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LLaMA-Mesh-rk3588-1.1.1

Text Generation
Transformers
Safetensors
llama
mesh-generation
conversational
text-generation-inference
Inference Endpoints
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  1. .gitattributes +24 -0
  2. LICENSE_LLAMA +32 -0
  3. LISENCE +39 -0
  4. LLaMA-Mesh-rk3588-w8a8-opt-0-hybrid-ratio-0.0.rkllm +3 -0
  5. LLaMA-Mesh-rk3588-w8a8-opt-0-hybrid-ratio-0.5.rkllm +3 -0
  6. LLaMA-Mesh-rk3588-w8a8-opt-0-hybrid-ratio-1.0.rkllm +3 -0
  7. LLaMA-Mesh-rk3588-w8a8-opt-1-hybrid-ratio-0.0.rkllm +3 -0
  8. LLaMA-Mesh-rk3588-w8a8-opt-1-hybrid-ratio-0.5.rkllm +3 -0
  9. LLaMA-Mesh-rk3588-w8a8-opt-1-hybrid-ratio-1.0.rkllm +3 -0
  10. LLaMA-Mesh-rk3588-w8a8_g128-opt-0-hybrid-ratio-0.0.rkllm +3 -0
  11. LLaMA-Mesh-rk3588-w8a8_g128-opt-0-hybrid-ratio-0.5.rkllm +3 -0
  12. LLaMA-Mesh-rk3588-w8a8_g128-opt-0-hybrid-ratio-1.0.rkllm +3 -0
  13. LLaMA-Mesh-rk3588-w8a8_g128-opt-1-hybrid-ratio-0.0.rkllm +3 -0
  14. LLaMA-Mesh-rk3588-w8a8_g128-opt-1-hybrid-ratio-0.5.rkllm +3 -0
  15. LLaMA-Mesh-rk3588-w8a8_g128-opt-1-hybrid-ratio-1.0.rkllm +3 -0
  16. LLaMA-Mesh-rk3588-w8a8_g256-opt-0-hybrid-ratio-0.0.rkllm +3 -0
  17. LLaMA-Mesh-rk3588-w8a8_g256-opt-0-hybrid-ratio-0.5.rkllm +3 -0
  18. LLaMA-Mesh-rk3588-w8a8_g256-opt-0-hybrid-ratio-1.0.rkllm +3 -0
  19. LLaMA-Mesh-rk3588-w8a8_g256-opt-1-hybrid-ratio-0.0.rkllm +3 -0
  20. LLaMA-Mesh-rk3588-w8a8_g256-opt-1-hybrid-ratio-0.5.rkllm +3 -0
  21. LLaMA-Mesh-rk3588-w8a8_g256-opt-1-hybrid-ratio-1.0.rkllm +3 -0
  22. LLaMA-Mesh-rk3588-w8a8_g512-opt-0-hybrid-ratio-0.0.rkllm +3 -0
  23. LLaMA-Mesh-rk3588-w8a8_g512-opt-0-hybrid-ratio-0.5.rkllm +3 -0
  24. LLaMA-Mesh-rk3588-w8a8_g512-opt-0-hybrid-ratio-1.0.rkllm +3 -0
  25. LLaMA-Mesh-rk3588-w8a8_g512-opt-1-hybrid-ratio-0.0.rkllm +3 -0
  26. LLaMA-Mesh-rk3588-w8a8_g512-opt-1-hybrid-ratio-0.5.rkllm +3 -0
  27. LLaMA-Mesh-rk3588-w8a8_g512-opt-1-hybrid-ratio-1.0.rkllm +3 -0
  28. README.md +137 -0
  29. config.json +38 -0
  30. generation_config.json +12 -0
  31. model.safetensors.index.json +298 -0
  32. special_tokens_map.json +23 -0
  33. tokenizer.json +0 -0
  34. tokenizer_config.json +2065 -0
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LICENSE_LLAMA ADDED
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LISENCE ADDED
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+ Note: This model incorporates Llama 3.1 technology, which is provided under the Llama 3.1 Community License Agreement. See `LLAMA_LICENSE.txt` for details. Attribution: "Built with Llama" as required.
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README.md ADDED
@@ -0,0 +1,137 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ library_name: transformers
3
+ license: llama3.1
4
+ pipeline_tag: text-generation
5
+ tags:
6
+ - mesh-generation
7
+ ---
8
+ # LLaMA-Mesh-RK3588-1.1.1
9
+
10
+ This version of LLaMA-Mesh has been converted to run on the RK3588 NPU using ['w8a8', 'w8a8_g128', 'w8a8_g256', 'w8a8_g512'] quantization.
