commit ae7e10785a3a03b1c1dd04431392d72850075254 Author: ModelHub XC Date: Fri Apr 10 10:33:19 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: RichardErkhov/cnmoro_-_TinyLlama-ContextQuestionPair-Classifier-Reranker-gguf Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..edb1b29 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,57 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text 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diff=lfs merge=lfs -text +TinyLlama-ContextQuestionPair-Classifier-Reranker.Q4_K_S.gguf filter=lfs diff=lfs merge=lfs -text +TinyLlama-ContextQuestionPair-Classifier-Reranker.Q4_K.gguf filter=lfs diff=lfs merge=lfs -text +TinyLlama-ContextQuestionPair-Classifier-Reranker.Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text +TinyLlama-ContextQuestionPair-Classifier-Reranker.Q4_1.gguf filter=lfs diff=lfs merge=lfs -text +TinyLlama-ContextQuestionPair-Classifier-Reranker.Q5_0.gguf filter=lfs diff=lfs merge=lfs -text +TinyLlama-ContextQuestionPair-Classifier-Reranker.Q5_K_S.gguf filter=lfs diff=lfs merge=lfs -text +TinyLlama-ContextQuestionPair-Classifier-Reranker.Q5_K.gguf filter=lfs diff=lfs merge=lfs -text +TinyLlama-ContextQuestionPair-Classifier-Reranker.Q5_K_M.gguf filter=lfs diff=lfs merge=lfs -text +TinyLlama-ContextQuestionPair-Classifier-Reranker.Q5_1.gguf filter=lfs diff=lfs merge=lfs -text +TinyLlama-ContextQuestionPair-Classifier-Reranker.Q6_K.gguf filter=lfs diff=lfs merge=lfs -text +TinyLlama-ContextQuestionPair-Classifier-Reranker.Q8_0.gguf filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..ab6c43b --- /dev/null +++ b/README.md @@ -0,0 +1,138 @@ +--- +pipeline_tag: text-ranking +--- +Quantization made by Richard Erkhov. + +[Github](https://github.com/RichardErkhov) + +[Discord](https://discord.gg/pvy7H8DZMG) + +[Request more models](https://github.com/RichardErkhov/quant_request) + + +TinyLlama-ContextQuestionPair-Classifier-Reranker - GGUF +- Model creator: https://huggingface.co/cnmoro/ +- Original model: https://huggingface.co/cnmoro/TinyLlama-ContextQuestionPair-Classifier-Reranker/ + + +| Name | Quant method | Size | +| ---- | ---- | ---- | +| [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 | +| [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 | +| [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 | +| [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 | +| [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 | +| [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 | +| [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 | +| [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 | +| [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 | +| [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 | +| [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 | +| [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 | +| [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 | +| [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 | +| [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 | +| [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 | +| [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 | +| [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 | +| [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 | +| [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 | +| [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 | +| [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 | + + + + +Original model description: +--- +license: cc-by-nc-2.0 +language: +- en +- pt +tags: +- classification +- llama +- tinyllama +- rag +- rerank +--- +```python +template = """<|system|> +You are a chatbot who always responds in JSON format indicating if the context contains relevant information to answer the question +<|user|> +Context: +{Text} + +Question: +{Prompt} +<|assistant|> +""" + +# Output should be: + +{"relevant": true} + +# or + +{"relevant": false} +``` + +Example: +```text +<|system|> +You are a chatbot who always responds in JSON format indicating if the context contains relevant information to answer the question +<|user|> +Context: +old. NFT were observed in almost all patients over 60 years of age, but the incidence was low. +Many ubiquitin-positive small-sized granules were observed in the second and third layer of the parahippocampal gyrus of aged patients, +and the incidence rose with increasing age. On the other hand, few of these granules were in patients with Alzheimer\'s type dementia. +Granulovacuolar degeneration was examined. Many centrally-located granules were positive for ubiquitin. Based on electron microscopic +observation of these granules at several stages, the granules were thought to be a type of autophagosome. During the first stage of +granulovacuolar degeneration, electron-dense materials appeared in the cytoplasm, following which they were surrounded by smooth cytoplasm, +following which they were surrounded by smooth endoplasmic reticulum. Analytical electron microscopy disclosed that the granules contained +some aluminium. Several senile changes in the central nervous system in cadavers were examined. The pattern of extension of Alzheimer\'s +neurofibrillary tangles (NFT) and senile plaques (SP) in the olfactory bulbs of 100 specimens was examined during routine autopsy by +immunohistochemical staining. NFT were first observed in the anterior olfactory nucleus after the age of 60, and incidence rose with +increasing age. Senile plaques were found in the nucleus when there were many SP in the cerebral cortex. Of 25 non-demented amyotrophic +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 +all patients over + +Question: +What is granulovacuolar degeneration and what was its observation on electron microscopy? +<|assistant|> +{"relevant": true} +``` + +vLLM recommended request parameters: + +```python +prompt = "<|system|>\nYou are a chatbot who always responds in JSON format indicating if the context contains relevant information to answer the question\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?\n<|assistant|>\n{\"relevant\":" + +headers = { + "Accept": "text/event-stream", + "Authorization": "Bearer EMPTY" +} + +body = { + "model": model, + "prompt": [prompt], + "best_of": 5, + "max_tokens": 1, + "temperature": 0, + "top_p": 1, + "use_beam_search": True, + "top_k": -1, + "min_p": 0, + "repetition_penalty": 1, + "length_penalty": 1, + "min_tokens": 1, + "logprobs": 1 +} + +result = requests.post(base_uri, headers=headers, json=body) +result = result.json() + +boolean_response = bool(eval(json_result['choices'][0]['text'].strip().title())) 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