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Model: neuralmagic/Llama-2-7b-chat-quantized.w8a8 Source: Original Platform
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README.md
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README.md
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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: llama2
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---
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# Llama-2-7b-chat-quantized.w8a8
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## Model Overview
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- **Model Architecture:** Llama-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 [Llama-2-7b-chat](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf), 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:** 7/2/2024
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- **Version:** 1.0
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- **License(s)**: [LLama2](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf/blob/main/LICENSE.txt)
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- **Model Developers:** Neural Magic
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Quantized version of [Llama-2-7b-chat](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf).
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It achieves an average score of 53.38 on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark (version 1), whereas the unquantized model achieves 53.41.
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### Model Optimizations
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This model was obtained by quantizing the weights of [Llama-2-7b-chat](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) 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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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 256 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/Llama-2-7b-chat-quantized.w8a8"
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number_gpus = 1
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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, tensor_parallel_size=number_gpus)
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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, add_generation_prompt=True, tokenize=False)
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llm = LLM(model=model_id, tensor_parallel_size=number_gpus)
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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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### Use with transformers
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The following example contemplates how the model can be deployed in Transformers using the `generate()` function.
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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model_id = "neuralmagic/Llama-2-7b-chat-quantized.w8a8"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype="auto",
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device_map="auto",
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)
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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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input_ids = tokenizer.apply_chat_template(
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messages,
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add_generation_prompt=True,
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return_tensors="pt"
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).to(model.device)
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terminators = [
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tokenizer.eos_token_id,
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tokenizer.convert_tokens_to_ids("<|eot_id|>")
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]
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outputs = model.generate(
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input_ids,
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max_new_tokens=256,
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eos_token_id=terminators,
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do_sample=True,
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temperature=0.6,
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top_p=0.9,
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)
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response = outputs[0][input_ids.shape[-1]:]
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print(tokenizer.decode(response, skip_special_tokens=True))
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```
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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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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 = "meta-llama/Llama-2-7b-chat-hf"
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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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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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trust_remote_code=True,
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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("Llama-2-7b-chat-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/Llama-2-7b-chat-quantized.w8a8",dtype=auto,gpu_memory_utilization=0.4,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \
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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>Llama-2-7b-chat </strong>
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</td>
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<td><strong>Llama-2-7b-chat-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>47.34
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</td>
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<td>47.04
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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>ARC Challenge (25-shot)
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</td>
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<td>53.24
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</td>
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<td>54.52
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</td>
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<td>102.4%
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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>23.20
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</td>
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<td>22.21
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</td>
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<td>95.8%
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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>78.65
