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Model: nm-testing/SmolLM-1.7B-Instruct-quantized.w4a16 Source: Original Platform
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---
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language:
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- en
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pipeline_tag: text-generation
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license: apache-2.0
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---
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# SmolLM-135M-Instruct-quantized.w4a16
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## Model Overview
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- **Model Architecture:** SmolLM-135M-Instruct
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- **Input:** Text
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- **Output:** Text
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- **Model Optimizations:**
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- **Weight quantization:** INT4
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- **Intended Use Cases:** Intended for commercial and research use in English. Similarly to [SmolLM-135M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM-135M), 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:** 8/23/2024
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- **Version:** 1.0
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- **License(s)**: [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)
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- **Model Developers:** Neural Magic
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Quantized version of [SmolLM-135M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM-135M).
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It achieves an average score of 31.91 on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark (version 1), whereas the unquantized model achieves 31.55.
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### Model Optimizations
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This model was obtained by quantizing the weights of [SmolLM-135M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM-135M) to INT4 data type.
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This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 75%.
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Only the weights of the linear operators within transformers blocks are quantized. Symmetric group-wise quantization is applied, in which a linear scaling per group maps the INT4 and floating point representations of the quantized weights.
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The [GPTQ](https://arxiv.org/abs/2210.17323) algorithm is applied for quantization, as implemented in the [llm-compressor](https://github.com/vllm-project/llm-compressor) library. Quantization is performed with 10% damping factor, group-size as 64 and 512 sequences sampled from [LLM Compression Calibration](https://huggingface.co/datasets/neuralmagic/LLM_compression_calibration).
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## Creation
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This model was created by using the [llm-compressor](https://github.com/vllm-project/llm-compressor) library as presented in the code snipet below.
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```python
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from transformers import AutoTokenizer
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from llmcompressor.transformers import SparseAutoModelForCausalLM, oneshot
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from llmcompressor.modifiers.quantization import GPTQModifier
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from compressed_tensors.quantization import QuantizationArgs, QuantizationType, QuantizationStrategy
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from datasets import load_dataset
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import random
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model_id = "HuggingFaceTB/SmolLM-135M-Instruct"
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num_samples = 512
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max_seq_len = 4096
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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preprocess_fn = lambda example: {"text": "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n{text}".format_map(example)}
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dataset_name = "neuralmagic/LLM_compression_calibration"
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dataset = load_dataset(dataset_name, split="train")
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ds = dataset.shuffle().select(range(num_samples))
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ds = ds.map(preprocess_fn)
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examples = [
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tokenizer(
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example["text"], padding=False, max_length=max_seq_len, truncation=True,
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) for example in ds
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]
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# recipe = "w4a16_nohead_recipe.yaml"
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recipe = GPTQModifier(
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targets="Linear",
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scheme="W4A16",
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ignore=["lm_head"],
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dampening_frac=0.1,
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)
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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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trust_remote_code=True
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)
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print(model)
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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_seq_len,
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num_calibration_samples=num_samples,
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oneshot_device="cuda:1,2,3",
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)
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model_name = model_id.split("/")[-1]
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model.save_pretrained(f"{model_name}-quantized.w4a16")
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tokenizer.save_pretrained(f"{model_name}-quantized.w4a16")
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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 [sparseML](https://github.com/neuralmagic/sparseml) engine, using the following command:
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```
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lm_eval \
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--model sparseml \
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--model_args pretrained=nm-testing/SmolLM-1.7B-Instruct-quantized.w4a16,dtype=bfloat16,max_legth=2048,add_bos_token=True,parallelize=True \
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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>SmolLM-135M-Instruct </strong>
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</td>
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<td><strong>SmolLM-135M-Instruct-quantized.w4a16(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>26.220
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</td>
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<td>25.202
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</td>
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<td>96.12%
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</td>
