Locutusque commited on
Commit
836b887
1 Parent(s): a2cb81d

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +62 -3
README.md CHANGED
@@ -1,3 +1,62 @@
1
- ---
2
- license: apache-2.0
3
- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: apache-2.0
3
+ datasets:
4
+ - Locutusque/hercules-v5.0
5
+ language:
6
+ - en
7
+ ---
8
+
9
+
10
+ # Orca-2.0-Tau-1.8B
11
+
12
+ <!-- Provide a quick summary of what the model is/does. -->
13
+ We fine-tuned qwen2-1.5B on a high quality mix for general-purpose assistants. A DPO version of this will be released soon. We use the ChatML prompt format.
14
+
15
+
16
+ ## Model Details
17
+
18
+ ### Model Description
19
+
20
+ <!-- Provide a longer summary of what this model is. -->
21
+
22
+ This model has capabilities in math, coding, writing, and more. We fine-tuned it using a high quality mix for general-purpose assistants.
23
+
24
+ - **Developed by:** M4-ai
25
+ - **Language(s) (NLP):** English and maybe Chinese
26
+ - **License:** apache-2.0
27
+ - **Finetuned from model:** [qwen2-1.5B](https://huggingface.co/Qwen/Qwen2-1.5B)
28
+
29
+ ## Uses
30
+
31
+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
32
+
33
+ General purpose assistant, question answering, chain-of-thought, etc..
34
+
35
+ ### Recommendations
36
+
37
+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
38
+
39
+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
40
+
41
+ ## Evaluation
42
+ Coming soon
43
+
44
+
45
+ ## Training Details
46
+
47
+ ### Training Data
48
+
49
+ - Locutusque/hercules-v5.0
50
+
51
+ ## Evaluations
52
+
53
+ coming soon
54
+
55
+ #### Training Hyperparameters
56
+
57
+ - **Training regime:** bf16 non-mixed precision
58
+ ## Technical Specifications
59
+
60
+ #### Hardware
61
+
62
+ We used 8 Kaggle TPUs, and we trained at a global batch size of 256 and sequence length of 1536.