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ChartVLM-base/.gitattributes

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auxiliary_decoder/README.md

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+---
+library_name: peft
+---
+## Training procedure
+
+
+The following `bitsandbytes` quantization config was used during training:
+- quant_method: bitsandbytes
+- load_in_8bit: True
+- load_in_4bit: False
+- llm_int8_threshold: 6.0
+- llm_int8_skip_modules: None
+- llm_int8_enable_fp32_cpu_offload: False
+- llm_int8_has_fp16_weight: False
+- bnb_4bit_quant_type: fp4
+- bnb_4bit_use_double_quant: False
+- bnb_4bit_compute_dtype: float32
+### Framework versions
+
+
+- PEFT 0.4.0

auxiliary_decoder/adapter_config.json

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+{
+  "auto_mapping": null,
+  "base_model_name_or_path": "/cpfs01/shared/ADLab/hug_ckpts/vicuna-7b-v1.5",
+  "bias": "none",
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+  "inference_mode": true,
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+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 16,
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "v_proj"
+  ],
+  "task_type": "CAUSAL_LM"
+}

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auxiliary_decoder/base/README.md

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+---
+inference: false
+license: llama2
+---
+
+# Vicuna Model Card
+
+## Model Details
+
+Vicuna is a chat assistant trained by fine-tuning Llama 2 on user-shared conversations collected from ShareGPT.
+
+- **Developed by:** [LMSYS](https://lmsys.org/)
+- **Model type:** An auto-regressive language model based on the transformer architecture
+- **License:** Llama 2 Community License Agreement	
+- **Finetuned from model:** [Llama 2](https://arxiv.org/abs/2307.09288)
+
+### Model Sources
+
+- **Repository:** https://github.com/lm-sys/FastChat
+- **Blog:** https://lmsys.org/blog/2023-03-30-vicuna/
+- **Paper:** https://arxiv.org/abs/2306.05685
+- **Demo:** https://chat.lmsys.org/
+
+## Uses
+
+The primary use of Vicuna is research on large language models and chatbots.
+The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence.
+
+## How to Get Started with the Model
+
+- Command line interface: https://github.com/lm-sys/FastChat#vicuna-weights
+- APIs (OpenAI API, Huggingface API): https://github.com/lm-sys/FastChat/tree/main#api  
+
+## Training Details
+
+Vicuna v1.5 is fine-tuned from Llama 2 with supervised instruction fine-tuning.
+The training data is around 125K conversations collected from ShareGPT.com.
+See more details in the "Training Details of Vicuna Models" section in the appendix of this [paper](https://arxiv.org/pdf/2306.05685.pdf).
+
+## Evaluation
+
+![Evaluation Results](https://github.com/lm-sys/lm-sys.github.io/blob/main/public/images/webdata/vicuna_v1.5_eval.png?raw=true)
+
+Vicuna is evaluated with standard benchmarks, human preference, and LLM-as-a-judge. See more details in this [paper](https://arxiv.org/pdf/2306.05685.pdf) and [leaderboard](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard).
+
+## Difference between different versions of Vicuna
+
+See [vicuna_weights_version.md](https://github.com/lm-sys/FastChat/blob/main/docs/vicuna_weights_version.md)

auxiliary_decoder/base/config.json

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+  "architectures": [
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+  ],
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+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "max_position_embeddings": 4096,
+  "model_type": "llama",
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+  "num_hidden_layers": 32,
+  "num_key_value_heads": 32,
+  "pad_token_id": 0,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.31.0",
+  "use_cache": true,
+  "vocab_size": 32000
+}

auxiliary_decoder/base/generation_config.json

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auxiliary_decoder/base/special_tokens_map.json

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auxiliary_decoder/base/tokenizer.model

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