Update README.md

README.md

@@ -1,6 +1,7 @@
 ---
 library_name: peft
 base_model: mistralai/Mistral-7B-v0.1
+license: apache-2.0
 ---
 
 # Model Card for MedMistral-7B
@@ -15,85 +16,98 @@ base_model: mistralai/Mistral-7B-v0.1
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+MedMistral-7B is a Mistral fine tune 180993 samples from the [medmcqa](https://huggingface.co/datasets/medmcqa)
+ dataset.
 
-
-
-- **Developed by:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **Developed by:** [segmed.ai](https://segmed.ai)
+- **Model type:** QLoRA Fine tune Mistral 7B
+- **License:** Apache 2.0
+- **Finetuned from model :**  [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) 
 
 ### Model Sources [optional]
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** https://github.com/segmed/med_mistral
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+This model is to demonstrate medical domain knowledge, but is not intended for medical advise. 
 
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
+Prompts used in training followed this format:
+```
+You are a helpful medical assistant. Your task is to answer the following question one of the options and explain why.
+### Question:  Turban epiglottitis is a clinical finding in - [0] Tubercular laryngitis [1] Tubercular pharyngitis [2] Polypoid degeneration of vocal cord [3] Subglottic hemangioma
+    
+### Answer: 0
+### Explanation: Ans. is 4a' i.e.. Tubercular laryngitis Laryngeal examination in TB lary ngitiso Hyperaemia of the vocal cord in its whole extent or confined to posterior part with impairment of adduction is the first sign.o Swelling in the interarytenoid region giving a mammilated appearance,o Ulceration of vocal cord giving mouse-nibbled appearance.o Superficial ragged ulceration on the arytenoids and interarytenoid region,o Granulation tissue in interarvtenoid region or vocal process of arytenoid,o Pseudoedema of the epiglottis 'Turban epiglottis",o Swelling of ventricular bands and aryepiglottic folds,o Marked pallor of surrounding mucosa.
+```
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+And for inference, you would need to remove the answer.
+```
+You are a helpful medical assistant. Your task is to answer the following question one of the options and explain why.
+### Question:  Turban epiglottitis is a clinical finding in - [0] Tubercular laryngitis [1] Tubercular pharyngitis [2] Polypoid degeneration of vocal cord [3] Subglottic hemangioma
+    
+### Answer: 
+```
 
-[More Information Needed]
+### Downstream Use
 
-### Out-of-Scope Use
+This model could be further fine tuned on your specific medical dataset.
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+This model is not intended for medical use. 
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
-
-[More Information Needed]
+Download the model and call generate_tokens using the prompt format defined above.
+```
+def generate_tokens(m, prompt, max_new_tokens=32):
+    model_input = tokenizer(prompt, return_tensors="pt").to("cuda")
+    m.eval()
+    with torch.no_grad():
+        return tokenizer.decode(m.generate(**model_input, max_new_tokens=max_new_tokens, do_sample=True, top_k=0, num_return_sequences=1, temperature=0.1, eos_token_id=tokenizer.eos_token_id)[0].cuda())
+```
+These are the hyper-parameters which yielded the best results when experimenting. 
 
 ## Training Details
 
 ### Training Data
 
-<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+180k samples from the [medmcqa](https://huggingface.co/datasets/medmcqa) were used for training. 914 samples were reserved for test and eval. 
 
-[More Information Needed]
+A small number of samples over 512 tokens were removed to simplify training and to keep the max tokens size small.
 
 ### Training Procedure 
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
+Training 
 
 #### Training Hyperparameters
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+- **Training regime:** This used the standard qLoRA peft parameters as defined by
+```
+peft_config = LoraConfig(
+        r=16,
+        lora_alpha=16,
+        lora_dropout=0.05,
+        bias="none",
+        task_type="CAUSAL_LM",
+        target_modules=[
+        "q_proj",
+        "k_proj",
+        "v_proj",
+        "o_proj",
+        "gate_proj",
+        "up_proj",
+        "down_proj",
+        "lm_head",
+    ]
+    )
+```
 
 #### Speeds, Sizes, Times [optional]
 
@@ -103,119 +117,10 @@ Use the code below to get started with the model.
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Data Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-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]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
-
-
-## Training procedure
-
-
-The following `bitsandbytes` quantization config was used during training:
-- quant_method: bitsandbytes
-- load_in_8bit: False
-- load_in_4bit: True
-- llm_int8_threshold: 6.0
-- llm_int8_skip_modules: None
-- llm_int8_enable_fp32_cpu_offload: False
-- llm_int8_has_fp16_weight: False
-- bnb_4bit_quant_type: nf4
-- bnb_4bit_use_double_quant: True
-- bnb_4bit_compute_dtype: bfloat16
+Evaluation was performed on the holdout of 914 samples from the medmcqa dataset. Given the multiple choice nature of the data, the F1 was computed and explanations thrown away.
 
-### Framework versions
+F1 medmcqa : 66%
 
+Evaluation was also performed on the [pubmed_qa](https://huggingface.co/datasets/pubmed_qa) labeled dataset. Since Yes/No answers were provided, an F1 was calculated. I waant to point out this evaluation is on a completely different dataset with different prompt format.
 
-- PEFT 0.7.0.dev0
+F1 pubmed_qa: