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Model: cogbuji/Mr-Grammatology-clinical-problems-Mistral-7B-0.5
Source: Original Platform

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README.md

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+---
+base_model: teknium/OpenHermes-2.5-Mistral-7B
+license: mit
+language:
+- en
+model_creator: Chime Ogbuji
+library_name: mlx
+model_name: Mr-Grammatology-clinical-problems-Mistral-7B-0.5
+pipeline_tag: text-generation
+prompt_template: '<|im_start|>system
+
+  {system_message}<|im_end|>
+
+  <|im_start|>user
+
+  {prompt}<|im_end|>
+
+  <|im_start|>assistant
+'
+tags:
+- mlx
+- medical
+- health  
+- mistral
+- instruct
+- finetune
+- chatml
+---
+
+# Mr-Grammatology-clinical-problems-Mistral-7B-0.5
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/651d96a3e8c4c2ebaafc1e7d/uyiryuBhU4y62f4CRxabO.png)
+
+The name of the model is a homage to Fela Kuti's song __Mr Grammarticalogy-Lisationalsim Is The Boss__ released on the B-side of his 1976 LP [Excuse O](https://www.discogs.com/release/3149841-Fela-And-The-Africa-70-Excuse-O).
+
+It is a 16/32 QLoRa all linear layers finetune of [teknium/OpenHermes-2.5-Mistral-7B](/teknium/OpenHermes-2.5-Mistral-7B) using [controlled natural language (CNL) phrases](https://github.com/chimezie/django-snomed-ct#controlled-natural-language) 
+generated from the September 23rd release of [SNOMED CT United States Edition](https://www.snomed.org/snomed-ct/Use-SNOMED-CT).  The general idea is described in [Domain-Specific Biomedical Ontologies, RALM, and Generative Medical Expert Systems](https://chimezie.medium.com/biomedical-ontology-retrieval-augmented-language-models-using-django-snomed-ct-and-ogbujipt-dfa0d0b150d8).
+
+It is an experimental model for non-production environments to test how generative AI systems can be trained for use in various medical informatics scenarios.
+
+The original model was converted to MLX format, quantized, and then subject to continued pretraining using all the active domain-expert text definitions available in SNOMED-CT at a constant learning rate of 1e-5 using 
+[mlx_lm's LoRa finetuning functionality](https://github.com/ml-explore/mlx-examples/blob/main/llms/mlx_lm/LORA.md) with 16 LoRa layers.  
+
+It was then trained on a dataset of 336,762 records of medical terminology **definition instructions** generated from SNOMED-CT using a fork of [django-snomed-ct](https://github.com/chimezie/django-snomed-ct).  These definition instructions were generated from the **disorder**, **finding**, **morphological abnormality**, and **situation** hierarchies in SNOMED-CT.  This training step was done using [mlx-tuning-fork](https://github.com/chimezie/mlx-tuning-fork) through 42,096 training iterations, with a batch size of 8 at a time, using LoRa on all linear layers.
+
+There were 51,082 records of more granular definition instructions, part of which were incorporated into the training dataset.  However, 40% were kept aside for validation.  
+
+## Use with mlx
+
+```bash
+pip install mlx-lm
+```
+
+Download and convert.
+
+```bash
+$ python -m mlx_lm.convert --hf-path cogbuji/Mr-Grammatology-clinical-problems-Mistral-7B-0.5 \
+         --mlx-path /path/to/mlx/model
+```
+
+Generate from prompts in commandline (see [Generate Text with LLMs and MLX](https://github.com/ml-explore/mlx-examples/tree/main/llms) for more options )
+
+```bash
+$ python -m mlx_lm.generate --prompt "How is Cardiomyopathy characterized in form?" \
+         --temp .4 -m 300 --model /path/to/mlx/model --seed 4
+```
+
+```
+Prompt: <|im_start|>user
+How is Cardiomyopathy characterized in form?<|im_end|>
+<|im_start|>assistant
+
+Cardiomyopathy is characterized in form by a morphologically abnormal structure located in a myocardium structure
+```
+
+## Example of use of 1-shot description prompting
+Using mlx-tuning-fork with OgbujiPts word looms to construct 1-shot example of description prompting.
+
+```bash
+$ python -m mlx_tuning_fork.training -nt 1200 -t .4 --loom-file=sct_prompt.toml -f chatml config.yaml
+
+```
+
+```
+Prompt: <|im_start|>system
+Give detailed responses.  Use critical thinking and think step-by-step.
+Don't make up answers if you don't know. Use SNOMED-CT definitions as much as possible, describing 
+characterizations in form of morphological abnormalities involved in diagnoses (their signs), their etiology (causes/basis),  
+their related findings. 
+## Question ##
+What are the signs and etiology of Skin ulcer due to diabetes mellitus?
+
+## Answer ##
+SNOMED-CT defines Skin ulcer due to diabetes mellitus as characterized in form by an ulcer located in a skin structure.  
+Ulcer is a mophologic abnormality.  
+The cause or origin of a disorder is part of its etiology. 
+Skin ulcer due to diabetes mellitus is caused by diabetes mellitus.  
+So, the etiology of Skin ulcer due to diabetes mellitus is diabetes mellitus.
+   
+Infected ulcer of skin is an infectious process characterized in form by an ulcer located in a skin structure. 
+Since the signs of Skin ulcer due to diabetes mellitus is also an ulcer, Infected ulcer of skin is 
+an example of a disorder with the same signs as Skin ulcer due to diabetes mellitus.
+
+<|im_end|>
+<|im_start|>user
+
+Take a deep breath and work on this problem step by step. 
+## Question ##
+The patient has Irritable bowel syndrome.
+Summarize the the biological processes and signs (characterizations in form)
+involved in the patient's diagnoses. Give examples of other disorders with the same signs and etiology.
+<|im_end|>
+<|im_start|>assistant
+
+Irritable bowel syndrome (SNOMED-CT definition: Irritable bowel syndrome) is a functional disorder of the gastrointestinal tract, characterized in form by abnormal bowel movements. It is a clinical diagnosis, and its signs and symptoms include abdominal pain, bloating, and altered bowel movements (diarrhea, constipation, or both).
+
+Etiology: Irritable bowel syndrome is believed to be caused by factors including abnormal gut motility, gastrointestinal dysrhythmias, and psychological factors.
+
+Signs: Examples of signs of the disorder include changes in stool consistency and abdominal pain.
+
+Irritable bowel syndrome is a functional bowel disorder, which is a category of gastrointestinal disorders with similar signs and symptoms. Examples of disorders with the same signs and etiology include:
+
+1. Functional dyspepsia: It is characterized by an upper abdominal pain or discomfort and has a similar etiology as irritable bowel syndrome. It is a functional disorder of the stomach and small intestine, and its signs include epigastric pain and discomfort.
+
+2. Chronic idiopathic constipation: It is characterized by chronic constipation and has a similar etiology as irritable bowel syndrome. It is a functional disorder of the colon
+==========
+Prompt: 447.658 tokens-per-sec
+
+```

