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Model: RedHatAI/starcoder2-15b-FP8 Source: Original Platform
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README.md
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---
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tags:
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- fp8
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- vllm
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license: other
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license_name: bigcode-openrail-m
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license_link: https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement
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---
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# starcoder2-15b-FP8
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## Model Overview
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- **Model Architecture:** starcoder2-15b
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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.
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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:** 8/1/2024
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- **Version:** 1.0
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- **License(s):** [bigcode-openrail-m](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement)
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- **Model Developers:** Neural Magic
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Quantized version of [starcoder2-15b](https://huggingface.co/bigcode/starcoder2-15b).
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<!-- It achieves an average score of 73.19 on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark (version 1), whereas the unquantized model achieves 73.48. -->
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It achieves an average score of 50.70 on the [HumanEval+](https://github.com/openai/human-eval?tab=readme-ov-file) benchmark, whereas the unquantized model achieves 50.25.
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### Model Optimizations
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This model was obtained by quantizing the weights and activations of [starcoder2-15b](https://huggingface.co/bigcode/starcoder2-15b) to FP8 data type, ready for inference with vLLM >= 0.5.2.
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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/starcoder2-15b-FP8"
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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?"},
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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, trust_remote_code=True, max_model_len=4096)
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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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A slight modification to the code was made due to the parameters of the model. Running the below code will throw an index error, and simply replacing the erroneous line with ```max_quant_shape = param.shape[0]``` resolves the issue.
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```python
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import torch
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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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from llmcompressor.transformers.compression.helpers import (
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calculate_offload_device_map,
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custom_offload_device_map,
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)
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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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"""
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model_stub = "bigcode/starcoder2-15b"
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model_name = model_stub.split("/")[-1]
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device_map = calculate_offload_device_map(
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model_stub, reserve_for_hessians=False, num_gpus=8, torch_dtype=torch.float16
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)
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model = SparseAutoModelForCausalLM.from_pretrained(
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model_stub, torch_dtype=torch.float16, device_map=device_map
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)
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tokenizer = AutoTokenizer.from_pretrained(model_stub)
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output_dir = f"./{model_name}-FP8"
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DATASET_ID = "HuggingFaceH4/ultrachat_200k"
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DATASET_SPLIT = "train_sft"
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NUM_CALIBRATION_SAMPLES = 512
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MAX_SEQUENCE_LENGTH = 4096
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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 preprocess(example):
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return {
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"text": " ".join([msg["content"] for msg in example["messages"]])
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}
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ds = ds.map(preprocess)
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def tokenize(sample):
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return tokenizer(
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sample["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(tokenize, remove_columns=ds.column_names)
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oneshot(
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model=model,
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output_dir=output_dir,
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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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save_compressed=True,
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)
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```
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## Evaluation
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The model was evaluated on the [HumanEval+](https://github.com/openai/human-eval?tab=readme-ov-file) benchmark with the [Neural Magic fork](https://github.com/neuralmagic/evalplus) of the [EvalPlus implementation of HumanEval+](https://github.com/evalplus/evalplus) and the [vLLM](https://docs.vllm.ai/en/stable/) engine, using the following command:
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```
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python codegen/generate.py --model neuralmagic/starcoder2-15b-FP8 --temperature 0.2 --n_samples 50 --resume --root ~ --dataset humaneval
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python evalplus/sanitize.py ~/humaneval/neuralmagic--starcoder2-15b-FP8_vllm_temp_0.2
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evalplus.evaluate --dataset humaneval --samples ~/humaneval/neuralmagic--starcoder2-15b-FP8_vllm_temp_0.2-sanitized
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```
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### Accuracy
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#### HumanEval+ 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>starcoder2-15b</strong>
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</td>
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<td><strong>starcoder2-15b-FP8(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>base pass@1
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</td>
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<td>44.8
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</td>
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<td>45.0
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</td>
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<td>100.4%
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</td>
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</tr>
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<tr>
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<td>base pass@10
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</td>
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<td>62.7