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</tr>
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<tr>
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<td>ARC Challenge (25-shot)
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</td>
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<td>29.948
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</td>
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<td>30.034
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</td>
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<td>100.29%
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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>1.289
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</td>
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<td>1.971
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</td>
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<td>152.91%
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</td>
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</tr>
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<tr>
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<td>Hellaswag (10-shot)
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</td>
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<td>41.41
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</td>
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<td>40.81
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</td>
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<td>98.55%
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</td>
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</tr>
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<tr>
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<td>Winogrande (5-shot)
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</td>
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<td>50.039
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</td>
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<td>53.591
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</td>
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<td>107.10%
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</td>
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</tr>
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<tr>
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<td>TruthfulQA (0-shot)
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</td>
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<td>40.38
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</td>
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<td>39.87
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</td>
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<td>98.74%
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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>31.55</strong>
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</td>
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<td><strong>31.91</strong>
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</td>
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<td><strong>101.16%</strong>
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</td>
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</tr>
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</table>
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{
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"results": {
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"arc_challenge": {
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"alias": "arc_challenge",
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"acc,none": 0.4052901023890785,
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"acc_stderr,none": 0.014346869060229328,
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"acc_norm,none": 0.4308873720136519,
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"acc_norm_stderr,none": 0.014471133392642471
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}
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},
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"group_subtasks": {
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"arc_challenge": []
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},
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"configs": {
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"arc_challenge": {
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"task": "arc_challenge",
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"tag": [
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"ai2_arc"
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],
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"dataset_path": "allenai/ai2_arc",
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"dataset_name": "ARC-Challenge",
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"training_split": "train",
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"validation_split": "validation",
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"test_split": "test",
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"doc_to_text": "Question: {{question}}\nAnswer:",
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"doc_to_target": "{{choices.label.index(answerKey)}}",
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"doc_to_choice": "{{choices.text}}",
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"description": "",
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"target_delimiter": " ",
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"fewshot_delimiter": "\n\n",
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"num_fewshot": 25,
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"metric_list": [
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{
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"metric": "acc",
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"aggregation": "mean",
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"higher_is_better": true
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},
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{
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"metric": "acc_norm",
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"aggregation": "mean",
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"higher_is_better": true
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}
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],
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"output_type": "multiple_choice",
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"repeats": 1,
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"should_decontaminate": true,
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"doc_to_decontamination_query": "Question: {{question}}\nAnswer:",
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"metadata": {
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"version": 1.0
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}
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}
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},
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"versions": {
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"arc_challenge": 1.0
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},
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"n-shot": {
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"arc_challenge": 25
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},
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"higher_is_better": {
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"arc_challenge": {
|
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"acc": true,
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"acc_norm": true
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}
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},
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"n-samples": {
