Upload folder using huggingface_hub (#2)

- 0d69daf2fc7b570d378c57f2212dd92869960698168c773e12cf8c66d4c93cee (03260a86bbdc41a610c0cc564b7cc6c1ed173cc2)
- ef6738ce6434048751cadc2d0664b4352a8184d7a1059358fc45217728a9f27b (b1474db1e071f7f854ee4761e0ed053fb30b3ff9)
- 9175404317690a90526c818d764048465fae36a542b83b761093f64c3d2d6960 (43a87cfcd4409f2dfc74d936d692237f8fc66e1c)

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/maywell-Synatra-7B-v0.3-dpo-bnb-8bit-smashed",
-                                                 trust_remote_code=True)
+                                                 trust_remote_code=True, device_map='auto')
     tokenizer = AutoTokenizer.from_pretrained("maywell/Synatra-7B-v0.3-dpo")
     
     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/tmpyt7zc2ec",
+  "_name_or_path": "/tmp/tmp2vxlmn9g",
   "architectures": [
     "MistralForCausalLM"
   ],
@@ -18,7 +18,7 @@
   "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/models5f9f32qs",
+        "cache_dir": "/ceph/hdd/staff/charpent/.cache/modelsqf6t78l_",
         "batch_size": 1,
         "model_name": "maywell/Synatra-7B-v0.3-dpo",
         "pruning_ratio": 0.0,