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Browse files- 79e30e5680eed62417cff37072fe7c5f6ca619b074462c1f23735fda4528c997 (ef41dcd30e3e0551c82ba6ccc6484d84814c4d51)
- f95fd345e0dcea7f99543184cbbdd00d1c0c66a74ed2b39ec66523139a9d0f72 (75f4f566b757a5df9b0a0159820ef13ce1507c8b)
- README.md +4 -3
- config.json +2 -2
- model-00001-of-00002.safetensors +2 -2
- model-00002-of-00002.safetensors +2 -2
- model.safetensors.index.json +0 -0
- plots.png +0 -0
- smash_config.json +1 -1
README.md
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---
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library_name: pruna-engine
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thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
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metrics:
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- memory_disk
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- inference_throughput
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- inference_CO2_emissions
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- inference_energy_consumption
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---
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<!-- header start -->
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<!-- 200823 -->
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## Results
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**Frequently Asked Questions**
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- ***How does the compression work?*** The model is compressed with llm-int8.
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("PrunaAI/facebook-xglm-7.5B-bnb-4bit-smashed",
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trust_remote_code=True)
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tokenizer = AutoTokenizer.from_pretrained("facebook/xglm-7.5B")
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input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
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---
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thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
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metrics:
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- memory_disk
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- inference_throughput
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- inference_CO2_emissions
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- inference_energy_consumption
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tags:
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- pruna-ai
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---
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<!-- header start -->
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<!-- 200823 -->
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## Results
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![image info](./plots.png)
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**Frequently Asked Questions**
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- ***How does the compression work?*** The model is compressed with llm-int8.
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("PrunaAI/facebook-xglm-7.5B-bnb-4bit-smashed",
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trust_remote_code=True, device_map='auto')
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tokenizer = AutoTokenizer.from_pretrained("facebook/xglm-7.5B")
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input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
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config.json
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{
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"_name_or_path": "/tmp/
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"activation_dropout": 0.0,
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"activation_function": "gelu",
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"architectures": [
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"quantization_config": {
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"bnb_4bit_compute_dtype": "bfloat16",
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"bnb_4bit_quant_type": "fp4",
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"bnb_4bit_use_double_quant":
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"llm_int8_enable_fp32_cpu_offload": false,
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"llm_int8_has_fp16_weight": false,
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"llm_int8_skip_modules": [
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{
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"_name_or_path": "/tmp/tmp_vn5a6ar",
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"activation_dropout": 0.0,
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"activation_function": "gelu",
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"architectures": [
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"quantization_config": {
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"bnb_4bit_compute_dtype": "bfloat16",
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"bnb_4bit_quant_type": "fp4",
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"bnb_4bit_use_double_quant": false,
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"llm_int8_enable_fp32_cpu_offload": false,
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"llm_int8_has_fp16_weight": false,
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"llm_int8_skip_modules": [
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model-00001-of-00002.safetensors
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model-00002-of-00002.safetensors
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model.safetensors.index.json
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plots.png
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smash_config.json
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"compilers": "None",
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"task": "text_text_generation",
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"device": "cuda",
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"cache_dir": "/ceph/hdd/staff/charpent/.cache/
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"batch_size": 1,
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"model_name": "facebook/xglm-7.5B",
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"pruning_ratio": 0.0,
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"compilers": "None",
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"task": "text_text_generation",
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"device": "cuda",
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"cache_dir": "/ceph/hdd/staff/charpent/.cache/modelsawbplsun",
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"batch_size": 1,
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"model_name": "facebook/xglm-7.5B",
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"pruning_ratio": 0.0,
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