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Files changed (4) hide show
  1. README.md +5 -3
  2. config.json +2 -2
  3. plots.png +0 -0
  4. 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/Arotte-gpt2-small-mt-bnb-8bit-smashed",
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- trust_remote_code=True)
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  tokenizer = AutoTokenizer.from_pretrained("Arotte/gpt2-small-mt")
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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/Arotte-gpt2-small-mt-bnb-8bit-smashed",
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+ trust_remote_code=True, device_map='auto')
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  tokenizer = AutoTokenizer.from_pretrained("Arotte/gpt2-small-mt")
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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
@@ -1,5 +1,5 @@
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  {
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- "_name_or_path": "/tmp/tmpfj0f_hnr",
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  "activation_function": "gelu_new",
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  "architectures": [
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  "GPT2LMHeadModel"
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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/tmp7164n9d5",
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  "activation_function": "gelu_new",
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  "architectures": [
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  "GPT2LMHeadModel"
 
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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": [
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/modelsuof3s2a1",
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  "batch_size": 1,
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  "model_name": "Arotte/gpt2-small-mt",
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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/modelsiif4fyb9",
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  "batch_size": 1,
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  "model_name": "Arotte/gpt2-small-mt",
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  "pruning_ratio": 0.0,