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Update README.md

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@@ -1,10 +1,11 @@
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  ---
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  license: llama3
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- library_name: peft
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  tags:
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- - trl
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- - sft
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  - generated_from_trainer
 
 
 
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  base_model: meta-llama/Meta-Llama-3-8B-Instruct
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  thumbnail: https://collaiborate.com/logo/logo-blue-bg-1.png
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  model-index:
@@ -74,13 +75,10 @@ Collaiborator-MEDLLM-Llama-3-8b-v1 was trained using an NVIDIA A40 GPU, which pr
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  ## How to use
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  import transformers
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-
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  import torch
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-
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  model_id = "collaiborateorg/Collaiborator-MEDLLM-Llama-3-8B-v1"
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-
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  pipeline = transformers.pipeline(
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  "text-generation",
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  model=model_id,
@@ -88,26 +86,22 @@ pipeline = transformers.pipeline(
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  device_map="auto",
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  )
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-
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  messages = [
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  {"role": "system", "content": "You are an expert trained on healthcare and biomedical domain!"},
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  {"role": "user", "content": "I'm a 35-year-old male and for the past few months, I've been experiencing fatigue, increased sensitivity to cold, and dry, itchy skin. What is the diagnosis here?"},
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  ]
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-
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  prompt = pipeline.tokenizer.apply_chat_template(
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  messages,
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  tokenize=False,
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  add_generation_prompt=True
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  )
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-
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  terminators = [
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  pipeline.tokenizer.eos_token_id,
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  pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
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  ]
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-
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  outputs = pipeline(
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  prompt,
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  max_new_tokens=256,
@@ -116,7 +110,6 @@ outputs = pipeline(
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  temperature=0.6,
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  top_p=0.9,
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  )
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-
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  print(outputs[0]["generated_text"][len(prompt):])
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  ### Contact Information
@@ -131,7 +124,7 @@ Website: https://www.collaiborate.com
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  The following hyperparameters were used during training:
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  - learning_rate: 0.0002
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- - train_batch_size: 8
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  - eval_batch_size: 8
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  - seed: 42
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  - gradient_accumulation_steps: 4
@@ -139,6 +132,7 @@ The following hyperparameters were used during training:
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: cosine
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  - lr_scheduler_warmup_ratio: 0.03
 
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  - mixed_precision_training: Native AMP
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  ### Framework versions
@@ -151,7 +145,7 @@ The following hyperparameters were used during training:
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  ### Citation
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- If you use Collaiborator-MEDLLM-Llama-3-8b-v1 in your research or applications, please cite it as follows:
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  @misc{Collaiborator_MEDLLM,
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  author = Collaiborator,
 
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  ---
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  license: llama3
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+ library_name: transformers
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  tags:
 
 
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  - generated_from_trainer
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+ - medical
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+ - Healthcare & Lifesciences
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+ - BioMed
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  base_model: meta-llama/Meta-Llama-3-8B-Instruct
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  thumbnail: https://collaiborate.com/logo/logo-blue-bg-1.png
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  model-index:
 
75
  ## How to use
76
 
77
  import transformers
 
78
  import torch
79
 
 
80
  model_id = "collaiborateorg/Collaiborator-MEDLLM-Llama-3-8B-v1"
81
 
 
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  pipeline = transformers.pipeline(
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  "text-generation",
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  model=model_id,
 
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  device_map="auto",
87
  )
88
 
 
89
  messages = [
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  {"role": "system", "content": "You are an expert trained on healthcare and biomedical domain!"},
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  {"role": "user", "content": "I'm a 35-year-old male and for the past few months, I've been experiencing fatigue, increased sensitivity to cold, and dry, itchy skin. What is the diagnosis here?"},
92
  ]
93
 
 
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  prompt = pipeline.tokenizer.apply_chat_template(
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  messages,
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  tokenize=False,
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  add_generation_prompt=True
98
  )
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  terminators = [
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  pipeline.tokenizer.eos_token_id,
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  pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
103
  ]
104
 
 
105
  outputs = pipeline(
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  prompt,
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  max_new_tokens=256,
 
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  temperature=0.6,
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  top_p=0.9,
112
  )
 
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  print(outputs[0]["generated_text"][len(prompt):])
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  ### Contact Information
 
124
 
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  The following hyperparameters were used during training:
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  - learning_rate: 0.0002
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+ - train_batch_size: 12
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  - eval_batch_size: 8
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  - seed: 42
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  - gradient_accumulation_steps: 4
 
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: cosine
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  - lr_scheduler_warmup_ratio: 0.03
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+ - training_steps: 2000
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  - mixed_precision_training: Native AMP
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  ### Framework versions
 
145
 
146
  ### Citation
147
 
148
+ If you use Collaiborator-MEDLLM-Llama-3-8b in your research or applications, please cite it as follows:
149
 
150
  @misc{Collaiborator_MEDLLM,
151
  author = Collaiborator,