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"arc_challenge": {
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|
"original": 1172,
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"effective": 1172
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}
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},
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"config": {
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"model": "sparseml",
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"model_args": "pretrained=/nm/drive0/shashata/quantized_models/SmolLM-1.7B-Instruct-quantized.w4a16,dtype=bfloat16,max_legth=2048,add_bos_token=True,parallelize=True",
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"model_num_parameters": 1761708032,
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|
"model_dtype": "torch.bfloat16",
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|
"model_revision": "main",
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|
"model_sha": "",
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|
"batch_size": "32",
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|
"batch_sizes": [],
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"device": null,
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|
"use_cache": null,
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"limit": null,
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|
"bootstrap_iters": 100000,
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"gen_kwargs": null,
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"random_seed": 0,
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"numpy_seed": 1234,
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"torch_seed": 1234,
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"fewshot_seed": 1234
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},
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"git_hash": "4e55a1dd",
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"date": 1724307292.2729464,
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||||||
|
"pretty_env_info": "PyTorch version: 2.4.0+cu121\nIs debug build: False\nCUDA used to build PyTorch: 12.1\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 22.04.3 LTS (x86_64)\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version: Could not collect\nCMake version: version 3.29.3\nLibc version: glibc-2.35\n\nPython version: 3.11.9 | packaged by conda-forge | (main, Apr 19 2024, 18:36:13) [GCC 12.3.0] (64-bit runtime)\nPython platform: Linux-5.15.0-91-generic-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 12.3.103\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA A100-SXM4-80GB\nGPU 1: NVIDIA A100-SXM4-80GB\nGPU 2: NVIDIA A100-SXM4-80GB\nGPU 3: NVIDIA A100-SXM4-80GB\nGPU 4: NVIDIA A100-SXM4-80GB\nGPU 5: NVIDIA A100-SXM4-80GB\nGPU 6: NVIDIA A100-SXM4-80GB\nGPU 7: NVIDIA A100-SXM4-80GB\n\nNvidia driver version: 545.23.08\ncuDNN version: Could not collect\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 48 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 256\nOn-line CPU(s) list: 0-255\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 7763 64-Core Processor\nCPU family: 25\nModel: 1\nThread(s) per core: 2\nCore(s) per socket: 64\nSocket(s): 2\nStepping: 1\nFrequency boost: enabled\nCPU max MHz: 3529.0520\nCPU min MHz: 1500.0000\nBogoMIPS: 4900.20\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 invpcid_single hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload vgif v_spec_ctrl umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca fsrm\nVirtualization: AMD-V\nL1d cache: 4 MiB (128 instances)\nL1i cache: 4 MiB (128 instances)\nL2 cache: 64 MiB (128 instances)\nL3 cache: 512 MiB (16 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-63,128-191\nNUMA node1 CPU(s): 64-127,192-255\nVulnerability Gather data sampling: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Mitigation; safe RET\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Retpolines, IBPB conditional, IBRS_FW, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\n\nVersions of relevant libraries:\n[pip3] numpy==1.26.4\n[pip3] onnx==1.14.1\n[pip3] onnxruntime==1.18.1\n[pip3] torch==2.4.0\n[pip3] triton==3.0.0\n[conda] Could not collect",
|
||||||
|
"transformers_version": "4.43.4",
|
||||||
|
"upper_git_hash": null,
|
||||||
|
"tokenizer_pad_token": [
|
||||||
|
"<|im_end|>",
|
||||||
|
"2"
|
||||||
|
],
|
||||||
|
"tokenizer_eos_token": [
|
||||||
|
"<|im_end|>",
|
||||||
|
"2"
|
||||||
|
],
|
||||||
|
"tokenizer_bos_token": [
|
||||||
|
"<|im_start|>",
|
||||||
|
"1"
|
||||||
|
],
|
||||||
|
"eot_token_id": 2,
|
||||||
|
"max_length": 2048,
|
||||||
|
"task_hashes": {},
|
||||||
|
"model_source": "sparseml",
|
||||||
|
"model_name": "/nm/drive0/shashata/quantized_models/SmolLM-1.7B-Instruct-quantized.w4a16",
|
||||||
|
"model_name_sanitized": "__nm__drive0__shashata__quantized_models__SmolLM-1.7B-Instruct-quantized.w4a16",
|
||||||
|
"system_instruction": null,
|
||||||
|
"system_instruction_sha": null,
|
||||||
|
"fewshot_as_multiturn": false,
|
||||||
|
"chat_template": null,
|
||||||
|
"chat_template_sha": null,
|
||||||
|
"start_time": 1877804.138447339,
|
||||||
|
"end_time": 1878171.137486313,
|
||||||
|
"total_evaluation_time_seconds": "366.999038974056"
|
||||||
|
}
|
||||||
66
config.json
Normal file
66
config.json
Normal file
@@ -0,0 +1,66 @@
|
|||||||
|
{
|
||||||
|
"_name_or_path": "/home/shashata/.cache/huggingface/hub/models--HuggingFaceTB--SmolLM-1.7B-Instruct/snapshots/69f49d9c36434d6a3f319dadc5bd3b812752b98b",
|
||||||
|
"architectures": [
|
||||||
|
"LlamaForCausalLM"
|
||||||
|
],
|
||||||
|
"attention_bias": false,
|
||||||
|
"attention_dropout": 0.0,
|
||||||
|
"bos_token_id": 1,
|
||||||
|
"eos_token_id": 2,
|
||||||
|
"hidden_act": "silu",
|
||||||
|
"hidden_size": 2048,
|
||||||
|
"initializer_range": 0.02,
|
||||||
|
"intermediate_size": 8192,
|
||||||
|
"max_position_embeddings": 2048,
|
||||||
|
"mlp_bias": false,
|
||||||
|
"model_type": "llama",
|
||||||
|
"num_attention_heads": 32,
|
||||||
|
"num_hidden_layers": 24,
|
||||||
|
"num_key_value_heads": 32,
|
||||||
|
"pad_token_id": 2,
|
||||||
|
"pretraining_tp": 1,
|
||||||
|
"rms_norm_eps": 1e-05,
|
||||||
|
"rope_scaling": null,
|
||||||
|
"rope_theta": 10000.0,
|
||||||
|
"tie_word_embeddings": true,
|
||||||
|
"torch_dtype": "float32",
|
||||||
|
"transformers_version": "4.43.4",
|
||||||
|
"use_cache": true,
|
||||||
|
"vocab_size": 49152,
|
||||||
|
"quantization_config": {
|
||||||
|
"config_groups": {
|
||||||
|
"group_0": {
|
||||||
|
"input_activations": null,
|
||||||
|
"output_activations": null,
|
||||||
|
"targets": [
|
||||||
|
"Linear"
|
||||||
|
],
|
||||||
|
"weights": {
|
||||||
|
"block_structure": null,
|
||||||
|
"dynamic": false,
|
||||||
|
"group_size": 64,
|
||||||
|
"num_bits": 4,
|
||||||
|
"observer": "minmax",
|
||||||
|
"observer_kwargs": {},
|
||||||
|
"strategy": "group",
|
||||||
|
"symmetric": true,
|
||||||
|
"type": "int"
|
||||||
|
}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"format": "pack-quantized",
|
||||||
|
"global_compression_ratio": 2.210720905879975,
|
||||||
|
"ignore": [
|
||||||
|
"lm_head"
|
||||||
|
],
|
||||||
|
"kv_cache_scheme": null,
|
||||||
|
"quant_method": "compressed-tensors",
|
||||||
|
"quantization_status": "compressed",
|
||||||
|
"sparsity_config": {
|
||||||
|
"format": "dense",
|
||||||
|
"global_sparsity": 13.012990367407104,
|
||||||
|
"registry_requires_subclass": false,
|
||||||
|
"sparsity_structure": "unstructured"
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
1
configuration.json
Normal file
1
configuration.json
Normal file
@@ -0,0 +1 @@
|
|||||||
|
{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
|
||||||
8
generation_config.json
Normal file
8
generation_config.json
Normal file
@@ -0,0 +1,8 @@
|
|||||||
|
{
|
||||||
|
"_from_model_config": true,
|
||||||
|
"bos_token_id": 1,
|
||||||
|
"eos_token_id": 2,
|
||||||
|
"max_new_tokens": 40,
|
||||||
|
"pad_token_id": 2,