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</td>
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<td>78.17
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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>Winogrande (5-shot)
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</td>
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<td>72.45
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</td>
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<td>72.85
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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>45.58
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</td>
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<td>45.50
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</td>
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<td>99.8%
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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>53.41</strong>
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</td>
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<td><strong>53.38</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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config.json
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{
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"_name_or_path": "/nm/drive0/alexandre/cache/hub/models--meta-llama--Llama-2-7b-chat-hf/snapshots/f5db02db724555f92da89c216ac04704f23d4590",
|
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"architectures": [
|
||||
"LlamaForCausalLM"
|
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],
|
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"attention_bias": false,
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": 1,
|
||||
"eos_token_id": 2,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 4096,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 11008,
|
||||
"max_position_embeddings": 4096,
|
||||
"mlp_bias": false,
|
||||
"model_type": "llama",
|
||||
"num_attention_heads": 32,
|
||||
"num_hidden_layers": 32,
|
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"num_key_value_heads": 32,
|
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"pretraining_tp": 1,
|
||||
"rms_norm_eps": 1e-05,
|
||||
"rope_scaling": null,
|
||||
"rope_theta": 10000.0,
|
||||
"tie_word_embeddings": false,
|
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"torch_dtype": "float16",
|
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"transformers_version": "4.42.3",
|
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"use_cache": true,
|
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"vocab_size": 32000,
|
||||
"quantization_config": {
|
||||
"config_groups": {
|
||||
"group_0": {
|
||||
"input_activations": {
|
||||
"block_structure": null,
|
||||
"dynamic": true,
|
||||
"group_size": null,
|
||||
"num_bits": 8,
|
||||
"observer": "memoryless",
|
||||
"observer_kwargs": {},
|
||||
"strategy": "token",
|
||||
"symmetric": true,
|
||||
"type": "int"
|
||||
},
|
||||
"output_activations": null,
|
||||
"targets": [
|
||||
"Linear"
|
||||
],
|
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"weights": {
|
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"block_structure": null,
|
||||
"dynamic": false,
|
||||
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|
||||
"num_bits": 8,
|
||||
"observer": "minmax",
|
||||
"observer_kwargs": {},
|
||||
"strategy": "channel",
|
||||
"symmetric": true,
|
||||
"type": "int"
|
||||
}
|
||||
}
|
||||
},
|
||||
"format": "int-quantized",
|
||||
"global_compression_ratio": 1.2397987000162187,
|
||||
"ignore": [
|
||||
"lm_head"
|
||||
],
|
||||
"kv_cache_scheme": null,
|
||||
"quant_method": "compressed-tensors",
|
||||
"quantization_status": "frozen",
|
||||
"sparsity_config": {
|
||||
"format": "dense",
|
||||
"global_sparsity": 1.2853574777183943,
|
||||
"registry_requires_subclass": false,
|
||||
"sparsity_structure": "unstructured"
|
||||
}
|
||||
}
|
||||
}
|
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configuration.json
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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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generation_config.json
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{
|
||||
"bos_token_id": 1,
|
||||
"do_sample": true,
|
||||
"eos_token_id": 2,
|
||||
"max_length": 4096,
|
||||
"pad_token_id": 0,
|
||||
"temperature": 0.6,
|
||||
"top_p": 0.9,
|
||||
"transformers_version": "4.42.3"
|
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}
|
||||
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||||
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||||
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|
||||
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|
||||
"model.norm.weight": "model-00002-of-00002.safetensors"
|
||||
}
|
||||
}
|
||||
11
recipe.yaml
Normal file
11
recipe.yaml
Normal file
@@ -0,0 +1,11 @@
|
||||
quant_stage:
|
||||
quant_modifiers:
|
||||
GPTQModifier:
|
||||
sequential_update: false
|
||||
dampening_frac: 1
|
||||
ignore: [lm_head]
|
||||
config_groups:
|
||||
group_0:
|
||||
targets: [Linear]
|
||||
weights: {num_bits: 8, type: int, symmetric: true, strategy: channel}
|
||||
input_activations: {num_bits: 8, type: int, symmetric: true, dynamic: true, strategy: token}
|
||||
3793
results_2024-07-04T17-06-35.145108.json
Normal file
3793
results_2024-07-04T17-06-35.145108.json
Normal file
File diff suppressed because it is too large
Load Diff
24
special_tokens_map.json
Normal file
24
special_tokens_map.json
Normal file
@@ -0,0 +1,24 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": "</s>",
|
||||
"unk_token": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
93392
tokenizer.json
Normal file
93392
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
43
tokenizer_config.json
Normal file
43
tokenizer_config.json
Normal file
@@ -0,0 +1,43 @@
|
||||
{
|
||||
"add_bos_token": true,
|
||||
"add_eos_token": false,
|
||||
"add_prefix_space": null,
|
||||
"added_tokens_decoder": {
|
||||
"0": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"1": {
|
||||
"content": "<s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"2": {
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"bos_token": "<s>",
|
||||
"chat_template": "{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 and system_message != false %}{% set content = '<<SYS>>\\n' + system_message + '\\n<</SYS>>\\n\\n' + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ bos_token + '[INST] ' + content.strip() + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ ' ' + content.strip() + ' ' + eos_token }}{% endif %}{% endfor %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "</s>",
|
||||
"legacy": false,
|
||||
"model_max_length": 1000000000000000019884624838656,
|
||||
"pad_token": "</s>",
|
||||
"padding_side": "right",
|
||||
"sp_model_kwargs": {},
|
||||
"tokenizer_class": "LlamaTokenizer",
|
||||
"unk_token": "<unk>",
|
||||
"use_default_system_prompt": false
|
||||
}
|
||||
Reference in New Issue
Block a user