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Model: alexandreteles/bonito-v1-gguf
Source: Original Platform

.gitattributes

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README.md

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+---
+datasets:
+- BatsResearch/ctga-v1
+language:
+- en
+library_name: transformers
+pipeline_tag: text2text-generation
+tags:
+- data generation
+license: apache-2.0
+---
+
+# Bonito-v1 GGUF
+
+You can find the original model at [BatsResearch/bonito-v1](https://huggingface.co/BatsResearch/bonito-v1)
+
+## Variations
+
+| Name | Quant method | Bits |
+| ---- | ---- | ---- |
+| [bonito-v1_iq4_nl.gguf](https://huggingface.co/alexandreteles/bonito-v1-gguf/blob/main/bonito-v1_iq4_nl.gguf) | IQ4_NL | 4 | 4.16 GB|
+| [bonito-v1_q4_k_m.gguf](https://huggingface.co/alexandreteles/bonito-v1-gguf/blob/main/bonito-v1_q4_k_m.gguf) | Q4_K_M | 4 | 4.37 GB|
+| [bonito-v1_q5_k_2.gguf](https://huggingface.co/alexandreteles/bonito-v1-gguf/blob/main/bonito-v1_q5_k_s.gguf) | Q5_K_S | 5 | 5.00 GB|
+| [bonito-v1_q5_k_m.gguf](https://huggingface.co/alexandreteles/bonito-v1-gguf/blob/main/bonito-v1_q5_k_m.gguf) | Q5_K_M | 5 | 5.13 GB|
+| [bonito-v1_q6_k.gguf](https://huggingface.co/alexandreteles/bonito-v1-gguf/blob/main/bonito-v1_q6_k.gguf) | Q6_K | 6 | 5.94 GB|
+| [bonito-v1_q8_0.gguf](https://huggingface.co/alexandreteles/bonito-v1-gguf/blob/main/bonito-v1_q8_0.gguf) | Q8_0 | 8 | 7.70 GB|
+| [bonito-v1_f16.gguf](https://huggingface.co/alexandreteles/bonito-v1-gguf/blob/main/bonito-v1_f16.gguf) | FP16 | 16 | 14.5 GB|
+
+## Model Card for bonito
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+Bonito is an open-source model for conditional task generation: the task of converting unannotated text into task-specific training datasets for instruction tuning. 
+
+![Bonito](https://raw.githubusercontent.com/BatsResearch/bonito/main/assets/workflow.png)
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+Bonito can be used to create synthetic instruction tuning datasets to adapt large language models on users' specialized, private data.
+In our [paper](https://github.com/BatsResearch/bonito), we show that Bonito can be used to adapt both pretrained and instruction tuned models to tasks without any annotations.
+
+- **Developed by:** Nihal V. Nayak, Yiyang Nan, Avi Trost, and Stephen H. Bach
+- **Model type:** MistralForCausalLM
+- **Language(s) (NLP):** English
+- **License:** TBD
+- **Finetuned from model:** `mistralai/Mistral-7B-v0.1`
+
+### Model Sources
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [https://github.com/BatsResearch/bonito](https://github.com/BatsResearch/bonito)
+- **Paper:** Arxiv link
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+To easily generate synthetic instruction tuning datasets, we recommend using the [bonito](https://github.com/BatsResearch/bonito) package built using the `transformers` and the `vllm` libraries. 
+
+```python
+from bonito import Bonito, SamplingParams
+from datasets import load_dataset
+
+# Initialize the Bonito model
+bonito = Bonito()
+
+# load dataaset with unannotated text
+unannotated_text = load_dataset(
+    "BatsResearch/bonito-experiment",
+    "unannotated_contract_nli"
+)["train"].select(range(10))
+
+# Generate synthetic instruction tuning dataset
+sampling_params = SamplingParams(max_tokens=256, top_p=0.95, temperature=0.5, n=1)
+synthetic_dataset = bonito.generate_tasks(
+    unannotated_text,
+    context_col="input",
+    task_type="nli",
+    sampling_params=sampling_params
+)
+```
+
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+Our model is trained to generate the following task types: summarization, sentiment analysis, multiple-choice question answering, extractive question answering, topic classification, natural language inference, question generation, text generation, question answering without choices, paraphrase identification, sentence completion, yes-no question answering, word sense disambiguation, paraphrase generation, textual entailment, and
+coreference resolution.
+The model might not produce accurate synthetic tasks beyond these task types.

added_tokens.json

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config.json

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+{
+  "_name_or_path": "mistralai/Mistral-7B-v0.1",
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 32768,
+  "model_type": "mistral",
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+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 10000.0,
+  "sliding_window": 4096,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.34.0",
+  "use_cache": true,
+  "vocab_size": 32000
+}

generation_config.json

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+{
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+  "transformers_version": "4.34.0"
+}

special_tokens_map.json

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tokenizer_config.json

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