First model version

README.md

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+---
+language:
+- en
+tags:
+- upstage
+- llama-2
+- instruct
+- instruction
+pipeline_tag: text-generation
+---
+# SOLAR-0-70b-8bit model card
+
+This is a 8bit quantized version of [upstage/Llama-2-70b-instruct-v2](https://huggingface.co/upstage/Llama-2-70b-instruct-v2)
+
+## Model Details
+
+* **Developed by**: [Upstage](https://en.upstage.ai)
+* **Backbone Model**: [LLaMA-2](https://github.com/facebookresearch/llama/tree/main)
+* **Language(s)**: English
+* **Library**: [HuggingFace Transformers](https://github.com/huggingface/transformers)
+* **License**: Fine-tuned checkpoints is licensed under the Non-Commercial Creative Commons license ([CC BY-NC-4.0](https://creativecommons.org/licenses/by-nc/4.0/))
+* **Where to send comments**: Instructions on how to provide feedback or comments on a model can be found by opening an issue in the [Hugging Face community's model repository](https://huggingface.co/upstage/SOLAR-0-70b-8bit/discussions)
+* **Contact**: For questions and comments about the model, please email [contact@upstage.ai](mailto:contact@upstage.ai)
+
+## Dataset Details
+
+### Used Datasets
+- Orca-style dataset
+- Alpaca-style dataset
+- No other dataset was used except for the dataset mentioned above
+- No benchmark test set or the training set are used
+
+
+### Prompt Template
+```
+### System:
+{System}
+
+### User:
+{User}
+
+### Assistant:
+{Assistant}
+```
+
+## Usage
+
+- The followings are tested on A100 80GB
+- Our model can handle up to 10k+ input tokens, thanks to the `rope_scaling` option
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
+
+tokenizer = AutoTokenizer.from_pretrained("upstage/SOLAR-0-70b-8bit")
+model = AutoModelForCausalLM.from_pretrained(
+    "upstage/SOLAR-0-70b-8bit",
+    device_map="auto",
+    torch_dtype=torch.float16,
+    load_in_8bit=True,
+    rope_scaling={"type": "dynamic", "factor": 2} # allows handling of longer inputs
+)
+
+prompt = "### User:\nThomas is healthy, but he has to go to the hospital. What could be the reasons?\n\n### Assistant:\n"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+del inputs["token_type_ids"]
+streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+
+output = model.generate(**inputs, streamer=streamer, use_cache=True, max_new_tokens=float('inf'))
+output_text = tokenizer.decode(output[0], skip_special_tokens=True)
+```
+
+## Hardware and Software
+
+* **Hardware**: We utilized an A100x8 * 4 for training our model
+* **Training Factors**: We fine-tuned this model using a combination of the [DeepSpeed library](https://github.com/microsoft/DeepSpeed) and the [HuggingFace Trainer](https://huggingface.co/docs/transformers/main_classes/trainer) / [HuggingFace Accelerate](https://huggingface.co/docs/accelerate/index)
+
+## Evaluation Results
+
+### Overview
+- We conducted a performance evaluation following the tasks being evaluated on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
+We evaluated our model on four benchmark datasets, which include `ARC-Challenge`, `HellaSwag`, `MMLU`, and `TruthfulQA`
+We used the [lm-evaluation-harness repository](https://github.com/EleutherAI/lm-evaluation-harness), specifically commit [b281b0921b636bc36ad05c0b0b0763bd6dd43463](https://github.com/EleutherAI/lm-evaluation-harness/tree/b281b0921b636bc36ad05c0b0b0763bd6dd43463).
+- We used [MT-bench](https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge), a set of challenging multi-turn open-ended questions, to evaluate the models
+
+### Main Results
+| Model | H4(Avg) | ARC | HellaSwag | MMLU | TruthfulQA | | MT_Bench |
+|--------------------------------------------------------------------|----------|----------|----------|------|----------|-|-------------|
+| **[Llama-2-70b-instruct-v2](https://huggingface.co/upstage/Llama-2-70b-instruct-v2)**(***Ours***, ***Open LLM Leaderboard***) | **73** | **71.1** | **87.9** | **70.6** | **62.2** | | **7.44063** |
+| [Llama-2-70b-instruct](https://huggingface.co/upstage/Llama-2-70b-instruct) (Ours, Open LLM Leaderboard) | 72.3 | 70.9 | 87.5 | 69.8 | 61 | | 7.24375  |
+| [llama-65b-instruct](https://huggingface.co/upstage/llama-65b-instruct) (Ours, Open LLM Leaderboard) | 69.4 | 67.6 | 86.5 | 64.9 | 58.8 | | |
+| Llama-2-70b-hf | 67.3 | 67.3 | 87.3 | 69.8 | 44.9 | | |
+| [llama-30b-instruct-2048](https://huggingface.co/upstage/llama-30b-instruct-2048) (Ours, Open LLM Leaderboard) | 67.0 | 64.9 | 84.9 | 61.9 | 56.3 | | |
+| [llama-30b-instruct](https://huggingface.co/upstage/llama-30b-instruct) (Ours, Open LLM Leaderboard) | 65.2 | 62.5 | 86.2 | 59.4 | 52.8 | | |
+| llama-65b | 64.2 | 63.5 | 86.1 | 63.9 | 43.4 | | |
+| falcon-40b-instruct | 63.4 | 61.6 | 84.3 | 55.4 | 52.5 | | |
+
+### Scripts for H4 Score Reproduction
+- Prepare evaluation environments:
+```
+# clone the repository
+git clone https://github.com/EleutherAI/lm-evaluation-harness.git
+# check out the specific commit
+git checkout b281b0921b636bc36ad05c0b0b0763bd6dd43463
+# change to the repository directory
+cd lm-evaluation-harness
+```
+
+## Contact Us
+
+### About Upstage
+- [Upstage](https://en.upstage.ai) is a company specialized in Large Language Models (LLMs) and AI. We will help you build private LLMs and related applications.
+If you have a dataset to build domain specific LLMs or make LLM applications, please contact us at ► [click here to contact](https://www.upstage.ai/private-llm?utm_source=huggingface&utm_medium=link&utm_campaign=privatellm)
+- As of August 1st, our 70B model has reached the top spot in openLLM rankings, marking itself as the current leading performer globally. 