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+    "model_type": "mistral"
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+}

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,61 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32000": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32001": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [],
+  "bos_token": "<s>",
+  "chat_template": "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "legacy": true,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": null,
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "LlamaTokenizer",
+  "trust_remote_code": false,
+  "unk_token": "<unk>",
+  "use_default_system_prompt": true,
+  "use_fast": true
+}

transformers_inference.py

@@ -0,0 +1,32 @@
+# Code to inference Open Hermes 2.5 with HF Transformers
+# Requires pytorch, transformers, bitsandbytes, sentencepiece, protobuf, and flash-attn packages
+
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from transformers import LlamaTokenizer, LlamaForCausalLM, MistralForCausalLM
+import bitsandbytes, flash_attn
+
+tokenizer = LlamaTokenizer.from_pretrained('teknium/OpenHermes-2.5-Mistral-7B', trust_remote_code=True)
+model = MistralForCausalLM.from_pretrained(
+    "teknium/OpenHermes-2.5-Mistral-7B",
+    torch_dtype=torch.float16,
+    device_map="auto",#{'': 'cuda:0'},
+    load_in_8bit=False,
+    load_in_4bit=True,
+    use_flash_attention_2=True
+)
+
+prompts = [
+    """<|im_start|>system
+You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.<|im_end|>
+<|im_start|>user
+Write a short story about Goku discovering kirby has teamed up with Majin Buu to destroy the world.<|im_end|>
+<|im_start|>assistant""",
+    ]
+
+for chat in prompts:
+    print(chat)
+    input_ids = tokenizer(chat, return_tensors="pt").input_ids.to("cuda")
+    generated_ids = model.generate(input_ids, max_new_tokens=750, temperature=0.8, repetition_penalty=1.1, do_sample=True, eos_token_id=tokenizer.eos_token_id)
+    response = tokenizer.decode(generated_ids[0][input_ids.shape[-1]:], skip_special_tokens=True, clean_up_tokenization_space=True)
+    print(f"Response: {response}")