Upload folder using huggingface_hub (#4)

- 556af2c007c01c17c1b06b5cd1c9424e4e43dbd7576e957a19e4b8f4047fe787 (9d95754234634339a699807f47fc4845a58a05a8)

README.md

@@ -34,7 +34,7 @@ tags:
 
 ## Results
 
-Detailed efficiency metrics coming soon!
+![image info](./plots.png)
 
 **Frequently Asked Questions**
 - ***How does the compression work?*** The model is compressed with llm-int8.
@@ -61,7 +61,7 @@ You can run the smashed model with these steps:
    from transformers import AutoModelForCausalLM, AutoTokenizer
 
     model = AutoModelForCausalLM.from_pretrained("PrunaAI/vicgalle-gpt2-alpaca-gpt4-bnb-8bit-smashed",
-                                                 trust_remote_code=True)
+                                                 trust_remote_code=True, device_map='auto')
     tokenizer = AutoTokenizer.from_pretrained("vicgalle/gpt2-alpaca-gpt4")
     
     input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]

config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "/tmp/tmpambkytgt",
+  "_name_or_path": "/tmp/tmpu_vrx355",
   "activation_function": "gelu_new",
   "architectures": [
     "GPT2LMHeadModel"
@@ -20,7 +20,7 @@
   "quantization_config": {
     "bnb_4bit_compute_dtype": "bfloat16",
     "bnb_4bit_quant_type": "fp4",
-    "bnb_4bit_use_double_quant": true,
+    "bnb_4bit_use_double_quant": false,
     "llm_int8_enable_fp32_cpu_offload": false,
     "llm_int8_has_fp16_weight": false,
     "llm_int8_skip_modules": [

smash_config.json

@@ -8,7 +8,7 @@
         "compilers": "None",
         "task": "text_text_generation",
         "device": "cuda",
-        "cache_dir": "/ceph/hdd/staff/charpent/.cache/models1gxfplcz",
+        "cache_dir": "/ceph/hdd/staff/charpent/.cache/modelslvqxvp4d",
         "batch_size": 1,
         "model_name": "vicgalle/gpt2-alpaca-gpt4",
         "pruning_ratio": 0.0,