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Model: RedHatAI/SmolLM-135M-Instruct-quantized.w8a8 Source: Original Platform
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README.md
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---
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library_name: transformers
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license: apache-2.0
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language:
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- en
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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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base_model: HuggingFaceTB/SmolLM-135M-Instruct
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---
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# SmolLM-135M-Instruct-quantized.w8a8
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## Model Overview
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- **Model Architecture:** Llama
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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 [SmolLM-135M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM-135M-Instruct), this models is intended for assistant-like chat.
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- **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English.
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- **Release Date:** 8/22/2024
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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-Instruct).
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It achieves an average score of 31.77 on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark (version 1), whereas the unquantized model achieves 31.88.
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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-Instruct) to INT8 data type.
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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 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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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.
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GPTQ used a 1% damping factor and 1,024 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/SmolLM-135M-Instruct-quantized.w8a8"
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sampling_params = SamplingParams(temperature=0.6, top_p=0.92, max_tokens=100)
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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messages = [
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{"role": "user", "content": "List the steps to bake a chocolate cake from scratch."},
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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 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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import random
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model_id = "HuggingFaceTB/SmolLM-135M-Instruct"
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num_samples = 1024
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max_seq_len = 2048
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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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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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)
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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("SmolLM-135M-Instruct-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/SmolLM-135M-Instruct-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>SmolLM-135M-Instruct-quantized</strong>
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</td>
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<td><strong>SmolLM-135M-Instruct-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>26.83
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</td>
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<td>26.45
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</td>
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<td>98.6%
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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>31.31
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</td>
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<td>31.14
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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>GSM-8K (5-shot, strict-match)
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</td>
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<td>0.68
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</td>
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<td>0.99
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</td>
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<td>144.4%
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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>40.57
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</td>
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<td>40.54
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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>Winogrande (5-shot)
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</td>
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<td>52.41
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</td>
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<td>51.54
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</td>
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<td>98.3%
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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>39.46
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</td>
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<td>39.97
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</td>
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<td>101.3%
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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>31.88</strong>
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</td>
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<td><strong>31.77</strong>
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</td>
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<td><strong>99.7%</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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version https://git-lfs.github.com/spec/v1
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size 1837
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configuration.json
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"pad_token_id": 2,
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"transformers_version": "4.44.0"
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}
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merges.txt
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version https://git-lfs.github.com/spec/v1
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size 466391
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:1f6ee9463560573bcccb6b48dfd89827ad9d49a4f50b6123a35b6f4efafb3baf
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size 219850592
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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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GPTQModifier:
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sequential_update: false
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dampening_frac: 0.01
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ignore: [lm_head]
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scheme: W8A8
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targets: Linear
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special_tokens_map.json
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{
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"additional_special_tokens": [
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"<|im_start|>",
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"<|im_end|>"
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],
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"bos_token": {
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"content": "<|im_start|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"eos_token": {
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"content": "<|im_end|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": {
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"content": "<|im_end|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"unk_token": {
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"content": "<|endoftext|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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}
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}
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tokenizer.json
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version https://git-lfs.github.com/spec/v1
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size 2104655
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version https://git-lfs.github.com/spec/v1
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oid sha256:75668bda11eb0d5ece137b4eb3fb2ad679f847fd19846b726369a26d92f9f28e
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size 3590
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version https://git-lfs.github.com/spec/v1
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oid sha256:82b84012e3add4d01d12ba14442026e49b8cbbaead1f79ecf3d919784f82dc79
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size 800662
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