Upload folder using huggingface_hub (#2)

- 5a81b9512a97ae3f807b18ad7b6d3b0b7e0211622f2052b100fc4833b1122e59 (b079366e465ba546664a129574f180a9302159d1)
- e6e65bd42b035a880800ea4e551b6e69c8e3ae668121cb16dc44ae3a34784fcd (5b5aa287a2efeea39ef9817e89fe48a263dbef51)

README.md

@@ -1,5 +1,4 @@
 ---
-library_name: pruna-engine
 thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
 metrics:
 - memory_disk
@@ -8,6 +7,8 @@ metrics:
 - inference_throughput
 - inference_CO2_emissions
 - inference_energy_consumption
+tags:
+- pruna-ai
 ---
 <!-- header start -->
 <!-- 200823 -->
@@ -33,7 +34,7 @@ metrics:
 
 ## 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.
@@ -60,7 +61,7 @@ You can run the smashed model with these steps:
    from transformers import AutoModelForCausalLM, AutoTokenizer
 
     model = AutoModelForCausalLM.from_pretrained("PrunaAI/NousResearch-Yarn-Llama-2-7b-128k-bnb-4bit-smashed",
-                                                 trust_remote_code=True)
+                                                 trust_remote_code=True, device_map='auto')
     tokenizer = AutoTokenizer.from_pretrained("NousResearch/Yarn-Llama-2-7b-128k")
     
     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/tmplpkfk47m",
+  "_name_or_path": "/tmp/tmpskzue6ra",
   "architectures": [
     "LlamaForCausalLM"
   ],
@@ -23,7 +23,7 @@
   "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": [

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:7e33accbda4814e0eb09395194557c2b0de461d74e9adf61660cfbb9ec9a1407
-size 3866042099
+oid sha256:2b4ad05622d90d60ab374cdc4687ff57805026b7147b9169c52ef7e5824989ff
+size 4167722008

smash_config.json

@@ -8,7 +8,7 @@
         "compilers": "None",
         "task": "text_text_generation",
         "device": "cuda",
-        "cache_dir": "/ceph/hdd/staff/charpent/.cache/modelsz8prnsvt",
+        "cache_dir": "/ceph/hdd/staff/charpent/.cache/modelsd4pre5za",
         "batch_size": 1,
         "model_name": "NousResearch/Yarn-Llama-2-7b-128k",
         "pruning_ratio": 0.0,