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--- |
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tags: |
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- fp8 |
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- vllm |
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pipeline_tag: text-generation |
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license: mit |
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license_link: https://huggingface.co/microsoft/Phi-3.5-mini-instruct/resolve/main/LICENSE |
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base_model: microsoft/Phi-3.5-mini-instruct |
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--- |
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# Phi-3.5-mini-instruct-FP8-KV |
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## Model Overview |
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- **Model Architecture:** Phi-3.5 |
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- **Input:** Text |
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- **Output:** Text |
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- **Model Optimizations:** |
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- **Weight quantization:** FP8 |
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- **Activation quantization:** FP8 |
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- **Intended Use Cases:** Intended for commercial and research use in English. Similarly to [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct), this models is intended for assistant-like chat. |
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- **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. |
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- **Release Date:** 8/11/2024 |
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- **Version:** 1.1 |
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- **License(s):** [mit](https://huggingface.co/microsoft/Phi-3.5-mini-instruct/resolve/main/LICENSE) |
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- **Model Developers:** Neural Magic |
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Quantized version of [Phi-3.5-mini-instruct](https://huggingface.co/microsoft/Phi-3.5-mini-instruct), with the new configuration files. |
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### Model Optimizations |
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This model was obtained by quantizing the weights and activations of [Phi-3.5-mini-instruct](https://huggingface.co/microsoft/Phi-3.5-mini-instruct) to FP8 data type, ready for inference with vLLM >= 0.5.1. |
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This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%. |
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Only the weights and activations of the linear operators within transformers blocks are quantized. Symmetric per-tensor quantization is applied, in which a single linear scaling maps the FP8 representations of the quantized weights and activations. |
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[AutoFP8](https://github.com/neuralmagic/AutoFP8) is used for quantization with 512 sequences of UltraChat. |
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## Deployment |
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### Use with vLLM |
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This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below. |
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```python |
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from vllm import LLM, SamplingParams |
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from transformers import AutoTokenizer |
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model_id = "neuralmagic/Phi-3.5-mini-instruct-FP8-KV" |
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sampling_params = SamplingParams(temperature=0.6, top_p=0.9, max_tokens=256) |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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messages = [ |
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{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, |
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{"role": "user", "content": "Who are you? Remember to respond in pirate speak!"}, |
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] |
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prompts = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
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llm = LLM(model=model_id, kv_cache_dtype="fp8") |
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outputs = llm.generate(prompts, sampling_params) |
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generated_text = outputs[0].outputs[0].text |
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print(generated_text) |
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``` |
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vLLM aslo supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details. |
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## Creation |
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This model was created by applying [LLM Compressor with calibration samples from UltraChat](https://github.com/vllm-project/llm-compressor/blob/sa/big_model_support/examples/big_model_offloading/big_model_w8a8_calibrate.py), as presented in the code snipet below. |
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```python |
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from datasets import load_dataset |
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from transformers import AutoTokenizer |
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from llmcompressor.transformers import SparseAutoModelForCausalLM, oneshot |
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# Select model and load it. |
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# Phi-3.5 is a special case for KV cache quantization because it has |
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# fused QKV linear layers. |
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MODEL_ID = "microsoft/Phi-3.5-mini-instruct" |
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model = SparseAutoModelForCausalLM.from_pretrained( |
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MODEL_ID, |
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device_map="auto", |
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torch_dtype="auto", |
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) |
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tokenizer = AutoTokenizer.from_pretrained(MODEL_ID) |
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# Select calibration dataset. |
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DATASET_ID = "HuggingFaceH4/ultrachat_200k" |
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DATASET_SPLIT = "train_sft" |
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# Select number of samples. 512 samples is a good place to start. |
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# Increasing the number of samples can improve accuracy. |
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NUM_CALIBRATION_SAMPLES = 512 |
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MAX_SEQUENCE_LENGTH = 2048 |
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# Load dataset and preprocess. |
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ds = load_dataset(DATASET_ID, split=DATASET_SPLIT) |
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ds = ds.shuffle(seed=42).select(range(NUM_CALIBRATION_SAMPLES)) |
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def process_and_tokenize(example): |
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text = tokenizer.apply_chat_template(example["messages"], tokenize=False) |
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return tokenizer( |
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text, |
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padding=False, |
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max_length=MAX_SEQUENCE_LENGTH, |
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truncation=True, |
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add_special_tokens=False, |
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) |
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ds = ds.map(process_and_tokenize, remove_columns=ds.column_names) |
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# Configure the quantization algorithm and scheme. |
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# In this case, we: |
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# * quantize the weights to fp8 with per-tensor scales |
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# * quantize the activations to fp8 with per-tensor scales |
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# * quantize the kv cache to fp8 with per-tensor scales |
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recipe = """ |
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quant_stage: |
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quant_modifiers: |
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QuantizationModifier: |
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ignore: ["lm_head"] |
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config_groups: |
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group_0: |
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weights: |
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num_bits: 8 |
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type: float |
