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- b8c18c3cd2c60ec3be4ce18201d524e9105da816b53728ce1eb6dcbcdaeba920 (1b6b23a1829e01efe10e4fde75555ac7fe62626b)
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Files changed (5) hide show
  1. README.md +5 -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.
@@ -60,12 +61,13 @@ 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/togethercomputer-GPT-JT-6B-v1-bnb-4bit-smashed",
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- trust_remote_code=True)
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  tokenizer = AutoTokenizer.from_pretrained("togethercomputer/GPT-JT-6B-v1")
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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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  outputs = model.generate(input_ids, max_new_tokens=216)
 
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  ```
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  ## Configurations
 
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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/togethercomputer-GPT-JT-6B-v1-bnb-4bit-smashed",
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+ trust_remote_code=True, device_map='auto')
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  tokenizer = AutoTokenizer.from_pretrained("togethercomputer/GPT-JT-6B-v1")
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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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  outputs = model.generate(input_ids, max_new_tokens=216)
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+ tokenizer.decode(outputs[0])
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  ```
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  ## Configurations
config.json CHANGED
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  {
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- "_name_or_path": "/tmp/tmp6bq5j4sh",
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  "activation_function": "gelu_new",
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  "architectures": [
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  "GPTJForCausalLM"
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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/tmps08uljpf",
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  "activation_function": "gelu_new",
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  "architectures": [
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  "GPTJForCausalLM"
 
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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
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  version https://git-lfs.github.com/spec/v1
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- oid sha256:edf78d3adf4818124d02b29aa9694a36e028c73f3c3a49998d86fc35e9819d95
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- size 3735846402
 
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  version https://git-lfs.github.com/spec/v1
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+ oid sha256:8cb0afced4b790d2eae8aa6623e91d404fce5acf688ac784ac1fc5993f65d000
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+ size 3998485888
plots.png ADDED
smash_config.json CHANGED
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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/modelsu50m47ja",
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  "batch_size": 1,
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  "model_name": "togethercomputer/GPT-JT-6B-v1",
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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/models17a99hiq",
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  "batch_size": 1,
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  "model_name": "togethercomputer/GPT-JT-6B-v1",
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  "pruning_ratio": 0.0,