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Model: neuralmagic/gemma-2-9b-it-quantized.w8a8 Source: Original Platform
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README.md
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---
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pipeline_tag: text-generation
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tags:
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- int8
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- vllm
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license: gemma
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base_model: google/gemma-2-9b-it
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---
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# gemma-2-9b-it-quantized.w8a8
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## Model Overview
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- **Model Architecture:** Gemma 2
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- **Input:** Text
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- **Output:** Text
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- **Model Optimizations:**
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- **Activation quantization:** INT8
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- **Weight quantization:** INT8
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- **Intended Use Cases:** Intended for commercial and research use in English. Similarly to [gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it), 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:** 8/16/2024
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- **Version:** 1.0
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- **License(s):** [gemma](https://ai.google.dev/gemma/terms)
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- **Model Developers:** Neural Magic
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Quantized version of [gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it).
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It achieves an average score of 73.71 on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark (version 1), whereas the unquantized model achieves 73.80.
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### Model Optimizations
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This model was obtained by quantizing the weights of [gemma-2-2b-it](https://huggingface.co/google/gemma-2-2b-it) to INT8 data type.
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This optimization reduces the number of bits used to represent weights and activations from 16 to 8, reducing GPU memory requirements (by approximately 50%) and increasing matrix-multiply compute throughput (by approximately 2x).
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Weight quantization also reduces disk size requirements by approximately 50%.
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Only weights and activations of the linear operators within transformers blocks are quantized.
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Weights are quantized with a symmetric static per-channel scheme, where a fixed linear scaling factor is applied between INT8 and floating point representations for each output channel dimension.
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Activations are quantized with a symmetric dynamic per-token scheme, computing a linear scaling factor at runtime for each token between INT8 and floating point representations.
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Linear scaling factors are computed via by minimizing the mean squarred error (MSE).
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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.GPTQ used a 1% damping factor and 256 sequences sequences taken from Neural Magic's [LLM compression calibration dataset](https://huggingface.co/datasets/neuralmagic/LLM_compression_calibration).
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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/gemma-2-9b-it-quantized.w8a8"
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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": "user", "content": "Who are you? Please respond in pirate speak!"},
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]
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prompts = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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llm = LLM(model=model_id)
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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 also 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 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 datasets import load_dataset
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from llmcompressor.transformers import SparseAutoModelForCausalLM, oneshot
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from llmcompressor.modifiers.quantization import GPTQModifier
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model_id = "google/gemma-2-9b-it"
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num_samples = 256
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max_seq_len = 8192
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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def preprocess_fn(example):
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return {"text": tokenizer.apply_chat_template(example["messages"], add_generation_prompt=False, tokenize=False)}
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ds = load_dataset("neuralmagic/LLM_compression_calibration", split="train")
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ds = ds.shuffle().select(range(num_samples))
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ds = ds.map(preprocess_fn)
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recipe = GPTQModifier(
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sequential=True,
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targets="Linear",
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scheme="W8A8",
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ignore=["lm_head"],
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dampening_frac=0.01,
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observer="mse"
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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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)
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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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)
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model.save_pretrained("gemma-2-9b-it-quantized.w8a8")
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```
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## Evaluation
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The model was evaluated on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) leaderboard tasks (version 1) with the [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/tree/383bbd54bc621086e05aa1b030d8d4d5635b25e6) (commit 383bbd54bc621086e05aa1b030d8d4d5635b25e6) and the [vLLM](https://docs.vllm.ai/en/stable/) engine, using the following command:
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```
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lm_eval \
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--model vllm \
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--model_args pretrained="neuralmagic/gemma-2-9b-it-quantized.w8a8",dtype=auto,gpu_memory_utilization=0.4,add_bos_token=True,max_model_len=4096 \
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--tasks openllm \
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--batch_size auto
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```
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### Accuracy
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#### Open LLM Leaderboard evaluation scores
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<table>
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<tr>
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<td><strong>Benchmark</strong>
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</td>
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<td><strong>gemma-2-9b-it</strong>
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</td>
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<td><strong>gemma-2-9b-it-quantized.w8a8 (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>72.29
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</td>
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<td>71.90
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</td>
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<td>99.5%
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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>71.08
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</td>
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<td>71.42
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</td>
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<td>100.5%
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</td>
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</tr>
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<tr>
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<td>GSM-8K (5-shot, strict-match)
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</td>
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<td>79.30
