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README.md

@@ -18,7 +18,7 @@ tags:
 - biology
 - mlx
 datasets:
-- health_fact
+- pubmed
 base_model:
 - BioMistral/BioMistral-7B
 - mistralai/Mistral-7B-Instruct-v0.1
@@ -26,79 +26,17 @@ pipeline_tag: text-generation
 ---
 
 # abhishek-ch/biomistral-7b-synthetic-ehr
-
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/6460910f455531c6be78b2dd/tGtYB0b3eS7A4zbqp1xz0.png)
-
-
 This model was converted to MLX format from [`BioMistral/BioMistral-7B-DARE`]().
 Refer to the [original model card](https://huggingface.co/BioMistral/BioMistral-7B-DARE) for more details on the model.
-
-
 ## Use with mlx
 
 ```bash
 pip install mlx-lm
 ```
 
-The model was LoRA fine-tuned on [health_facts](https://huggingface.co/datasets/health_fact) and 
-Synthetic EHR dataset inspired by MIMIC-IV using the format below, for 1000 steps (~1M tokens) using mlx.
-
 ```python
-def format_prompt(prompt:str, question: str) -> str:
-    return """<s>[INST]
-## Instructions
-{}
-## User Question
-{}.
-[/INST]</s> 
-""".format(prompt, question)
-```
-
-Example For Synthetic EHR Diagnosis System Prompt
-```
-You are an expert in provide diagnosis summary based on clinical notes inspired by MIMIC-IV-Note dataset.
-These notes encompass Chief Complaint along with Patient Summary & medical admission details.
-```
-
-Example for Healthfacts Check System Prompt
-```
-You are a Public Health AI Assistant. You can do the fact-checking of public health claims. \nEach answer labelled with true, false, unproven or mixture. \nPlease provide the reason behind the answer
-```
+from mlx_lm import load, generate
 
-## Loading the model using `mlx`
-
-```python
-from mlx_lm import generate, load
 model, tokenizer = load("abhishek-ch/biomistral-7b-synthetic-ehr")
-response = generate(
-    fused_model,
-    fused_tokenizer,
-    prompt=format_prompt(prompt, question),
-    verbose=True,  # Set to True to see the prompt and response
-    temp=0.0,
-    max_tokens=512,
-)
-```
-
-## Loading the model using `transformers`
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-repo_id = "abhishek-ch/biomistral-7b-synthetic-ehr"
-
-tokenizer = AutoTokenizer.from_pretrained(repo_id)
-model = AutoModelForCausalLM.from_pretrained(repo_id)
-model.to("mps")
-
-input_text = format_prompt(system_prompt, question)
-input_ids = tokenizer(input_text, return_tensors="pt").to("mps")
-
-outputs = model.generate(
-    **input_ids,
-    max_new_tokens=512,
-)
-print(tokenizer.decode(outputs[0]))
-
+response = generate(model, tokenizer, prompt="hello", verbose=True)
 ```
-

model-00003-of-00003.safetensors

@@ -1,3 +1,3 @@
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+oid sha256:7ce4b3720a2fb5c47e9129cd08a5cce042d30d65a13b9356b3cba34495bfe57a
 size 3869410022