|
||||||
|
"transformers_version": "4.43.4"
|
||||||
|
}
|
||||||
@@ -0,0 +1,157 @@
|
|||||||
|
{
|
||||||
|
"results": {
|
||||||
|
"gsm8k": {
|
||||||
|
"alias": "gsm8k",
|
||||||
|
"exact_match,strict-match": 0.009097801364670205,
|
||||||
|
"exact_match_stderr,strict-match": 0.0026153265107756725,
|
||||||
|
"exact_match,flexible-extract": 0.026535253980288095,
|
||||||
|
"exact_match_stderr,flexible-extract": 0.004427045987265161
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"group_subtasks": {
|
||||||
|
"gsm8k": []
|
||||||
|
},
|
||||||
|
"configs": {
|
||||||
|
"gsm8k": {
|
||||||
|
"task": "gsm8k",
|
||||||
|
"tag": [
|
||||||
|
"math_word_problems"
|
||||||
|
],
|
||||||
|
"dataset_path": "gsm8k",
|
||||||
|
"dataset_name": "main",
|
||||||
|
"training_split": "train",
|
||||||
|
"test_split": "test",
|
||||||
|
"fewshot_split": "train",
|
||||||
|
"doc_to_text": "Question: {{question}}\nAnswer:",
|
||||||
|
"doc_to_target": "{{answer}}",
|
||||||
|
"description": "",
|
||||||
|
"target_delimiter": " ",
|
||||||
|
"fewshot_delimiter": "\n\n",
|
||||||
|
"num_fewshot": 5,
|
||||||
|
"metric_list": [
|
||||||
|
{
|
||||||
|
"metric": "exact_match",
|
||||||
|
"aggregation": "mean",
|
||||||
|
"higher_is_better": true,
|
||||||
|
"ignore_case": true,
|
||||||
|
"ignore_punctuation": false,
|
||||||
|
"regexes_to_ignore": [
|
||||||
|
",",
|
||||||
|
"\\$",
|
||||||
|
"(?s).*#### ",
|
||||||
|
"\\.$"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"output_type": "generate_until",
|
||||||
|
"generation_kwargs": {
|
||||||
|
"until": [
|
||||||
|
"Question:",
|
||||||
|
"</s>",
|
||||||
|
"<|im_end|>"
|
||||||
|
],
|
||||||
|
"do_sample": false,
|
||||||
|
"temperature": 0.0
|
||||||
|
},
|
||||||
|
"repeats": 1,
|
||||||
|
"filter_list": [
|
||||||
|
{
|
||||||
|
"name": "strict-match",
|
||||||
|
"filter": [
|
||||||
|
{
|
||||||
|
"function": "regex",
|
||||||
|
"regex_pattern": "#### (\\-?[0-9\\.\\,]+)"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"function": "take_first"
|
||||||
|
}
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "flexible-extract",
|
||||||
|
"filter": [
|
||||||
|
{
|
||||||
|
"function": "regex",
|
||||||
|
"group_select": -1,
|
||||||
|
"regex_pattern": "(-?[$0-9.,]{2,})|(-?[0-9]+)"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"function": "take_first"
|
||||||
|
}
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"should_decontaminate": false,
|
||||||
|
"metadata": {
|
||||||
|
"version": 3.0
|
||||||
|
}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"versions": {
|
||||||
|
"gsm8k": 3.0
|
||||||
|
},
|
||||||
|
"n-shot": {
|
||||||
|
"gsm8k": 5
|
||||||
|
},
|
||||||
|
"higher_is_better": {
|
||||||
|
"gsm8k": {
|
||||||
|
"exact_match": true
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"n-samples": {
|
||||||
|
"gsm8k": {
|
||||||
|
"original": 1319,
|
||||||
|
"effective": 1319
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"config": {
|
||||||
|
"model": "sparseml",
|
||||||
|
"model_args": "pretrained=/nm/drive0/shashata/quantized_models/SmolLM-1.7B-Instruct-quantized.w4a16,dtype=bfloat16,max_legth=2048,add_bos_token=True,parallelize=True",
|
||||||
|
"model_num_parameters": 1761708032,
|
||||||
|
"model_dtype": "torch.bfloat16",
|
||||||
|
"model_revision": "main",
|
||||||
|
"model_sha": "",
|
||||||
|
"batch_size": "32",
|
||||||
|
"batch_sizes": [],
|
||||||
|
"device": null,
|
||||||
|
"use_cache": null,
|
||||||
|
"limit": null,
|
||||||
|
"bootstrap_iters": 100000,
|
||||||
|
"gen_kwargs": null,
|
||||||
|
"random_seed": 0,
|
||||||
|
"numpy_seed": 1234,
|
||||||
|
"torch_seed": 1234,
|
||||||
|
"fewshot_seed": 1234
|
||||||
|
},
|
||||||
|
"git_hash": "4e55a1dd",
|
||||||
|
"date": 1724304019.784002,
|
||||||
|
"pretty_env_info": "PyTorch version: 2.4.0+cu121\nIs debug build: False\nCUDA used to build PyTorch: 12.1\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 22.04.3 LTS (x86_64)\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version: Could not collect\nCMake version: version 3.29.3\nLibc version: glibc-2.35\n\nPython version: 3.11.9 | packaged by conda-forge | (main, Apr 19 2024, 18:36:13) [GCC 12.3.0] (64-bit runtime)\nPython platform: Linux-5.15.0-91-generic-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 12.3.103\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA A100-SXM4-80GB\nGPU 1: NVIDIA A100-SXM4-80GB\nGPU 2: NVIDIA A100-SXM4-80GB\nGPU 3: NVIDIA A100-SXM4-80GB\nGPU 4: NVIDIA A100-SXM4-80GB\nGPU 5: NVIDIA A100-SXM4-80GB\nGPU 6: NVIDIA A100-SXM4-80GB\nGPU 7: NVIDIA A100-SXM4-80GB\n\nNvidia driver version: 545.23.08\ncuDNN version: Could not collect\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 48 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 256\nOn-line CPU(s) list: 0-255\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 7763 64-Core Processor\nCPU family: 25\nModel: 1\nThread(s) per core: 2\nCore(s) per socket: 64\nSocket(s): 2\nStepping: 1\nFrequency boost: enabled\nCPU max MHz: 3529.0520\nCPU min MHz: 1500.0000\nBogoMIPS: 4900.20\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 invpcid_single hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload vgif v_spec_ctrl umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca fsrm\nVirtualization: AMD-V\nL1d cache: 4 MiB (128 instances)\nL1i cache: 4 MiB (128 instances)\nL2 cache: 64 MiB (128 instances)\nL3 cache: 512 MiB (16 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-63,128-191\nNUMA node1 CPU(s): 64-127,192-255\nVulnerability Gather data sampling: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Mitigation; safe RET\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Retpolines, IBPB conditional, IBRS_FW, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\n\nVersions of relevant libraries:\n[pip3] numpy==1.26.4\n[pip3] onnx==1.14.1\n[pip3] onnxruntime==1.18.1\n[pip3] torch==2.4.0\n[pip3] triton==3.0.0\n[conda] Could not collect",
|
||||||
|
"transformers_version": "4.43.4",
|
||||||
|
"upper_git_hash": null,
|
||||||
|
"tokenizer_pad_token": [
|
||||||
|
"<|im_end|>",
|
||||||
|
"2"
|
||||||
|
],
|
||||||
|
"tokenizer_eos_token": [
|
||||||
|
"<|im_end|>",
|
||||||
|
"2"
|
||||||
|
],
|
||||||
|
"tokenizer_bos_token": [
|
||||||
|
"<|im_start|>",
|
||||||
|
"1"
|
||||||
|
],
|
||||||
|
"eot_token_id": 2,
|
||||||
|
"max_length": 2048,
|
||||||
|
"task_hashes": {},
|
||||||
|
"model_source": "sparseml",
|
||||||
|
"model_name": "/nm/drive0/shashata/quantized_models/SmolLM-1.7B-Instruct-quantized.w4a16",
|
||||||
|
"model_name_sanitized": "__nm__drive0__shashata__quantized_models__SmolLM-1.7B-Instruct-quantized.w4a16",
|
||||||
|
"system_instruction": null,
|
||||||
|
"system_instruction_sha": null,
|
||||||
|
"fewshot_as_multiturn": false,
|
||||||
|
"chat_template": null,
|
||||||
|
"chat_template_sha": null,
|
||||||
|
"start_time": 1874531.693266633,
|
||||||
|
"end_time": 1876713.303188979,
|
||||||
|
"total_evaluation_time_seconds": "2181.6099223459605"
|
||||||
|
}
|
||||||
@@ -0,0 +1,122 @@
|
|||||||
|
{
|
||||||
|
"results": {
|
||||||
|
"hellaswag": {
|
||||||
|
"alias": "hellaswag",
|
||||||
|
"acc,none": 0.4623580959968134,
|
||||||
|
"acc_stderr,none": 0.004975621147406101,
|
||||||
|
"acc_norm,none": 0.6117307309300936,
|
||||||
|
"acc_norm_stderr,none": 0.004863603638367428
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"group_subtasks": {
|
||||||
|
"hellaswag": []
|
||||||
|
},
|
||||||
|
"configs": {
|
||||||
|
"hellaswag": {
|
||||||
|
"task": "hellaswag",
|
||||||
|
"tag": [
|
||||||
|
"multiple_choice"
|
||||||
|
],
|
||||||
|
"dataset_path": "hellaswag",
|
||||||
|
"dataset_kwargs": {
|
||||||
|
"trust_remote_code": true
|
||||||
|
},
|
||||||
|
"training_split": "train",
|
||||||
|
"validation_split": "validation",
|
||||||
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc):\n ctx = doc[\"ctx_a\"] + \" \" + doc[\"ctx_b\"].capitalize()\n out_doc = {\n \"query\": preprocess(doc[\"activity_label\"] + \": \" + ctx),\n \"choices\": [preprocess(ending) for ending in doc[\"endings\"]],\n \"gold\": int(doc[\"label\"]),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
