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- 50be082d2df1d750fa7c25ebf54e73dda080ceb1f8541e48e5f014897a770da2 (2dce4ec70c3768f6c2e706841339abe798ce2db3)
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Files changed (5) hide show
  1. README.md +4 -3
  2. config.json +2 -2
  3. model.safetensors +2 -2
  4. plots.png +0 -0
  5. smash_config.json +1 -1
README.md CHANGED
@@ -1,5 +1,4 @@
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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
@@ -8,6 +7,8 @@ metrics:
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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 -->
@@ -33,7 +34,7 @@ metrics:
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  ## Results
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- Detailed efficiency metrics coming soon!
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  **Frequently Asked Questions**
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  - ***How does the compression work?*** The model is compressed with llm-int8.
@@ -60,7 +61,7 @@ You can run the smashed model with these steps:
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  from transformers import AutoModelForCausalLM, AutoTokenizer
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  model = AutoModelForCausalLM.from_pretrained("PrunaAI/elyza-ELYZA-japanese-Llama-2-7b-fast-instruct-bnb-4bit-smashed",
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- trust_remote_code=True)
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  tokenizer = AutoTokenizer.from_pretrained("elyza/ELYZA-japanese-Llama-2-7b-fast-instruct")
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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/elyza-ELYZA-japanese-Llama-2-7b-fast-instruct-bnb-4bit-smashed",
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+ trust_remote_code=True, device_map='auto')
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  tokenizer = AutoTokenizer.from_pretrained("elyza/ELYZA-japanese-Llama-2-7b-fast-instruct")
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  input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
config.json CHANGED
@@ -1,5 +1,5 @@
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  {
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- "_name_or_path": "/tmp/tmp3u4zs3ol",
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  "architectures": [
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  "LlamaForCausalLM"
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  ],
@@ -21,7 +21,7 @@
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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": true,
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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/tmprl_sn264",
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  "architectures": [
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  "LlamaForCausalLM"
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  ],
 
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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": [
model.safetensors CHANGED
@@ -1,3 +1,3 @@
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  version https://git-lfs.github.com/spec/v1
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- oid sha256:f31cf5afd7be5b2c2613f233d71f74458d7d14842b6e361abc95f9bb87c2d71c
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- size 4079738694
 
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  version https://git-lfs.github.com/spec/v1
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+ oid sha256:4fdf7b8c0b5ac1bdd69015313f1cd3033328276e439bbe08a20d6242c1b5ca05
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+ size 4381418664
plots.png ADDED
smash_config.json CHANGED
@@ -8,7 +8,7 @@
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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/modelswqzii5iw",
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  "batch_size": 1,
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  "model_name": "elyza/ELYZA-japanese-Llama-2-7b-fast-instruct",
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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/models1mb_ajzj",
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  "batch_size": 1,
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  "model_name": "elyza/ELYZA-japanese-Llama-2-7b-fast-instruct",
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  "pruning_ratio": 0.0,