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README.md

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+Quantization made by Richard Erkhov.
+
+[Github](https://github.com/RichardErkhov)
+
+[Discord](https://discord.gg/pvy7H8DZMG)
+
+[Request more models](https://github.com/RichardErkhov/quant_request)
+
+
+Collaiborator-MEDLLM-Llama-3-8B-v2 - GGUF
+- Model creator: https://huggingface.co/collaiborateorg/
+- Original model: https://huggingface.co/collaiborateorg/Collaiborator-MEDLLM-Llama-3-8B-v2/
+
+
+| Name | Quant method | Size |
+| ---- | ---- | ---- |
+| [Collaiborator-MEDLLM-Llama-3-8B-v2.Q2_K.gguf](https://huggingface.co/RichardErkhov/collaiborateorg_-_Collaiborator-MEDLLM-Llama-3-8B-v2-gguf/blob/main/Collaiborator-MEDLLM-Llama-3-8B-v2.Q2_K.gguf) | Q2_K | 2.96GB |
+| [Collaiborator-MEDLLM-Llama-3-8B-v2.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/collaiborateorg_-_Collaiborator-MEDLLM-Llama-3-8B-v2-gguf/blob/main/Collaiborator-MEDLLM-Llama-3-8B-v2.IQ3_XS.gguf) | IQ3_XS | 3.28GB |
+| [Collaiborator-MEDLLM-Llama-3-8B-v2.IQ3_S.gguf](https://huggingface.co/RichardErkhov/collaiborateorg_-_Collaiborator-MEDLLM-Llama-3-8B-v2-gguf/blob/main/Collaiborator-MEDLLM-Llama-3-8B-v2.IQ3_S.gguf) | IQ3_S | 3.43GB |
+| [Collaiborator-MEDLLM-Llama-3-8B-v2.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/collaiborateorg_-_Collaiborator-MEDLLM-Llama-3-8B-v2-gguf/blob/main/Collaiborator-MEDLLM-Llama-3-8B-v2.Q3_K_S.gguf) | Q3_K_S | 3.41GB |
+| [Collaiborator-MEDLLM-Llama-3-8B-v2.IQ3_M.gguf](https://huggingface.co/RichardErkhov/collaiborateorg_-_Collaiborator-MEDLLM-Llama-3-8B-v2-gguf/blob/main/Collaiborator-MEDLLM-Llama-3-8B-v2.IQ3_M.gguf) | IQ3_M | 3.52GB |
+| [Collaiborator-MEDLLM-Llama-3-8B-v2.Q3_K.gguf](https://huggingface.co/RichardErkhov/collaiborateorg_-_Collaiborator-MEDLLM-Llama-3-8B-v2-gguf/blob/main/Collaiborator-MEDLLM-Llama-3-8B-v2.Q3_K.gguf) | Q3_K | 3.74GB |
+| [Collaiborator-MEDLLM-Llama-3-8B-v2.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/collaiborateorg_-_Collaiborator-MEDLLM-Llama-3-8B-v2-gguf/blob/main/Collaiborator-MEDLLM-Llama-3-8B-v2.Q3_K_M.gguf) | Q3_K_M | 3.74GB |
+| [Collaiborator-MEDLLM-Llama-3-8B-v2.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/collaiborateorg_-_Collaiborator-MEDLLM-Llama-3-8B-v2-gguf/blob/main/Collaiborator-MEDLLM-Llama-3-8B-v2.Q3_K_L.gguf) | Q3_K_L | 4.03GB |
+| [Collaiborator-MEDLLM-Llama-3-8B-v2.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/collaiborateorg_-_Collaiborator-MEDLLM-Llama-3-8B-v2-gguf/blob/main/Collaiborator-MEDLLM-Llama-3-8B-v2.IQ4_XS.gguf) | IQ4_XS | 4.18GB |
+| [Collaiborator-MEDLLM-Llama-3-8B-v2.Q4_0.gguf](https://huggingface.co/RichardErkhov/collaiborateorg_-_Collaiborator-MEDLLM-Llama-3-8B-v2-gguf/blob/main/Collaiborator-MEDLLM-Llama-3-8B-v2.Q4_0.gguf) | Q4_0 | 4.34GB |