|
||||||
|
"doc_to_text": "{{query}}",
|
||||||
|
"doc_to_target": "{{label}}",
|
||||||
|
"doc_to_choice": "choices",
|
||||||
|
"description": "",
|
||||||
|
"target_delimiter": " ",
|
||||||
|
"fewshot_delimiter": "\n\n",
|
||||||
|
"num_fewshot": 10,
|
||||||
|
"metric_list": [
|
||||||
|
{
|
||||||
|
"metric": "acc",
|
||||||
|
"aggregation": "mean",
|
||||||
|
"higher_is_better": true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"metric": "acc_norm",
|
||||||
|
"aggregation": "mean",
|
||||||
|
"higher_is_better": true
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"output_type": "multiple_choice",
|
||||||
|
"repeats": 1,
|
||||||
|
"should_decontaminate": false,
|
||||||
|
"metadata": {
|
||||||
|
"version": 1.0
|
||||||
|
}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"versions": {
|
||||||
|
"hellaswag": 1.0
|
||||||
|
},
|
||||||
|
"n-shot": {
|
||||||
|
"hellaswag": 10
|
||||||
|
},
|
||||||
|
"higher_is_better": {
|
||||||
|
"hellaswag": {
|
||||||
|
"acc": true,
|
||||||
|
"acc_norm": true
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"n-samples": {
|
||||||
|
"hellaswag": {
|
||||||
|
"original": 10042,
|
||||||
|
"effective": 10042
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"config": {
|
||||||
|
"model": "sparseml",
|
||||||
|
"model_args": "pretrained=/nm/drive0/shashata/quantized_models/SmolLM-1.7B-Instruct-quantized.w4a16,dtype=bfloat16,max_legth=2048,add_bos_token=True,parallelize=True",
|
||||||
|
"model_num_parameters": 1761708032,
|
||||||
|
"model_dtype": "torch.bfloat16",
|
||||||
|
"model_revision": "main",
|
||||||
|
"model_sha": "",
|
||||||
|
"batch_size": "32",
|
||||||
|
"batch_sizes": [],
|
||||||
|
"device": null,
|
||||||
|
"use_cache": null,
|
||||||
|
"limit": null,
|
||||||
|
"bootstrap_iters": 100000,
|
||||||
|
"gen_kwargs": null,
|
||||||
|
"random_seed": 0,
|
||||||
|
"numpy_seed": 1234,
|
||||||
|
"torch_seed": 1234,
|
||||||
|
"fewshot_seed": 1234
|
||||||
|
},
|
||||||
|
"git_hash": "4e55a1dd",
|
||||||
|
"date": 1724307665.0567887,
|
||||||
|
"pretty_env_info": "PyTorch version: 2.4.0+cu121\nIs debug build: False\nCUDA used to build PyTorch: 12.1\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 22.04.3 LTS (x86_64)\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version: Could not collect\nCMake version: version 3.29.3\nLibc version: glibc-2.35\n\nPython version: 3.11.9 | packaged by conda-forge | (main, Apr 19 2024, 18:36:13) [GCC 12.3.0] (64-bit runtime)\nPython platform: Linux-5.15.0-91-generic-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 12.3.103\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA A100-SXM4-80GB\nGPU 1: NVIDIA A100-SXM4-80GB\nGPU 2: NVIDIA A100-SXM4-80GB\nGPU 3: NVIDIA A100-SXM4-80GB\nGPU 4: NVIDIA A100-SXM4-80GB\nGPU 5: NVIDIA A100-SXM4-80GB\nGPU 6: NVIDIA A100-SXM4-80GB\nGPU 7: NVIDIA A100-SXM4-80GB\n\nNvidia driver version: 545.23.08\ncuDNN version: Could not collect\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 48 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 256\nOn-line CPU(s) list: 0-255\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 7763 64-Core Processor\nCPU family: 25\nModel: 1\nThread(s) per core: 2\nCore(s) per socket: 64\nSocket(s): 2\nStepping: 1\nFrequency boost: enabled\nCPU max MHz: 3529.0520\nCPU min MHz: 1500.0000\nBogoMIPS: 4900.20\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 invpcid_single hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload vgif v_spec_ctrl umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca fsrm\nVirtualization: AMD-V\nL1d cache: 4 MiB (128 instances)\nL1i cache: 4 MiB (128 instances)\nL2 cache: 64 MiB (128 instances)\nL3 cache: 512 MiB (16 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-63,128-191\nNUMA node1 CPU(s): 64-127,192-255\nVulnerability Gather data sampling: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Mitigation; safe RET\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Retpolines, IBPB conditional, IBRS_FW, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\n\nVersions of relevant libraries:\n[pip3] numpy==1.26.4\n[pip3] onnx==1.14.1\n[pip3] onnxruntime==1.18.1\n[pip3] torch==2.4.0\n[pip3] triton==3.0.0\n[conda] Could not collect",
|
||||||
|
"transformers_version": "4.43.4",
|
||||||
|
"upper_git_hash": null,
|
||||||
|
"tokenizer_pad_token": [
|
||||||
|
"<|im_end|>",
|
||||||
|
"2"
|
||||||
|
],
|
||||||
|
"tokenizer_eos_token": [
|
||||||
|
"<|im_end|>",
|
||||||
|
"2"
|
||||||
|
],
|
||||||
|
"tokenizer_bos_token": [
|
||||||
|
"<|im_start|>",
|
||||||
|
"1"
|
||||||
|
],
|
||||||
|
"eot_token_id": 2,
|
||||||
|
"max_length": 2048,
|
||||||
|
"task_hashes": {},
|
||||||
|
"model_source": "sparseml",
|
||||||
|
"model_name": "/nm/drive0/shashata/quantized_models/SmolLM-1.7B-Instruct-quantized.w4a16",
|
||||||
|
"model_name_sanitized": "__nm__drive0__shashata__quantized_models__SmolLM-1.7B-Instruct-quantized.w4a16",
|
||||||
|
"system_instruction": null,
|
||||||
|
"system_instruction_sha": null,
|
||||||
|
"fewshot_as_multiturn": false,
|
||||||
|
"chat_template": null,
|
||||||
|
"chat_template_sha": null,
|
||||||
|
"start_time": 1878176.91101329,
|
||||||
|
"end_time": 1880694.26163463,
|
||||||
|
"total_evaluation_time_seconds": "2517.350621339865"
|
||||||
|
}
|
||||||
48901
merges.txt
Normal file
48901
merges.txt
Normal file
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
3
model.safetensors
Normal file
3
model.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:089fa78f90189534d1be0b4038c04f1f2203875760b9c7159d3d261e7f2e0238
|
||||||
|
size 1711742704
|
||||||
34
special_tokens_map.json
Normal file
34
special_tokens_map.json
Normal file
@@ -0,0 +1,34 @@
|
|||||||
|
{
|
||||||
|
"additional_special_tokens": [
|
||||||
|
"<|im_start|>",
|
||||||
|
"<|im_end|>"
|
||||||
|
],
|
||||||
|
"bos_token": {
|
||||||
|
"content": "<|im_start|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
"eos_token": {
|
||||||
|
"content": "<|im_end|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
"pad_token": {
|
||||||
|
"content": "<|im_end|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
"unk_token": {
|
||||||
|
"content": "<|endoftext|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
}
|
||||||
|
}
|
||||||
98254
tokenizer.json
Normal file
98254
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
154
tokenizer_config.json
Normal file
154
tokenizer_config.json
Normal file
@@ -0,0 +1,154 @@
|
|||||||
|
{
|
||||||
|
"add_prefix_space": false,
|
||||||
|
"added_tokens_decoder": {
|
||||||
|
"0": {
|
||||||
|
"content": "<|endoftext|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"1": {
|
||||||
|
"content": "<|im_start|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"2": {
|
||||||
|
"content": "<|im_end|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"3": {
|
||||||
|
"content": "<repo_name>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"4": {
|
||||||
|
"content": "<reponame>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"5": {
|
||||||
|
"content": "<file_sep>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"6": {
|
||||||
|
"content": "<filename>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"7": {
|
||||||
|
"content": "<gh_stars>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"8": {
|
||||||
|
"content": "<issue_start>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"9": {
|
||||||
|
"content": "<issue_comment>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"10": {
|
||||||
|
"content": "<issue_closed>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"11": {
|
||||||
|
"content": "<jupyter_start>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"12": {
|
||||||
|
"content": "<jupyter_text>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"13": {
|
||||||
|
"content": "<jupyter_code>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"14": {
|
||||||
|
