Hugging Face's logo

RichardErkhov
/
cnmoro_-_TinyLlama-ContextQuestionPair-Classifier-Reranker-gguf

Text Ranking
GGUF
conversational
Model card Files
xet
 Community
1
RichardErkhov commited on
Commit
2d0a592
·
verified ·
1 Parent(s): 6ea157e

uploaded readme

Browse files
Files changed (1) hide show
  1. README.md +135 -0
README.md ADDED
@@ -0,0 +1,135 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ Quantization made by Richard Erkhov.
2
+
3
+ [Github](https://github.com/RichardErkhov)
4
+
5
+ [Discord](https://discord.gg/pvy7H8DZMG)
6
+
7
+ [Request more models](https://github.com/RichardErkhov/quant_request)
8
+
9
+
10
+ TinyLlama-ContextQuestionPair-Classifier-Reranker - GGUF
11
+ - Model creator: https://huggingface.co/cnmoro/
12
+ - Original model: https://huggingface.co/cnmoro/TinyLlama-ContextQuestionPair-Classifier-Reranker/
13
+
14
+
15
+ | Name | Quant method | Size |
16
+ | ---- | ---- | ---- |
17
+ | [TinyLlama-ContextQuestionPair-Classifier-Reranker.Q2_K.gguf](https://huggingface.co/RichardErkhov/cnmoro_-_TinyLlama-ContextQuestionPair-Classifier-Reranker-gguf/blob/main/TinyLlama-ContextQuestionPair-Classifier-Reranker.Q2_K.gguf) | Q2_K | 0.4GB |
18
+ | [TinyLlama-ContextQuestionPair-Classifier-Reranker.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/cnmoro_-_TinyLlama-ContextQuestionPair-Classifier-Reranker-gguf/blob/main/TinyLlama-ContextQuestionPair-Classifier-Reranker.IQ3_XS.gguf) | IQ3_XS | 0.44GB |
19
+ | [TinyLlama-ContextQuestionPair-Classifier-Reranker.IQ3_S.gguf](https://huggingface.co/RichardErkhov/cnmoro_-_TinyLlama-ContextQuestionPair-Classifier-Reranker-gguf/blob/main/TinyLlama-ContextQuestionPair-Classifier-Reranker.IQ3_S.gguf) | IQ3_S | 0.47GB |
20
+ | [TinyLlama-ContextQuestionPair-Classifier-Reranker.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/cnmoro_-_TinyLlama-ContextQuestionPair-Classifier-Reranker-gguf/blob/main/TinyLlama-ContextQuestionPair-Classifier-Reranker.Q3_K_S.gguf) | Q3_K_S | 0.47GB |
21
+ | [TinyLlama-ContextQuestionPair-Classifier-Reranker.IQ3_M.gguf](https://huggingface.co/RichardErkhov/cnmoro_-_TinyLlama-ContextQuestionPair-Classifier-Reranker-gguf/blob/main/TinyLlama-ContextQuestionPair-Classifier-Reranker.IQ3_M.gguf) | IQ3_M | 0.48GB |
22
+ | [TinyLlama-ContextQuestionPair-Classifier-Reranker.Q3_K.gguf](https://huggingface.co/RichardErkhov/cnmoro_-_TinyLlama-ContextQuestionPair-Classifier-Reranker-gguf/blob/main/TinyLlama-ContextQuestionPair-Classifier-Reranker.Q3_K.gguf) | Q3_K | 0.51GB |
23
+ | [TinyLlama-ContextQuestionPair-Classifier-Reranker.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/cnmoro_-_TinyLlama-ContextQuestionPair-Classifier-Reranker-gguf/blob/main/TinyLlama-ContextQuestionPair-Classifier-Reranker.Q3_K_M.gguf) | Q3_K_M | 0.51GB |
24
+ | [TinyLlama-ContextQuestionPair-Classifier-Reranker.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/cnmoro_-_TinyLlama-ContextQuestionPair-Classifier-Reranker-gguf/blob/main/TinyLlama-ContextQuestionPair-Classifier-Reranker.Q3_K_L.gguf) | Q3_K_L | 0.55GB |
25
+ | [TinyLlama-ContextQuestionPair-Classifier-Reranker.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/cnmoro_-_TinyLlama-ContextQuestionPair-Classifier-Reranker-gguf/blob/main/TinyLlama-ContextQuestionPair-Classifier-Reranker.IQ4_XS.gguf) | IQ4_XS | 0.57GB |
26
+ | [TinyLlama-ContextQuestionPair-Classifier-Reranker.Q4_0.gguf](https://huggingface.co/RichardErkhov/cnmoro_-_TinyLlama-ContextQuestionPair-Classifier-Reranker-gguf/blob/main/TinyLlama-ContextQuestionPair-Classifier-Reranker.Q4_0.gguf) | Q4_0 | 0.59GB |
27
+ | [TinyLlama-ContextQuestionPair-Classifier-Reranker.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/cnmoro_-_TinyLlama-ContextQuestionPair-Classifier-Reranker-gguf/blob/main/TinyLlama-ContextQuestionPair-Classifier-Reranker.IQ4_NL.gguf) | IQ4_NL | 0.6GB |