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</td>
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<td>64.0
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</td>
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<td>102.0%
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</td>
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</tr>
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<tr>
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<td>base+extra pass@1
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</td>
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<td>38.6
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</td>
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<td>38.4
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</td>
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<td>99.48%
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</td>
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</tr>
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<tr>
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<td>base+extra pass@10
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</td>
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<td>54.9
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</td>
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<td>55.4
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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>50.25</strong>
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</td>
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<td><strong>50.70</strong>
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</td>
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<td><strong>100.7%</strong>
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</td>
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</tr>
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</table>
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config.json
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"Starcoder2ForCausalLM"
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],
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"norm_epsilon": 1e-05,
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"norm_type": "layer_norm",
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"rope_theta": 100000,
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"ignore": [
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"quant_method": "compressed-tensors",
|
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"quantization_status": "compressed"
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}
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}
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recipe.yaml
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recipe.yaml
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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: {num_bits: 8, type: float, strategy: tensor, dynamic: false, symmetric: true}
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input_activations: {num_bits: 8, type: float, strategy: tensor, dynamic: false,
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symmetric: true}
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targets: [Linear]
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"<EMAIL>",
|
||||
"<KEY>",
|
||||
"<PASSWORD>"
|
||||
],
|
||||
"bos_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": "<|endoftext|>",
|
||||
"unk_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
98410
tokenizer.json
Normal file
98410
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
357
tokenizer_config.json
Normal file
357
tokenizer_config.json
Normal file
@@ -0,0 +1,357 @@
|
||||
{
|
||||
"add_prefix_space": false,
|
||||
"added_tokens_decoder": {
|
||||
"0": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"1": {
|
||||
"content": "<fim_prefix>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"2": {
|
||||
"content": "<fim_middle>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"3": {
|
||||
"content": "<fim_suffix>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"4": {
|
||||
"content": "<fim_pad>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"5": {
|
||||
"content": "<repo_name>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"6": {
|
||||
"content": "<file_sep>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"7": {
|
||||
"content": "<issue_start>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"8": {
|
||||
"content": "<issue_comment>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"9": {
|
||||
"content": "<issue_closed>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"10": {
|
||||
"content": "<jupyter_start>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"11": {
|
||||
"content": "<jupyter_text>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"12": {
|
||||
"content": "<jupyter_code>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"13": {
|
||||
"content": "<jupyter_output>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"14": {
|
||||
"content": "<jupyter_script>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"15": {
|
||||
"content": "<empty_output>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"16": {
|
||||
"content": "<code_to_intermediate>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"17": {
|
||||
"content": "<intermediate_to_code>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"18": {
|
||||
"content": "<pr>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"19": {
|
||||
"content": "<pr_status>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"20": {
|
||||
"content": "<pr_is_merged>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"21": {
|
||||
"content": "<pr_base>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"22": {
|
||||
"content": "<pr_file>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"23": {
|
||||
"content": "<pr_base_code>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"24": {
|
||||
"content": "<pr_diff>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"25": {
|
||||
"content": "<pr_diff_hunk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"26": {
|
||||
"content": "<pr_comment>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"27": {
|
||||
"content": "<pr_event_id>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"28": {
|
||||
"content": "<pr_review>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"29": {
|
||||
"content": "<pr_review_state>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"30": {
|
||||
"content": "<pr_review_comment>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"31": {
|
||||
"content": "<pr_in_reply_to_review_id>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32": {
|
||||
"content": "<pr_in_reply_to_comment_id>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"33": {
|
||||
"content": "<pr_diff_hunk_comment_line>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"34": {
|
||||
"content": "<NAME>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"35": {
|
||||
"content": "<EMAIL>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"36": {
|
||||
"content": "<KEY>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"37": {
|
||||
"content": "<PASSWORD>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|endoftext|>",
|
||||
"<fim_prefix>",
|
||||
"<fim_middle>",
|
||||
"<fim_suffix>",
|
||||
"<fim_pad>",
|
||||
"<repo_name>",
|
||||
"<file_sep>",
|
||||
"<issue_start>",
|
||||
"<issue_comment>",
|
||||
"<issue_closed>",
|
||||
"<jupyter_start>",
|
||||
"<jupyter_text>",
|
||||
"<jupyter_code>",
|
||||
"<jupyter_output>",
|
||||
"<jupyter_script>",
|
||||
"<empty_output>",
|
||||
"<code_to_intermediate>",
|
||||
"<intermediate_to_code>",
|
||||
"<pr>",
|
||||
"<pr_status>",
|
||||
"<pr_is_merged>",
|
||||
"<pr_base>",
|
||||
"<pr_file>",
|
||||
"<pr_base_code>",
|
||||
"<pr_diff>",
|
||||
"<pr_diff_hunk>",
|
||||
"<pr_comment>",
|
||||
"<pr_event_id>",
|
||||
"<pr_review>",
|
||||
"<pr_review_state>",
|
||||
"<pr_review_comment>",
|
||||
"<pr_in_reply_to_review_id>",
|
||||
"<pr_in_reply_to_comment_id>",
|
||||
"<pr_diff_hunk_comment_line>",
|
||||
"<NAME>",
|
||||
"<EMAIL>",
|
||||
"<KEY>",
|
||||
"<PASSWORD>"
|
||||
],
|
||||
"bos_token": "<|endoftext|>",
|
||||
"clean_up_tokenization_spaces": true,
|
||||
"eos_token": "<|endoftext|>",
|
||||
"model_max_length": 1000000000000000019884624838656,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"tokenizer_class": "GPT2Tokenizer",
|
||||
"unk_token": "<|endoftext|>",
|
||||
"vocab_size": 49152
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user