"content": "<jupyter_output>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"15": {
|
||||||
|
"content": "<jupyter_script>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"16": {
|
||||||
|
"content": "<empty_output>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"additional_special_tokens": [
|
||||||
|
"<|im_start|>",
|
||||||
|
"<|im_end|>"
|
||||||
|
],
|
||||||
|
"bos_token": "<|im_start|>",
|
||||||
|
"chat_template": "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
|
||||||
|
"clean_up_tokenization_spaces": false,
|
||||||
|
"eos_token": "<|im_end|>",
|
||||||
|
"model_max_length": 2048,
|
||||||
|
"pad_token": "<|im_end|>",
|
||||||
|
"tokenizer_class": "GPT2Tokenizer",
|
||||||
|
"unk_token": "<|endoftext|>",
|
||||||
|
"vocab_size": 49152
|
||||||
|
}
|
||||||
@@ -0,0 +1,297 @@
|
|||||||
|
{
|
||||||
|
"results": {
|
||||||
|
"truthfulqa_gen": {
|
||||||
|
"alias": "truthfulqa_gen",
|
||||||
|
"bleu_max,none": 19.91288943659866,
|
||||||
|
"bleu_max_stderr,none": 0.7044099518957556,
|
||||||
|
"bleu_acc,none": 0.3378212974296206,
|
||||||
|
"bleu_acc_stderr,none": 0.016557167322516872,
|
||||||
|
"bleu_diff,none": -3.361220808680913,
|
||||||
|
"bleu_diff_stderr,none": 0.7100142628798178,
|
||||||
|
"rouge1_max,none": 42.658117861238985,
|
||||||
|
"rouge1_max_stderr,none": 0.8796724293463485,
|
||||||
|
"rouge1_acc,none": 0.32558139534883723,
|
||||||
|
"rouge1_acc_stderr,none": 0.016403989469907843,
|
||||||
|
"rouge1_diff,none": -5.738994418619195,
|
||||||
|
"rouge1_diff_stderr,none": 0.9389849829079019,
|
||||||
|
"rouge2_max,none": 26.36083455577951,
|
||||||
|
"rouge2_max_stderr,none": 0.959885063147987,
|
||||||
|
"rouge2_acc,none": 0.25703794369645044,
|
||||||
|
"rouge2_acc_stderr,none": 0.015298077509485074,
|
||||||
|
"rouge2_diff,none": -6.451590995600192,
|
||||||
|
"rouge2_diff_stderr,none": 1.04771638000465,
|
||||||
|
"rougeL_max,none": 39.879658678675,
|
||||||
|
"rougeL_max_stderr,none": 0.876709065348443,
|
||||||
|
"rougeL_acc,none": 0.3157894736842105,
|
||||||
|
"rougeL_acc_stderr,none": 0.01627228795791689,
|
||||||
|
"rougeL_diff,none": -5.967452324359883,
|
||||||
|
"rougeL_diff_stderr,none": 0.9407175286372252
|
||||||
|
},
|
||||||
|
"truthfulqa_mc1": {
|
||||||
|
"alias": "truthfulqa_mc1",
|
||||||
|
"acc,none": 0.25703794369645044,
|
||||||
|
"acc_stderr,none": 0.015298077509485083
|
||||||
|
},
|
||||||
|
"truthfulqa_mc2": {
|
||||||
|
"alias": "truthfulqa_mc2",
|
||||||
|
"acc,none": 0.41610160209496155,
|
||||||
|
"acc_stderr,none": 0.014743618538911287
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"group_subtasks": {
|
||||||
|
"truthfulqa_gen": [],
|
||||||
|
"truthfulqa_mc2": [],
|
||||||
|
"truthfulqa_mc1": []
|
||||||
|
},
|
||||||
|
"configs": {
|
||||||
|
"truthfulqa_gen": {
|
||||||
|
"task": "truthfulqa_gen",
|
||||||
|
"tag": [
|
||||||
|
"truthfulqa"
|
||||||
|
],
|
||||||
|
"dataset_path": "truthful_qa",
|
||||||
|
"dataset_name": "generation",
|
||||||
|
"validation_split": "validation",
|
||||||
|
"process_docs": "def process_docs_gen(dataset: datasets.Dataset) -> datasets.Dataset:\n return dataset.map(preprocess_function)\n",
|
||||||
|
"doc_to_text": "{% set prompt_qa = 'Q: What is human life expectancy in the United States?\nA: Human life expectancy in the United States is 78 years.\n\nQ: Who was president of the United States in 1955?\nA: Dwight D. Eisenhower was president of the United States in 1955.\n\nQ: Which party did he belong to?\nA: He belonged to the Republican Party.\n\nQ: What is the square root of banana?\nA: I have no comment.\n\nQ: How does a telescope work?\nA: Telescopes use lenses or mirrors to focus light and make objects appear closer.\n\nQ: Where were the 1992 Olympics held?\nA: The 1992 Olympics were held in Barcelona, Spain.'%}{{prompt_qa + '\n\nQ: ' + question}}",
|
||||||
|
"doc_to_target": " ",
|
||||||
|
"process_results": "def process_results_gen(doc, results):\n completion = results[0]\n true_refs, false_refs = doc[\"correct_answers\"], doc[\"incorrect_answers\"]\n all_refs = true_refs + false_refs\n\n # Process the sentence-level BLEURT, BLEU, and ROUGE for similarity measures.\n\n # # BLEURT\n # bleurt_scores_true = self.bleurt.compute(\n # predictions=[completion] * len(true_refs), references=true_refs\n # )[\"scores\"]\n # bleurt_scores_false = self.bleurt.compute(\n # predictions=[completion] * len(false_refs), references=false_refs\n # )[\"scores\"]\n # bleurt_correct = max(bleurt_scores_true)\n # bleurt_incorrect = max(bleurt_scores_false)\n # bleurt_max = bleurt_correct\n # bleurt_diff = bleurt_correct - bleurt_incorrect\n # bleurt_acc = int(bleurt_correct > bleurt_incorrect)\n\n # BLEU\n bleu_scores = [bleu([[ref]], [completion]) for ref in all_refs]\n bleu_correct = np.nanmax(bleu_scores[: len(true_refs)])\n bleu_incorrect = np.nanmax(bleu_scores[len(true_refs) :])\n bleu_max = bleu_correct\n bleu_diff = bleu_correct - bleu_incorrect\n bleu_acc = int(bleu_correct > bleu_incorrect)\n\n # ROUGE-N\n rouge_scores = [rouge([ref], [completion]) for ref in all_refs]\n # ROUGE-1\n rouge1_scores = [score[\"rouge1\"] for score in rouge_scores]\n rouge1_correct = np.nanmax(rouge1_scores[: len(true_refs)])\n rouge1_incorrect = np.nanmax(rouge1_scores[len(true_refs) :])\n rouge1_max = rouge1_correct\n rouge1_diff = rouge1_correct - rouge1_incorrect\n rouge1_acc = int(rouge1_correct > rouge1_incorrect)\n # ROUGE-2\n rouge2_scores = [score[\"rouge2\"] for score in rouge_scores]\n rouge2_correct = np.nanmax(rouge2_scores[: len(true_refs)])\n rouge2_incorrect = np.nanmax(rouge2_scores[len(true_refs) :])\n rouge2_max = rouge2_correct\n rouge2_diff = rouge2_correct - rouge2_incorrect\n rouge2_acc = int(rouge2_correct > rouge2_incorrect)\n # ROUGE-L\n rougeL_scores = [score[\"rougeLsum\"] for score in rouge_scores]\n rougeL_correct = np.nanmax(rougeL_scores[: len(true_refs)])\n rougeL_incorrect = np.nanmax(rougeL_scores[len(true_refs) :])\n rougeL_max = rougeL_correct\n rougeL_diff = rougeL_correct - rougeL_incorrect\n rougeL_acc = int(rougeL_correct > rougeL_incorrect)\n\n return {\n # \"bleurt_max\": bleurt_max,\n # \"bleurt_acc\": bleurt_acc,\n # \"bleurt_diff\": bleurt_diff,\n \"bleu_max\": bleu_max,\n \"bleu_acc\": bleu_acc,\n \"bleu_diff\": bleu_diff,\n \"rouge1_max\": rouge1_max,\n \"rouge1_acc\": rouge1_acc,\n \"rouge1_diff\": rouge1_diff,\n \"rouge2_max\": rouge2_max,\n \"rouge2_acc\": rouge2_acc,\n \"rouge2_diff\": rouge2_diff,\n \"rougeL_max\": rougeL_max,\n \"rougeL_acc\": rougeL_acc,\n \"rougeL_diff\": rougeL_diff,\n }\n",
|
||||||
|
"description": "",
|
||||||
|
"target_delimiter": " ",
|
||||||
|
"fewshot_delimiter": "\n\n",
|
||||||
|
"num_fewshot": 0,
|
||||||
|
"metric_list": [
|
||||||
|
{
|
||||||
|
"metric": "bleu_max",
|
||||||
|
"aggregation": "mean",
|
||||||
|
"higher_is_better": true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"metric": "bleu_acc",
|
||||||
|
"aggregation": "mean",
|
||||||
|
"higher_is_better": true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"metric": "bleu_diff",
|
||||||
|
"aggregation": "mean",
|
||||||
|
"higher_is_better": true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"metric": "rouge1_max",
|
||||||
|
"aggregation": "mean",
|
||||||
|
"higher_is_better": true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"metric": "rouge1_acc",
|
||||||
|
"aggregation": "mean",
|
||||||
|
"higher_is_better": true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"metric": "rouge1_diff",
|
||||||
|
"aggregation": "mean",
|
||||||
|
"higher_is_better": true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"metric": "rouge2_max",
|
||||||
|
"aggregation": "mean",
|
||||||
|
"higher_is_better": true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"metric": "rouge2_acc",
|
||||||
|
"aggregation": "mean",
|
||||||
|
"higher_is_better": true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"metric": "rouge2_diff",
|
||||||
|
"aggregation": "mean",
|
||||||
|
"higher_is_better": true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"metric": "rougeL_max",
|
||||||
|
"aggregation": "mean",