28
+ | [TinyLlama-ContextQuestionPair-Classifier-Reranker.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/cnmoro_-_TinyLlama-ContextQuestionPair-Classifier-Reranker-gguf/blob/main/TinyLlama-ContextQuestionPair-Classifier-Reranker.Q4_K_S.gguf) | Q4_K_S | 0.6GB |
29
+ | [TinyLlama-ContextQuestionPair-Classifier-Reranker.Q4_K.gguf](https://huggingface.co/RichardErkhov/cnmoro_-_TinyLlama-ContextQuestionPair-Classifier-Reranker-gguf/blob/main/TinyLlama-ContextQuestionPair-Classifier-Reranker.Q4_K.gguf) | Q4_K | 0.62GB |
30
+ | [TinyLlama-ContextQuestionPair-Classifier-Reranker.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/cnmoro_-_TinyLlama-ContextQuestionPair-Classifier-Reranker-gguf/blob/main/TinyLlama-ContextQuestionPair-Classifier-Reranker.Q4_K_M.gguf) | Q4_K_M | 0.62GB |
31
+ | [TinyLlama-ContextQuestionPair-Classifier-Reranker.Q4_1.gguf](https://huggingface.co/RichardErkhov/cnmoro_-_TinyLlama-ContextQuestionPair-Classifier-Reranker-gguf/blob/main/TinyLlama-ContextQuestionPair-Classifier-Reranker.Q4_1.gguf) | Q4_1 | 0.65GB |
32
+ | [TinyLlama-ContextQuestionPair-Classifier-Reranker.Q5_0.gguf](https://huggingface.co/RichardErkhov/cnmoro_-_TinyLlama-ContextQuestionPair-Classifier-Reranker-gguf/blob/main/TinyLlama-ContextQuestionPair-Classifier-Reranker.Q5_0.gguf) | Q5_0 | 0.71GB |
33
+ | [TinyLlama-ContextQuestionPair-Classifier-Reranker.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/cnmoro_-_TinyLlama-ContextQuestionPair-Classifier-Reranker-gguf/blob/main/TinyLlama-ContextQuestionPair-Classifier-Reranker.Q5_K_S.gguf) | Q5_K_S | 0.71GB |
34
+ | [TinyLlama-ContextQuestionPair-Classifier-Reranker.Q5_K.gguf](https://huggingface.co/RichardErkhov/cnmoro_-_TinyLlama-ContextQuestionPair-Classifier-Reranker-gguf/blob/main/TinyLlama-ContextQuestionPair-Classifier-Reranker.Q5_K.gguf) | Q5_K | 0.73GB |
35
+ | [TinyLlama-ContextQuestionPair-Classifier-Reranker.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/cnmoro_-_TinyLlama-ContextQuestionPair-Classifier-Reranker-gguf/blob/main/TinyLlama-ContextQuestionPair-Classifier-Reranker.Q5_K_M.gguf) | Q5_K_M | 0.73GB |
36
+ | [TinyLlama-ContextQuestionPair-Classifier-Reranker.Q5_1.gguf](https://huggingface.co/RichardErkhov/cnmoro_-_TinyLlama-ContextQuestionPair-Classifier-Reranker-gguf/blob/main/TinyLlama-ContextQuestionPair-Classifier-Reranker.Q5_1.gguf) | Q5_1 | 0.77GB |
37
+ | [TinyLlama-ContextQuestionPair-Classifier-Reranker.Q6_K.gguf](https://huggingface.co/RichardErkhov/cnmoro_-_TinyLlama-ContextQuestionPair-Classifier-Reranker-gguf/blob/main/TinyLlama-ContextQuestionPair-Classifier-Reranker.Q6_K.gguf) | Q6_K | 0.84GB |
38
+ | [TinyLlama-ContextQuestionPair-Classifier-Reranker.Q8_0.gguf](https://huggingface.co/RichardErkhov/cnmoro_-_TinyLlama-ContextQuestionPair-Classifier-Reranker-gguf/blob/main/TinyLlama-ContextQuestionPair-Classifier-Reranker.Q8_0.gguf) | Q8_0 | 1.09GB |
39
+
40
+
41
+
42
+
43
+ Original model description:
44
+ ---
45
+ license: cc-by-nc-2.0
46
+ language:
47
+ - en
48
+ - pt
49
+ tags:
50
+ - classification
51
+ - llama
52
+ - tinyllama
53
+ - rag
54
+ - rerank
55
+ ---
56
+ ```python
57
+ template = """<s><|system|>
58
+ You are a chatbot who always responds in JSON format indicating if the context contains relevant information to answer the question</s>
59
+ <|user|>
60
+ Context:
61
+ {Text}
62
+
63
+ Question:
64
+ {Prompt}</s>
65
+ <|assistant|>
66
+ """
67
+
68
+ # Output should be:
69
+
70
+ {"relevant": true}
71
+
72
+ # or
73
+
74
+ {"relevant": false}
75
+ ```
76
+
77
+ Example:
78
+ ```text
79
+ <s><|system|>
80
+ You are a chatbot who always responds in JSON format indicating if the context contains relevant information to answer the question</s>
81
+ <|user|>
82
+ Context:
83
+ old. NFT were observed in almost all patients over 60 years of age, but the incidence was low.
