Update README.md

README.md

@@ -34,9 +34,9 @@ Trying to get better at medical Q & A
 ### Model Sources [optional]
 
 
-- **Repository:** [Tonic/mistralmed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [Tonic/MistralMed_Chat]
+- **Repository:** [Tonic/mistralmed](https://huggingface.co/Tonic/mistralmed)
+- **Code :** [github](https://github.com/Josephrp/mistralmed/blob/main/finetuning.py)
+- **Demo :** [Tonic/MistralMed_Chat](https://huggingface.co/Tonic/MistralMed_Chat)
 
 ## Uses
 
@@ -79,7 +79,8 @@ Dataset({
     num_rows: 16407
 })
 
-[2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 12628, 264, 2718, 12271, 5122, 272, 14164, 5746, 9283, 302, 272, 2787, 12271, 390, 264, 2692, 908, 395, 9623, 304, 6836, 3069, 28723, 13, 3260, 908, 1023, 6685, 272, 2718, 1423, 24329, 304, 272, 908, 1580, 347, 624, 302, 272, 2296, 5936, 262, 674, 647, 464, 3134, 647, 464, 28721, 495, 28730, 410, 262, 296, 647, 464, 19928, 647, 464, 14876, 28730, 9122, 647, 464, 28713, 16939, 647, 464, 3134, 28730, 720, 11009, 352, 647, 464, 267, 1805, 416, 647, 464, 3134, 28730, 9122, 14303, 13, 1014, 9623, 1580, 347, 624, 302, 272, 2296, 28747, 5936, 861, 647, 464, 5128, 28730, 11023, 28730, 1408, 647, 464, 11023, 28730, 4395, 647, 464, 16239, 263, 647, 464, 274, 9312, 647, 464, 28599, 647, 464, 2383, 411, 647, 464, 7449, 28730, 4837, 8524, 647, 464, 3537, 28730, 13102, 7449, 647, 464, 10470, 28713, 647, 464, 13952, 28730, 266, 28730, 2453, 314, 647, 464, 3537, 28730, 8502, 28730, 11023, 647, 464, 3537, 28730, 7502, 28730, 11023, 647, 464, 4101, 3591, 1421, 13, 13, 27332, 15255, 12271, 28747, 13, 3195, 460, 272, 19724, 354, 393, 1082, 721, 402, 4475, 294, 689, 6519, 300, 3250, 17428, 325, 9162, 28755, 28731, 1550, 13, 13, 27332, 11736, 288, 9283, 28747, 13, 28741, 331, 19742, 1683, 288, 17428, 28725, 481, 358, 721, 282, 17428, 28725, 442, 1683, 20837, 636, 721, 282, 17428, 6948, 6556, 1837, 304, 27729, 5827, 2818, 356, 2425, 472, 28723, 23331, 28733, 21255, 314, 3076, 695, 10747, 28725, 1259, 390, 16779, 294, 8731, 17653, 28725, 993, 347, 4525, 916, 2948, 10139, 28723, 5800, 7193, 506, 4894, 369, 13147, 494, 361, 262, 28725, 264, 7876, 1307, 298, 3363, 2856, 799, 7692, 282, 18257, 28725, 349, 5645, 1835, 393, 15155, 28790, 297, 11781, 311, 28725, 736, 349, 708, 6740, 5566, 298, 1760, 871, 11935, 938, 354, 5827, 302, 393, 15155, 297, 10589, 28723, 13, 2]
+
+#### Preprocessing [optional]
 
 MistralForCausalLM(
   (model): MistralModel(
@@ -108,10 +109,6 @@ MistralForCausalLM(
   (lm_head): Linear(in_features=4096, out_features=32000, bias=False)
 )
 
-#### Preprocessing [optional]
-
-[More Information Needed]
-
 
 #### Training Hyperparameters
 
@@ -136,9 +133,9 @@ config = LoraConfig(
 
 #### Speeds, Sizes, Times [optional]
 
-trainable params: 21260288 || all params: 3773331456 || trainable%: 0.5634354746703705
-TrainOutput(global_step=1000, training_loss=0.47226515007019043, metrics={'train_runtime': 3143.4141, 'train_samples_per_second': 2.545, 'train_steps_per_second': 0.318, 'total_flos': 1.75274075357184e+17, 'train_loss': 0.47226515007019043, 'epoch': 0.49})
-[More Information Needed]
+- trainable params: 21260288 || all params: 3773331456 || trainable%: 0.5634354746703705
+- TrainOutput(global_step=1000, training_loss=0.47226515007019043, metrics={'train_runtime': 3143.4141, 'train_samples_per_second': 2.545, 'train_steps_per_second': 0.318, 'total_flos': 1.75274075357184e+17, 'train_loss': 0.47226515007019043, 'epoch': 0.49})
+
 
 ## Environmental Impact
 
@@ -146,11 +143,11 @@ TrainOutput(global_step=1000, training_loss=0.47226515007019043, metrics={'train
 
 Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+- **Hardware Type:** A100
+- **Hours used:** 1
+- **Cloud Provider:** Google
+- **Compute Region:** East1
+- **Carbon Emitted:** 0.09
 
 ## Training Results