11
+ This model has been optimized with the following LoRA:
12
+
13
+ Compatible with RKLLM version: 1.1.1
14
+
15
+ ## Useful links:
16
+ [Official RKLLM GitHub](https://github.com/airockchip/rknn-llm)
17
+
18
+ [RockhipNPU Reddit](https://reddit.com/r/RockchipNPU)
19
+
20
+ [EZRKNN-LLM](https://github.com/Pelochus/ezrknn-llm/)
21
+
22
+ Pretty much anything by these folks: [marty1885](https://github.com/marty1885) and [happyme531](https://huggingface.co/happyme531)
23
+
24
+ Converted using https://github.com/c0zaut/ez-er-rkllm-toolkit
25
+
26
+ # Original Model Card for base model, LLaMA-Mesh, below:
27
+
28
+
29
+ # LLaMA-Mesh: Unifying 3D Mesh Generation with Language Models
30
+
31
+ [**Paper**](https://arxiv.org/pdf/2411.09595) | [**Project Page**](https://research.nvidia.com/labs/toronto-ai/LLaMA-Mesh/)
32
+
33
+ Pre-trained model weights of LLaMA-Mesh: Unifying 3D Mesh Generation with Language Models
34
+
35
+
36
+ [Zhengyi Wang](https://thuwzy.github.io/), [Jonathan Lorraine](https://www.jonlorraine.com/), [Yikai Wang](https://yikaiw.github.io/), [Hang Su](https://www.suhangss.me/), [Jun Zhu](https://ml.cs.tsinghua.edu.cn/~jun/index.shtml), [Sanja Fidler](https://www.cs.utoronto.ca/~fidler/), [Xiaohui Zeng](https://www.cs.utoronto.ca/~xiaohui/)
37
+
38
+
39
+ Abstract: *This work explores expanding the capabilities of large language models (LLMs) pretrained on text to generate 3D meshes within a unified model. This offers key advantages of (1) leveraging spatial knowledge already embedded in LLMs, derived from textual sources like 3D tutorials, and (2) enabling conversational 3D generation and mesh understanding. A primary challenge is effectively tokenizing 3D mesh data into discrete tokens that LLMs can process seamlessly. To address this, we introduce LLaMA-Mesh, a novel approach that represents the vertex coordinates and face definitions of 3D meshes as plain text, allowing direct integration with LLMs without expanding the vocabulary. We construct a supervised fine-tuning (SFT) dataset enabling pretrained LLMs to (1) generate 3D meshes from text prompts, (2) produce interleaved text and 3D mesh outputs as required, and (3) understand and interpret 3D meshes. Our work is the first to demonstrate that LLMs can be fine-tuned to acquire complex spatial knowledge for 3D mesh generation in a text-based format, effectively unifying the 3D and text modalities. LLaMA-Mesh achieves mesh generation quality on par with models trained from scratch while maintaining strong text generation performance.*
40
+
41
+
42
+ <video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/634e15aec1ce28f1de91c470/CwSCmyJizQderIYC8CaJ4.mp4"></video>
43
+
44
+ ## Method
45
+
46
+ Overview of our method. LLaMA-Mesh unifies text and 3D mesh in a uniform format by representing the numerical values of vertex coordinates and face definitions of a 3D mesh as plain text. Our model is trained using text and 3D interleaved data in an end-to-end manner. Therefore, our model can generate both text and 3D meshes in a unified model.
47
+
48
+ ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/634e15aec1ce28f1de91c470/0DzHXhoxonG5ZMeTysA6s.jpeg)
49
+
50
+ ### Model Developer: Base model weight is from Meta. Finetuned by Nvidia
51
+
52
+ ## Third-Party Community Consideration:
53
+ This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see link to Non-NVIDIA [Llama 3.1 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md).
54
+
55
+ ## License/Terms of Use:
56
+ This model, Llama-Mesh, is distributed under the following licenses:
57
+ 1. NSCLv1 License
58
+ The Llama-Mesh model is licensed under the NSCLv1 license, which allows non-commercial use only. For details, please refer to the LICENSE.txt file.
59
+ 2. Llama 3.1 Community License Agreement
60
+ This model incorporates components of Llama 3.1 technology, which is licensed under the Llama 3.1 Community License Agreement. Redistribution and use of Llama 3.1 materials must comply with the terms of this agreement. See the LLAMA_LICENSE.txt file for full details.
61
+
62
+ ## Attribution
63
+ This model is built with Llama 3.1 technology, as required by the Llama 3.1 Community License Agreement. The required attribution is: "Built with Llama".
