Upload folder using huggingface_hub (#2)

- 2cf82aea2e540d0dca3d469dfea6ec500d1758f6d7142108b9f49e4d9221d2c8 (8bc6b4f3a247855612cf5c89d93e466ede8e8975)
- 8adce3cadc68b7c0cf774baa86bf6aa80131e77abb59a4c3da0e5c3188c93bdd (d024de7467cb1f5bbdc6c0f3bd93394bba534232)

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/WizardLM-WizardMath-7B-V1.0-bnb-8bit-smashed",
-                                                 trust_remote_code=True)
+                                                 trust_remote_code=True, device_map='auto')
     tokenizer = AutoTokenizer.from_pretrained("WizardLM/WizardMath-7B-V1.0")
     
     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/tmprbwbvhpl",
+  "_name_or_path": "/tmp/tmpa5e896_r",
   "architectures": [
     "LlamaForCausalLM"
   ],
@@ -21,7 +21,7 @@
   "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/modelsbc57hhgt",
+        "cache_dir": "/ceph/hdd/staff/charpent/.cache/modelsfsqan3l4",
         "batch_size": 1,
         "model_name": "WizardLM/WizardMath-7B-V1.0",
         "pruning_ratio": 0.0,