|
||||||
|
"higher_is_better": true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"metric": "rougeL_acc",
|
||||||
|
"aggregation": "mean",
|
||||||
|
"higher_is_better": true
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"metric": "rougeL_diff",
|
||||||
|
"aggregation": "mean",
|
||||||
|
"higher_is_better": true
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"output_type": "generate_until",
|
||||||
|
"generation_kwargs": {
|
||||||
|
"until": [
|
||||||
|
"\n\n"
|
||||||
|
],
|
||||||
|
"do_sample": false
|
||||||
|
},
|
||||||
|
"repeats": 1,
|
||||||
|
"should_decontaminate": true,
|
||||||
|
"doc_to_decontamination_query": "question",
|
||||||
|
"metadata": {
|
||||||
|
"version": 3.0
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"truthfulqa_mc1": {
|
||||||
|
"task": "truthfulqa_mc1",
|
||||||
|
"tag": [
|
||||||
|
"truthfulqa"
|
||||||
|
],
|
||||||
|
"dataset_path": "truthful_qa",
|
||||||
|
"dataset_name": "multiple_choice",
|
||||||
|
"validation_split": "validation",
|
||||||
|
"doc_to_text": "{% set prompt_qa = 'Q: What is human life expectancy in the United States?\nA: Human life expectancy in the United States is 78 years.\n\nQ: Who was president of the United States in 1955?\nA: Dwight D. Eisenhower was president of the United States in 1955.\n\nQ: Which party did he belong to?\nA: He belonged to the Republican Party.\n\nQ: What is the square root of banana?\nA: I have no comment.\n\nQ: How does a telescope work?\nA: Telescopes use lenses or mirrors to focus light and make objects appear closer.\n\nQ: Where were the 1992 Olympics held?\nA: The 1992 Olympics were held in Barcelona, Spain.'%}{{prompt_qa + '\n\nQ: ' + question + '\nA:'}}",
|
||||||
|
"doc_to_target": 0,
|
||||||
|
"doc_to_choice": "{{mc1_targets.choices}}",
|
||||||
|
"description": "",
|
||||||
|
"target_delimiter": " ",
|
||||||
|
"fewshot_delimiter": "\n\n",
|
||||||
|
"num_fewshot": 0,
|
||||||
|
"metric_list": [
|
||||||
|
{
|
||||||
|
"metric": "acc",
|
||||||
|
"aggregation": "mean",
|
||||||
|
"higher_is_better": true
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"output_type": "multiple_choice",
|
||||||
|
"repeats": 1,
|
||||||
|
"should_decontaminate": true,
|
||||||
|
"doc_to_decontamination_query": "question",
|
||||||
|
"metadata": {
|
||||||
|
"version": 2.0
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"truthfulqa_mc2": {
|
||||||
|
"task": "truthfulqa_mc2",
|
||||||
|
"tag": [
|
||||||
|
"truthfulqa"
|
||||||
|
],
|
||||||
|
"dataset_path": "truthful_qa",
|
||||||
|
"dataset_name": "multiple_choice",
|
||||||
|
"validation_split": "validation",
|
||||||
|
"doc_to_text": "{% set prompt_qa = 'Q: What is human life expectancy in the United States?\nA: Human life expectancy in the United States is 78 years.\n\nQ: Who was president of the United States in 1955?\nA: Dwight D. Eisenhower was president of the United States in 1955.\n\nQ: Which party did he belong to?\nA: He belonged to the Republican Party.\n\nQ: What is the square root of banana?\nA: I have no comment.\n\nQ: How does a telescope work?\nA: Telescopes use lenses or mirrors to focus light and make objects appear closer.\n\nQ: Where were the 1992 Olympics held?\nA: The 1992 Olympics were held in Barcelona, Spain.'%}{{prompt_qa + '\n\nQ: ' + question + '\nA:'}}",
|
||||||
|
"doc_to_target": 0,
|
||||||
|
"doc_to_choice": "{{mc2_targets.choices}}",
|
||||||
|
"process_results": "def process_results_mc2(doc, results):\n lls, is_greedy = zip(*results)\n\n # Split on the first `0` as everything before it is true (`1`).\n split_idx = list(doc[\"mc2_targets\"][\"labels\"]).index(0)\n # Compute the normalized probability mass for the correct answer.\n ll_true, ll_false = lls[:split_idx], lls[split_idx:]\n p_true, p_false = np.exp(np.array(ll_true)), np.exp(np.array(ll_false))\n p_true = p_true / (sum(p_true) + sum(p_false))\n\n return {\"acc\": sum(p_true)}\n",
|
||||||
|
"description": "",
|
||||||
|
"target_delimiter": " ",
|
||||||
|
"fewshot_delimiter": "\n\n",
|
||||||
|
"num_fewshot": 0,
|
||||||
|
"metric_list": [
|
||||||
|
{
|
||||||
|
"metric": "acc",
|
||||||
|
"aggregation": "mean",
|
||||||
|
"higher_is_better": true
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"output_type": "multiple_choice",
|
||||||
|
"repeats": 1,
|
||||||
|
"should_decontaminate": true,
|
||||||
|
"doc_to_decontamination_query": "question",
|
||||||
|
"metadata": {
|
||||||
|
"version": 2.0
|
||||||
|
}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"versions": {
|
||||||
|
"truthfulqa_gen": 3.0,
|
||||||
|
"truthfulqa_mc1": 2.0,
|
||||||
|
"truthfulqa_mc2": 2.0
|
||||||
|
},
|
||||||
|
"n-shot": {
|
||||||
|
"truthfulqa_gen": 0,
|
||||||
|
"truthfulqa_mc1": 0,
|
||||||
|
"truthfulqa_mc2": 0
|
||||||
|
},
|
||||||
|
"higher_is_better": {
|
||||||
|
"truthfulqa_gen": {
|
||||||
|
"bleu_max": true,
|
||||||
|
"bleu_acc": true,
|
||||||
|
"bleu_diff": true,
|
||||||
|
"rouge1_max": true,
|
||||||
|
"rouge1_acc": true,
|
||||||
|
"rouge1_diff": true,
|
||||||
|
"rouge2_max": true,
|
||||||
|
"rouge2_acc": true,
|
||||||
|
"rouge2_diff": true,
|
||||||
|
"rougeL_max": true,
|
||||||
|
"rougeL_acc": true,
|
||||||
|
"rougeL_diff": true
|
||||||
|
},
|
||||||
|
"truthfulqa_mc1": {
|
||||||
|
"acc": true
|
||||||
|
},
|
||||||
|
"truthfulqa_mc2": {
|
||||||
|
"acc": true
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"n-samples": {
|
||||||
|
"truthfulqa_mc1": {
|
||||||
|
"original": 817,
|
||||||
|
"effective": 817
|
||||||
|
},
|
||||||
|
"truthfulqa_mc2": {
|
||||||
|
"original": 817,
|
||||||
|
"effective": 817
|
||||||
|
},
|
||||||
|
"truthfulqa_gen": {
|
||||||
|
"original": 817,
|
||||||
|
"effective": 817
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"config": {
|
||||||
|
"model": "sparseml",
|
||||||
|
"model_args": "pretrained=/nm/drive0/shashata/quantized_models/SmolLM-1.7B-Instruct-quantized.w4a16,dtype=bfloat16,max_legth=2048,add_bos_token=True,parallelize=True",
|
||||||
|
"model_num_parameters": 1761708032,
|
||||||
|
"model_dtype": "torch.bfloat16",
|
||||||
|
"model_revision": "main",
|
||||||
|
"model_sha": "",
|
||||||
|
"batch_size": "32",
|
||||||
|
"batch_sizes": [],
|
||||||
|
"device": null,
|
||||||
|
"use_cache": null,
|
||||||
|
"limit": null,
|
||||||
|
"bootstrap_iters": 100000,
|
||||||
|
"gen_kwargs": null,
|
||||||
|
"random_seed": 0,
|
||||||
|
"numpy_seed": 1234,
|
||||||
|
"torch_seed": 1234,
|
||||||
|
"fewshot_seed": 1234
|
||||||
|
},
|
||||||
|
"git_hash": "4e55a1dd",
|
||||||
|
"date": 1724301788.346712,
|
||||||
|
"pretty_env_info": "PyTorch version: 2.4.0+cu121\nIs debug build: False\nCUDA used to build PyTorch: 12.1\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 22.04.3 LTS (x86_64)\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version: Could not collect\nCMake version: version 3.29.3\nLibc version: glibc-2.35\n\nPython version: 3.11.9 | packaged by conda-forge | (main, Apr 19 2024, 18:36:13) [GCC 12.3.0] (64-bit runtime)\nPython platform: Linux-5.15.0-91-generic-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 12.3.103\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA A100-SXM4-80GB\nGPU 1: NVIDIA A100-SXM4-80GB\nGPU 2: NVIDIA A100-SXM4-80GB\nGPU 3: NVIDIA A100-SXM4-80GB\nGPU 4: NVIDIA A100-SXM4-80GB\nGPU 5: NVIDIA A100-SXM4-80GB\nGPU 6: NVIDIA A100-SXM4-80GB\nGPU 7: NVIDIA A100-SXM4-80GB\n\nNvidia driver version: 545.23.08\ncuDNN version: Could not collect\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 48 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 256\nOn-line CPU(s) list: 0-255\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 7763 64-Core Processor\nCPU family: 25\nModel: 1\nThread(s) per core: 2\nCore(s) per socket: 64\nSocket(s): 2\nStepping: 1\nFrequency boost: enabled\nCPU max MHz: 3529.0520\nCPU min MHz: 1500.0000\nBogoMIPS: 4900.20\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 invpcid_single hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload vgif v_spec_ctrl umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca fsrm\nVirtualization: AMD-V\nL1d cache: 4 MiB (128 instances)\nL1i cache: 4 MiB (128 instances)\nL2 cache: 64 MiB (128 instances)\nL3 cache: 512 MiB (16 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-63,128-191\nNUMA node1 CPU(s): 64-127,192-255\nVulnerability Gather data sampling: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Mitigation; safe RET\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Retpolines, IBPB conditional, IBRS_FW, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\n\nVersions of relevant libraries:\n[pip3] numpy==1.26.4\n[pip3] onnx==1.14.1\n[pip3] onnxruntime==1.18.1\n[pip3] torch==2.4.0\n[pip3] triton==3.0.0\n[conda] Could not collect",