config.json

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+{
+  "_name_or_path": "/data/project/public/checkpoints/Llama-2-70b-instruct-v2",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 8192,
+  "initializer_range": 0.02,
+  "intermediate_size": 28672,
+  "max_position_embeddings": 4096,
+  "model_type": "llama",
+  "num_attention_heads": 64,
+  "num_hidden_layers": 80,
+  "num_key_value_heads": 8,
+  "pad_token_id": 0,
+  "pretraining_tp": 1,
+  "quantization_config": {
+    "bnb_4bit_compute_dtype": "float32",
+    "bnb_4bit_quant_type": "fp4",
+    "bnb_4bit_use_double_quant": false,
+    "llm_int8_enable_fp32_cpu_offload": false,
+    "llm_int8_has_fp16_weight": false,
+    "llm_int8_skip_modules": null,
+    "llm_int8_threshold": 6.0,
+    "load_in_4bit": false,
+    "load_in_8bit": true
+  },
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.31.0",
+  "use_cache": false,
+  "vocab_size": 32000
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 0,
+  "transformers_version": "4.31.0",
+  "use_cache": false
+}

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tokenizer_config.json

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+{
+  "add_bos_token": true,
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+  "bos_token": {
+    "__type": "AddedToken",
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "clean_up_tokenization_spaces": false,
+  "eos_token": {
+    "__type": "AddedToken",
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+  "legacy": false,
+  "model_max_length": 1000000000000000019884624838656,
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+  "sp_model_kwargs": {},
+  "tokenizer_class": "LlamaTokenizer",
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}