutoModelForCausalLM
|
| 54 |
tokenizer = AutoTokenizer.from_pretrained("aigcode/AIGCodeGeek-DS-6.7B", trust_remote_code=True)
|
| 55 |
model = AutoModelForCausalLM.from_pretrained("aigcode/AIGCodeGeek-DS-6.7B", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
|
| 56 |
messages=[
|
| 57 |
+
{ 'role': 'user', 'content': "write a merge sort algorithm in python."}
|
| 58 |
]
|
| 59 |
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
|
| 60 |
# tokenizer.eos_token_id is the id of <|EOT|> token
|
|
|
|
| 62 |
print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
|
| 63 |
```
|
| 64 |
|
|
|
|
|
|
|
| 65 |
|
| 66 |
### Acknowledgements
|
| 67 |
We gain a lot of knowledge and resources from the open-source community:
|