84
+ Many ubiquitin-positive small-sized granules were observed in the second and third layer of the parahippocampal gyrus of aged patients,
85
+ and the incidence rose with increasing age. On the other hand, few of these granules were in patients with Alzheimer\'s type dementia.
86
+ Granulovacuolar degeneration was examined. Many centrally-located granules were positive for ubiquitin. Based on electron microscopic
87
+ observation of these granules at several stages, the granules were thought to be a type of autophagosome. During the first stage of
88
+ granulovacuolar degeneration, electron-dense materials appeared in the cytoplasm, following which they were surrounded by smooth cytoplasm,
89
+ following which they were surrounded by smooth endoplasmic reticulum. Analytical electron microscopy disclosed that the granules contained
90
+ some aluminium. Several senile changes in the central nervous system in cadavers were examined. The pattern of extension of Alzheimer\'s
91
+ neurofibrillary tangles (NFT) and senile plaques (SP) in the olfactory bulbs of 100 specimens was examined during routine autopsy by
92
+ immunohistochemical staining. NFT were first observed in the anterior olfactory nucleus after the age of 60, and incidence rose with
93
+ increasing age. Senile plaques were found in the nucleus when there were many SP in the cerebral cortex. Of 25 non-demented amyotrophic
94
+ lateral sclerosis patients, SP were found in the cerebral cortices of 10, and 9 of 10 were over 60 years old. NFT were observed in almost
95
+ all patients over
96
+
97
+ Question:
98
+ What is granulovacuolar degeneration and what was its observation on electron microscopy?</s>
99
+ <|assistant|>
100
+ {"relevant": true}</s>
101
+ ```
102
+
103
+ vLLM recommended request parameters:
104
+
105
+ ```python
106
+ prompt = "<s><|system|>\nYou are a chatbot who always responds in JSON format indicating if the context contains relevant information to answer the question</s>\n<|user|>\nContext:\nConhecida como missão de imagem de raios-x e espectroscopia (da sigla em inglês XRISM), a estratégia é utilizar o telescópio para ampliar os estudos da humanidade a níveis celestiais com uma fração dos pixels da tela de um Gameboy original, lançado em 1989. Isso é possível por meio de uma ferramenta chamada “Resolve”. Apesar de utilizar a medição em pixels, a tecnologia é bastante diferente de uma câmera. Com um conjunto de microcalorímetros de seis pixels quadrados que mede 0,5 cm², ela detecta a temperatura de cada raio-x que o atinge. Como funciona o Resolve do telescópio XRISM? Cientista do projeto XRISM da NASA, Brian Williams explicou em um comunicado o funcionamento do telescópio. “Chamamos o Resolve de espectrômetro de microcalorímetros porque cada um de seus 36 pixels está medindo pequenas quantidades de calor entregues por cada raio-x recebido, nos permitindo ver as impressões digitais químicas dos elementos que compõem as fontes com detalhes sem precedentes”.\n\nQuestion:\nQual é a sigla em alemão mencionada?</s>\n<|assistant|>\n{\"relevant\":"
107
+
108
+ headers = {
109
+ "Accept": "text/event-stream",
110
+ "Authorization": "Bearer EMPTY"
111
+ }
112
+
113
+ body = {
114
+ "model": model,
115
+ "prompt": [prompt],
116
+ "best_of": 5,
117
+ "max_tokens": 1,
118
+ "temperature": 0,
119
+ "top_p": 1,
120
+ "use_beam_search": True,
121
+ "top_k": -1,
122
+ "min_p": 0,
123
+ "repetition_penalty": 1,
124
+ "length_penalty": 1,
125
+ "min_tokens": 1,
126
+ "logprobs": 1
127
+ }
128
+
129
+ result = requests.post(base_uri, headers=headers, json=body)
130
+ result = result.json()
131
+
132
+ boolean_response = bool(eval(json_result['choices'][0]['text'].strip().title()))
133
+ print(boolean_response)
134
+ ```
135
+