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</td>
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<td>78.85
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</td>
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<td>99.4%
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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>81.93
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</td>
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<td>81.60
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</td>
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<td>99.6
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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>77.98
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</td>
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<td>78.37
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</td>
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<td>100.5%
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</td>
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</tr>
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<tr>
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<td>TruthfulQA (0-shot)
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</td>
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<td>60.21
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</td>
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<td>60.12
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</td>
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<td>99.9%
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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>73.80</strong>
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</td>
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<td><strong>73.71</strong>
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</td>
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<td><strong>99.9%</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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{
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"_name_or_path": "/root/.cache/huggingface/hub/models--google--gemma-2-9b-it/snapshots/4efc01a1a58107f8c7f68027f5d8e475dfc34a6f",
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"architectures": [
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"Gemma2ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"attn_logit_softcapping": 50.0,
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"bos_token_id": 2,
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"cache_implementation": "hybrid",
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"eos_token_id": 1,
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"final_logit_softcapping": 30.0,
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"head_dim": 256,
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"hidden_act": "gelu_pytorch_tanh",
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"hidden_activation": "gelu_pytorch_tanh",
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"hidden_size": 3584,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 8192,
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"model_type": "gemma2",
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"num_attention_heads": 16,
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"num_hidden_layers": 42,
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"num_key_value_heads": 8,
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"pad_token_id": 0,
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"query_pre_attn_scalar": 256,
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"rms_norm_eps": 1e-06,
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"rope_theta": 10000.0,
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"sliding_window": 4096,
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"sliding_window_size": 4096,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.44.0",
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"use_cache": true,
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"vocab_size": 256000,
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"quantization_config": {
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"config_groups": {
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"group_0": {
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"input_activations": {
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"block_structure": null,
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"dynamic": true,
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"group_size": null,
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"num_bits": 8,
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"observer": "memoryless",
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"observer_kwargs": {},
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"strategy": "token",
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"symmetric": true,
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"type": "int"
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},
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"output_activations": null,
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"targets": [
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"Linear"
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],
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"weights": {
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"block_structure": null,
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"dynamic": false,
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"group_size": null,
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"num_bits": 8,
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"observer": "minmax",
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"observer_kwargs": {},
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"strategy": "channel",
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"symmetric": true,
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"type": "int"
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}
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}
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},
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"format": "int-quantized",
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"global_compression_ratio": 1.2111813093687274,
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"ignore": [
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"lm_head"
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],
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"kv_cache_scheme": null,
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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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configuration.json
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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{
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"_from_model_config": true,
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"bos_token_id": 2,
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"cache_implementation": "hybrid",
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"eos_token_id": 1,
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"pad_token_id": 0,
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"transformers_version": "4.44.0"
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}
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model-00001-of-00003.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:3a330f889ca1a4ee939b52a4e2011b10e2e34ad6fa10591673667119b977a85c
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size 4956386704
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||||||
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||||||
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||||||
|
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||||||
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||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
}
|
||||||
|
}
|
||||||
9
recipe.yaml
Normal file
9
recipe.yaml
Normal file
@@ -0,0 +1,9 @@
|
|||||||
|
quant_stage:
|
||||||
|
quant_modifiers:
|
||||||
|
GPTQModifier:
|
||||||
|
sequential_update: true
|
||||||
|
dampening_frac: 0.01
|
||||||
|
ignore: [lm_head]
|
||||||
|
scheme: W8A8
|
||||||
|
targets: Linear
|
||||||
|
observer: mse
|
||||||
34
special_tokens_map.json
Normal file
34
special_tokens_map.json
Normal file
@@ -0,0 +1,34 @@
|
|||||||
|
{
|
||||||
|
"additional_special_tokens": [
|
||||||
|
"<start_of_turn>",
|
||||||
|
"<end_of_turn>"
|
||||||
|
],
|
||||||
|
"bos_token": {
|
||||||
|
"content": "<bos>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
"eos_token": {
|
||||||
|
"content": "<eos>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
"pad_token": {
|
||||||
|
"content": "<pad>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
"unk_token": {
|
||||||
|
"content": "<unk>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
}
|
||||||
|
}
|
||||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:2e79abe26df05a5549e8622f80f25374f6ba306d56cc13915950e9b58a721211
|
||||||
|
size 17525456
|
||||||
3
tokenizer.model
Normal file
3
tokenizer.model
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:61a7b147390c64585d6c3543dd6fc636906c9af3865a5548f27f31aee1d4c8e2
|
||||||
|
size 4241003
|
||||||
2013
tokenizer_config.json
Normal file
2013
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user