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strategy: tensor |
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dynamic: false |
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symmetric: true |
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input_activations: |
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num_bits: 8 |
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type: float |
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strategy: tensor |
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dynamic: false |
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symmetric: true |
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targets: ["Linear"] |
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kv_cache_scheme: |
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num_bits: 8 |
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type: float |
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strategy: tensor |
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dynamic: false |
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symmetric: true |
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""" |
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# Apply algorithms. |
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oneshot( |
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model=model, |
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dataset=ds, |
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recipe=recipe, |
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max_seq_length=MAX_SEQUENCE_LENGTH, |
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num_calibration_samples=NUM_CALIBRATION_SAMPLES, |
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) |
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# Confirm generations of the quantized model look sane. |
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print("\n\n") |
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print("========== SAMPLE GENERATION ==============") |
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input_ids = tokenizer("Hello my name is", return_tensors="pt").input_ids.to("cuda") |
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output = model.generate(input_ids, max_new_tokens=100) |
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print(tokenizer.decode(output[0])) |
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print("==========================================\n\n") |
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# Save to disk compressed. |
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SAVE_DIR = MODEL_ID.split("/")[1] + "-FP8-KV" |
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model.save_pretrained(SAVE_DIR, save_compressed=True) |
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tokenizer.save_pretrained(SAVE_DIR) |
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``` |
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## Evaluation |
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The model was evaluated on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) leaderboard tasks (version 1) with the [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/tree/383bbd54bc621086e05aa1b030d8d4d5635b25e6) (commit 383bbd54bc621086e05aa1b030d8d4d5635b25e6) and the [vLLM](https://docs.vllm.ai/en/stable/) engine, using the following command: |
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``` |
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lm_eval \ |
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--model vllm \ |
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--model_args pretrained="neuralmagic/Phi-3.5-mini-instruct-FP8-KV",kv_cache_dtype="fp8",gpu_memory_utilization=0.4,add_bos_token=True,max_model_len=4096 \ |
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--tasks openllm \ |
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--batch_size auto |
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``` |
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### Accuracy |
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#### Open LLM Leaderboard evaluation scores |
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<table> |
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<tr> |
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<td><strong>Benchmark</strong> |
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</td> |
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<td><strong>Phi-3.5-mini-instruct</strong> |
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</td> |
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<td><strong>Phi-3.5-mini-instruct-FP8-KV(this model)</strong> |
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</td> |
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<td><strong>Recovery</strong> |
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</td> |
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</tr> |
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<tr> |
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<td>MMLU (5-shot) |
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</td> |
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<td>68.81 |
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</td> |
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<td>68.56 |
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</td> |
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<td>99.64% |
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</td> |
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</tr> |
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<tr> |
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<td>ARC Challenge (25-shot, acc_norm) |
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</td> |
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<td>64.68 |
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</td> |
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<td>64.51 |
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</td> |
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<td>99.74% |
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</td> |
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</tr> |
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<tr> |
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<td>GSM-8K (5-shot, strict-match) |
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</td> |
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<td>78.24 |
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</td> |
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<td>77.26 |
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</td> |
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<td>98.75% |
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</td> |
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</tr> |
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<tr> |
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<td>Hellaswag (10-shot, acc_norm) |
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</td> |
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<td>79.03 |
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</td> |
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<td>78.88 |
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</td> |
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<td>99.81% |
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</td> |
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</tr> |
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<tr> |
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<td>Winogrande (5-shot, acc) |
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</td> |
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<td>73.40 |
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</td> |
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<td>73.80 |
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</td> |
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<td>100.5% |
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</td> |
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</tr> |
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<tr> |
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<td>TruthfulQA (0-shot, mc2) |
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</td> |
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<td>56.39 |
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</td> |
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<td>56.95 |
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</td> |
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<td>100.9% |
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</td> |
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</tr> |
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<tr> |
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<td><strong>Average</strong> |
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</td> |
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<td><strong>70.09</strong> |
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</td> |
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<td><strong>70.00</strong> |
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</td> |
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<td><strong>99.89%</strong> |
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</td> |
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</tr> |
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</table> |
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