64
+
65
+ ## Reference(s):
66
+ Llama 3.1 [Github](https://github.com/meta-llama/llama-models/tree/main/models/llama3_1)
67
+
68
+ ## Model Architecture:
69
+ **Architecture Type:** Transformer
70
+ *Network Architecture:* Llama 3.1
71
+
72
+ ## Input:
73
+ **Input Type(s):** Text
74
+
75
+ **Input Format(s):** String
76
+
77
+ **Input Parameters:** 1D
78
+
79
+ **Other Properties Related to Input:** Max token length 8k
80
+
81
+ ## Output:
82
+ **Output Type(s):** Text
83
+
84
+ **Output Format:** String
85
+
86
+ **Output Parameters:** 1D
87
+
88
+ **Other Properties Related to Output:** Max token length 8k
89
+
90
+ **Supported Hardware Microarchitecture Compatibility:**
91
+ * NVIDIA Ada
92
+
93
+ **Supported Operating System(s):**
94
+ * Linux
95
+
96
+ ## Model Version(s):
97
+ Llama 3.1 8B mesh
98
+
99
+ # Training Dataset:
100
+ Please refer to [Llama 3.1 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md) for information on Training, Testing, and Evaluation Datasets).
101
+
102
+ The data is curated through converting Objaverse mesh data into text string (in the format as vertex index, face index as string). The model is finetuned on the curated dataset with 32 GPU.
103
+
104
+ [**Objaverse**](https://objaverse.allenai.org/explore/)
105
+
106
+ **Data Collection Method by dataset**: Unknown
107
+
108
+ **Labeling Method by dataset**: Unknown
109
+
110
+ **Properties:** We use 30k mesh data, which is a subset from the Objaverse. We filter the Objaverse dataset by the number of faces, and only keep the shape with the number of faces less than 500. They are saved as obj file format.
111
+
112
+ **Dataset License(s):** The use of the dataset as a whole is licensed under the ODC-By v1.0 license.
113
+
114
+
115
+ ## Inference:
116
+ **Engine**: Pytorch
117
+
118
+ **Test Hardware**: A100
119
+
120
+ ## Ethical Considerations:
121
+ NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
122
+
123
+ Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
124
+
125
+ ## BibTeX
126
+
127
+ ```bibtex
128
+ @misc{wang2024llamameshunifying3dmesh,
129
+ title={LLaMA-Mesh: Unifying 3D Mesh Generation with Language Models},
130
+ author={Zhengyi Wang and Jonathan Lorraine and Yikai Wang and Hang Su and Jun Zhu and Sanja Fidler and Xiaohui Zeng},
131
+ year={2024},
132
+ eprint={2411.09595},
133
+ archivePrefix={arXiv},
134
+ primaryClass={cs.LG},
135
+ url={https://arxiv.org/abs/2411.09595},
136
+ }
137
+ ```
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+ },
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+ },
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1772
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1778
+ },
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1780
+ "content": "<|reserved_special_token_214|>",
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1782
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1783
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1784
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+ },
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1788
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+ "lstrip": false,
1790
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+ },
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1796
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+ },
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1812
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1817
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1818
+ },
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1820
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1823
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1824
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1825
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1826
+ },
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1828
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1829
+ "lstrip": false,
1830
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1831
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1832
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1833
+ "special": true
1834
+ },
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1836
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1838
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1839
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1841
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+ },
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1844
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+ "lstrip": false,
1846
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1847
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1848
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1849
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1850
+ },
1851
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1852
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1853
+ "lstrip": false,
1854
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1855
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1856
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+ },
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1860
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1863
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1864
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1865
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1871
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+ },
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+ },
1883
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1884
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+ "lstrip": false,
1886
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1887
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1888
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1889
+ "special": true
1890
+ },
1891