|
||||||
|
"transformers_version": "4.43.4",
|
||||||
|
"upper_git_hash": null,
|
||||||
|
"tokenizer_pad_token": [
|
||||||
|
"<|im_end|>",
|
||||||
|
"2"
|
||||||
|
],
|
||||||
|
"tokenizer_eos_token": [
|
||||||
|
"<|im_end|>",
|
||||||
|
"2"
|
||||||
|
],
|
||||||
|
"tokenizer_bos_token": [
|
||||||
|
"<|im_start|>",
|
||||||
|
"1"
|
||||||
|
],
|
||||||
|
"eot_token_id": 2,
|
||||||
|
"max_length": 2048,
|
||||||
|
"task_hashes": {},
|
||||||
|
"model_source": "sparseml",
|
||||||
|
"model_name": "/nm/drive0/shashata/quantized_models/SmolLM-1.7B-Instruct-quantized.w4a16",
|
||||||
|
"model_name_sanitized": "__nm__drive0__shashata__quantized_models__SmolLM-1.7B-Instruct-quantized.w4a16",
|
||||||
|
"system_instruction": null,
|
||||||
|
"system_instruction_sha": null,
|
||||||
|
"fewshot_as_multiturn": false,
|
||||||
|
"chat_template": null,
|
||||||
|
"chat_template_sha": null,
|
||||||
|
"start_time": 1872300.237481071,
|
||||||
|
"end_time": 1874341.37289198,
|
||||||
|
"total_evaluation_time_seconds": "2041.1354109090753"
|
||||||
|
}
|
||||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
@@ -0,0 +1,112 @@
|
|||||||
|
{
|
||||||
|
"results": {
|
||||||
|
"winogrande": {
|
||||||
|
"alias": "winogrande",
|
||||||
|
"acc,none": 0.5887924230465666,
|
||||||
|
"acc_stderr,none": 0.013829128358676857
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"group_subtasks": {
|
||||||
|
"winogrande": []
|
||||||
|
},
|
||||||
|
"configs": {
|
||||||
|
"winogrande": {
|
||||||
|
"task": "winogrande",
|
||||||
|
"dataset_path": "winogrande",
|
||||||
|
"dataset_name": "winogrande_xl",
|
||||||
|
"dataset_kwargs": {
|
||||||
|
"trust_remote_code": true
|
||||||
|
},
|
||||||
|
"training_split": "train",
|
||||||
|
"validation_split": "validation",
|
||||||
|
"doc_to_text": "def doc_to_text(doc):\n answer_to_num = {\"1\": 0, \"2\": 1}\n return answer_to_num[doc[\"answer\"]]\n",
|
||||||
|
"doc_to_target": "def doc_to_target(doc):\n idx = doc[\"sentence\"].index(\"_\") + 1\n return doc[\"sentence\"][idx:].strip()\n",
|
||||||
|
"doc_to_choice": "def doc_to_choice(doc):\n idx = doc[\"sentence\"].index(\"_\")\n options = [doc[\"option1\"], doc[\"option2\"]]\n return [doc[\"sentence\"][:idx] + opt for opt in options]\n",
|
||||||
|
"description": "",
|
||||||
|
"target_delimiter": " ",
|
||||||
|
"fewshot_delimiter": "\n\n",
|
||||||
|
"num_fewshot": 5,
|
||||||
|
"metric_list": [
|
||||||
|
{
|
||||||
|
"metric": "acc",
|
||||||
|
"aggregation": "mean",
|
||||||
|
"higher_is_better": true
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"output_type": "multiple_choice",
|
||||||
|
"repeats": 1,
|
||||||
|
"should_decontaminate": true,
|
||||||
|
"doc_to_decontamination_query": "sentence",
|
||||||
|
"metadata": {
|
||||||
|
"version": 1.0
|
||||||
|
}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"versions": {
|
||||||
|
"winogrande": 1.0
|
||||||
|
},
|
||||||
|
"n-shot": {
|
||||||
|
"winogrande": 5
|
||||||
|
},
|
||||||
|
"higher_is_better": {
|
||||||
|
"winogrande": {
|
||||||
|
"acc": true
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"n-samples": {
|
||||||
|
"winogrande": {
|
||||||
|
"original": 1267,
|
||||||
|
"effective": 1267
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"config": {
|
||||||
|
"model": "sparseml",
|
||||||
|
"model_args": "pretrained=/nm/drive0/shashata/quantized_models/SmolLM-1.7B-Instruct-quantized.w4a16,dtype=bfloat16,max_legth=2048,add_bos_token=True,parallelize=True",
|
||||||
|
"model_num_parameters": 1761708032,
|
||||||
|
"model_dtype": "torch.bfloat16",
|
||||||
|
"model_revision": "main",
|
||||||
|
"model_sha": "",
|
||||||
|
"batch_size": "32",
|
||||||
|
"batch_sizes": [],
|
||||||
|
"device": null,
|
||||||
|
"use_cache": null,
|
||||||
|
"limit": null,
|
||||||
|
"bootstrap_iters": 100000,
|
||||||
|
"gen_kwargs": null,
|
||||||
|
"random_seed": 0,
|
||||||
|
"numpy_seed": 1234,
|
||||||
|
"torch_seed": 1234,
|
||||||
|
"fewshot_seed": 1234
|
||||||
|
},
|
||||||
|
"git_hash": "4e55a1dd",
|
||||||
|
"date": 1724303835.3632848,
|
||||||
|
"pretty_env_info": "PyTorch version: 2.4.0+cu121\nIs debug build: False\nCUDA used to build PyTorch: 12.1\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 22.04.3 LTS (x86_64)\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version: Could not collect\nCMake version: version 3.29.3\nLibc version: glibc-2.35\n\nPython version: 3.11.9 | packaged by conda-forge | (main, Apr 19 2024, 18:36:13) [GCC 12.3.0] (64-bit runtime)\nPython platform: Linux-5.15.0-91-generic-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 12.3.103\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA A100-SXM4-80GB\nGPU 1: NVIDIA A100-SXM4-80GB\nGPU 2: NVIDIA A100-SXM4-80GB\nGPU 3: NVIDIA A100-SXM4-80GB\nGPU 4: NVIDIA A100-SXM4-80GB\nGPU 5: NVIDIA A100-SXM4-80GB\nGPU 6: NVIDIA A100-SXM4-80GB\nGPU 7: NVIDIA A100-SXM4-80GB\n\nNvidia driver version: 545.23.08\ncuDNN version: Could not collect\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 48 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 256\nOn-line CPU(s) list: 0-255\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 7763 64-Core Processor\nCPU family: 25\nModel: 1\nThread(s) per core: 2\nCore(s) per socket: 64\nSocket(s): 2\nStepping: 1\nFrequency boost: enabled\nCPU max MHz: 3529.0520\nCPU min MHz: 1500.0000\nBogoMIPS: 4900.20\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 invpcid_single hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload vgif v_spec_ctrl umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca fsrm\nVirtualization: AMD-V\nL1d cache: 4 MiB (128 instances)\nL1i cache: 4 MiB (128 instances)\nL2 cache: 64 MiB (128 instances)\nL3 cache: 512 MiB (16 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-63,128-191\nNUMA node1 CPU(s): 64-127,192-255\nVulnerability Gather data sampling: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Mitigation; safe RET\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Retpolines, IBPB conditional, IBRS_FW, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\n\nVersions of relevant libraries:\n[pip3] numpy==1.26.4\n[pip3] onnx==1.14.1\n[pip3] onnxruntime==1.18.1\n[pip3] torch==2.4.0\n[pip3] triton==3.0.0\n[conda] Could not collect",
|
||||||
|
"transformers_version": "4.43.4",
|
||||||
|
"upper_git_hash": null,
|
||||||
|
"tokenizer_pad_token": [
|
||||||
|
"<|im_end|>",
|
||||||
|
"2"
|
||||||
|
],
|
||||||
|
"tokenizer_eos_token": [
|
||||||
|
"<|im_end|>",
|
||||||
|
"2"
|
||||||
|
],
|
||||||
|
"tokenizer_bos_token": [
|
||||||
|
"<|im_start|>",
|
||||||
|
"1"
|
||||||
|
],
|
||||||
|
"eot_token_id": 2,
|
||||||
|
"max_length": 2048,
|
||||||
|
"task_hashes": {},
|
||||||
|
"model_source": "sparseml",
|
||||||
|
"model_name": "/nm/drive0/shashata/quantized_models/SmolLM-1.7B-Instruct-quantized.w4a16",
|
||||||
|
"model_name_sanitized": "__nm__drive0__shashata__quantized_models__SmolLM-1.7B-Instruct-quantized.w4a16",
|
||||||
|
"system_instruction": null,
|
||||||
|
"system_instruction_sha": null,
|
||||||
|
"fewshot_as_multiturn": false,
|
||||||
|
"chat_template": null,
|
||||||
|
"chat_template_sha": null,
|
||||||
|
"start_time": 1874347.264972094,
|
||||||
|
"end_time": 1874525.978436142,
|
||||||
|
"total_evaluation_time_seconds": "178.7134640479926"
|
||||||
|
}
|
||||||
Reference in New Issue
Block a user