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1892
+ "content": "<|reserved_special_token_228|>",
1893
+ "lstrip": false,
1894
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1895
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1896
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1897
+ "special": true
1898
+ },
1899
+ "128237": {
1900
+ "content": "<|reserved_special_token_229|>",
1901
+ "lstrip": false,
1902
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1903
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1904
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1905
+ "special": true
1906
+ },
1907
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1908
+ "content": "<|reserved_special_token_230|>",
1909
+ "lstrip": false,
1910
+ "normalized": false,
1911
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1912
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1913
+ "special": true
1914
+ },
1915
+ "128239": {
1916
+ "content": "<|reserved_special_token_231|>",
1917
+ "lstrip": false,
1918
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1919
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1920
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1921
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+ },
1923
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1924
+ "content": "<|reserved_special_token_232|>",
1925
+ "lstrip": false,
1926
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1927
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1928
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1929
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1930
+ },
1931
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1932
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1933
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1934
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1935
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1936
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1937
+ "special": true
1938
+ },
1939
+ "128242": {
1940
+ "content": "<|reserved_special_token_234|>",
1941
+ "lstrip": false,
1942
+ "normalized": false,
1943
+ "rstrip": false,
1944
+ "single_word": false,
1945
+ "special": true
1946
+ },
1947
+ "128243": {
1948
+ "content": "<|reserved_special_token_235|>",
1949
+ "lstrip": false,
1950
+ "normalized": false,
1951
+ "rstrip": false,
1952
+ "single_word": false,
1953
+ "special": true
1954
+ },
1955
+ "128244": {
1956
+ "content": "<|reserved_special_token_236|>",
1957
+ "lstrip": false,
1958
+ "normalized": false,
1959
+ "rstrip": false,
1960
+ "single_word": false,
1961
+ "special": true
1962
+ },
1963
+ "128245": {
1964
+ "content": "<|reserved_special_token_237|>",
1965
+ "lstrip": false,
1966
+ "normalized": false,
1967
+ "rstrip": false,
1968
+ "single_word": false,
1969
+ "special": true
1970
+ },
1971
+ "128246": {
1972
+ "content": "<|reserved_special_token_238|>",
1973
+ "lstrip": false,
1974
+ "normalized": false,
1975
+ "rstrip": false,
1976
+ "single_word": false,
1977
+ "special": true
1978
+ },
1979
+ "128247": {
1980
+ "content": "<|reserved_special_token_239|>",
1981
+ "lstrip": false,
1982
+ "normalized": false,
1983
+ "rstrip": false,
1984
+ "single_word": false,
1985
+ "special": true
1986
+ },
1987
+ "128248": {
1988
+ "content": "<|reserved_special_token_240|>",
1989
+ "lstrip": false,
1990
+ "normalized": false,
1991
+ "rstrip": false,
1992
+ "single_word": false,
1993
+ "special": true
1994
+ },
1995
+ "128249": {
1996
+ "content": "<|reserved_special_token_241|>",
1997
+ "lstrip": false,
1998
+ "normalized": false,
1999
+ "rstrip": false,
2000
+ "single_word": false,
2001
+ "special": true
2002
+ },
2003
+ "128250": {
2004
+ "content": "<|reserved_special_token_242|>",
2005
+ "lstrip": false,
2006
+ "normalized": false,
2007
+ "rstrip": false,
2008
+ "single_word": false,
2009
+ "special": true
2010
+ },
2011
+ "128251": {
2012
+ "content": "<|reserved_special_token_243|>",
2013
+ "lstrip": false,
2014
+ "normalized": false,
2015
+ "rstrip": false,
2016
+ "single_word": false,
2017
+ "special": true
2018
+ },
2019
+ "128252": {
2020
+ "content": "<|reserved_special_token_244|>",
2021
+ "lstrip": false,
2022
+ "normalized": false,
2023
+ "rstrip": false,
2024
+ "single_word": false,
2025
+ "special": true
2026
+ },
2027
+ "128253": {
2028
+ "content": "<|reserved_special_token_245|>",
2029
+ "lstrip": false,
2030
+ "normalized": false,
2031
+ "rstrip": false,
2032
+ "single_word": false,
2033
+ "special": true
2034
+ },
2035
+ "128254": {
2036
+ "content": "<|reserved_special_token_246|>",
2037
+ "lstrip": false,
2038
+ "normalized": false,
2039
+ "rstrip": false,
2040
+ "single_word": false,
2041
+ "special": true
2042
+ },
2043
+ "128255": {
2044
+ "content": "<|reserved_special_token_247|>",
2045
+ "lstrip": false,
2046
+ "normalized": false,
2047
+ "rstrip": false,
2048
+ "single_word": false,
2049
+ "special": true
2050
+ }
2051
+ },
2052
+ "bos_token": "<|begin_of_text|>",
2053
+ "chat_template": "{{ '<|begin_of_text|>' }}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% endif %}{% if system_message is defined %}{{ '<|start_header_id|>system<|end_header_id|>\n\n' + system_message + '<|eot_id|>' }}{% endif %}{% for message in loop_messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|start_header_id|>user<|end_header_id|>\n\n' + content + '<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n' }}{% elif message['role'] == 'assistant' %}{{ content + '<|eot_id|>' }}{% endif %}{% endfor %}",
2054
+ "clean_up_tokenization_spaces": true,
2055
+ "eos_token": "<|eot_id|>",
2056
+ "model_input_names": [
2057
+ "input_ids",
2058
+ "attention_mask"
2059
+ ],
2060
+ "model_max_length": 131072,
2061
+ "pad_token": "<|eot_id|>",
2062
+ "padding_side": "right",
2063
+ "split_special_tokens": false,
2064
+ "tokenizer_class": "PreTrainedTokenizerFast"
2065
+ }