+| [Collaiborator-MEDLLM-Llama-3-8B-v2.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/collaiborateorg_-_Collaiborator-MEDLLM-Llama-3-8B-v2-gguf/blob/main/Collaiborator-MEDLLM-Llama-3-8B-v2.IQ4_NL.gguf) | IQ4_NL | 4.38GB |
+| [Collaiborator-MEDLLM-Llama-3-8B-v2.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/collaiborateorg_-_Collaiborator-MEDLLM-Llama-3-8B-v2-gguf/blob/main/Collaiborator-MEDLLM-Llama-3-8B-v2.Q4_K_S.gguf) | Q4_K_S | 4.37GB |
+| [Collaiborator-MEDLLM-Llama-3-8B-v2.Q4_K.gguf](https://huggingface.co/RichardErkhov/collaiborateorg_-_Collaiborator-MEDLLM-Llama-3-8B-v2-gguf/blob/main/Collaiborator-MEDLLM-Llama-3-8B-v2.Q4_K.gguf) | Q4_K | 4.58GB |
+| [Collaiborator-MEDLLM-Llama-3-8B-v2.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/collaiborateorg_-_Collaiborator-MEDLLM-Llama-3-8B-v2-gguf/blob/main/Collaiborator-MEDLLM-Llama-3-8B-v2.Q4_K_M.gguf) | Q4_K_M | 4.58GB |
+| [Collaiborator-MEDLLM-Llama-3-8B-v2.Q4_1.gguf](https://huggingface.co/RichardErkhov/collaiborateorg_-_Collaiborator-MEDLLM-Llama-3-8B-v2-gguf/blob/main/Collaiborator-MEDLLM-Llama-3-8B-v2.Q4_1.gguf) | Q4_1 | 4.78GB |
+| [Collaiborator-MEDLLM-Llama-3-8B-v2.Q5_0.gguf](https://huggingface.co/RichardErkhov/collaiborateorg_-_Collaiborator-MEDLLM-Llama-3-8B-v2-gguf/blob/main/Collaiborator-MEDLLM-Llama-3-8B-v2.Q5_0.gguf) | Q5_0 | 5.21GB |
+| [Collaiborator-MEDLLM-Llama-3-8B-v2.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/collaiborateorg_-_Collaiborator-MEDLLM-Llama-3-8B-v2-gguf/blob/main/Collaiborator-MEDLLM-Llama-3-8B-v2.Q5_K_S.gguf) | Q5_K_S | 5.21GB |
+| [Collaiborator-MEDLLM-Llama-3-8B-v2.Q5_K.gguf](https://huggingface.co/RichardErkhov/collaiborateorg_-_Collaiborator-MEDLLM-Llama-3-8B-v2-gguf/blob/main/Collaiborator-MEDLLM-Llama-3-8B-v2.Q5_K.gguf) | Q5_K | 5.34GB |
+| [Collaiborator-MEDLLM-Llama-3-8B-v2.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/collaiborateorg_-_Collaiborator-MEDLLM-Llama-3-8B-v2-gguf/blob/main/Collaiborator-MEDLLM-Llama-3-8B-v2.Q5_K_M.gguf) | Q5_K_M | 5.34GB |
+| [Collaiborator-MEDLLM-Llama-3-8B-v2.Q5_1.gguf](https://huggingface.co/RichardErkhov/collaiborateorg_-_Collaiborator-MEDLLM-Llama-3-8B-v2-gguf/blob/main/Collaiborator-MEDLLM-Llama-3-8B-v2.Q5_1.gguf) | Q5_1 | 5.65GB |
+| [Collaiborator-MEDLLM-Llama-3-8B-v2.Q6_K.gguf](https://huggingface.co/RichardErkhov/collaiborateorg_-_Collaiborator-MEDLLM-Llama-3-8B-v2-gguf/blob/main/Collaiborator-MEDLLM-Llama-3-8B-v2.Q6_K.gguf) | Q6_K | 6.14GB |
+| [Collaiborator-MEDLLM-Llama-3-8B-v2.Q8_0.gguf](https://huggingface.co/RichardErkhov/collaiborateorg_-_Collaiborator-MEDLLM-Llama-3-8B-v2-gguf/blob/main/Collaiborator-MEDLLM-Llama-3-8B-v2.Q8_0.gguf) | Q8_0 | 7.95GB |
+
+
+
+
+Original model description:
+---
+license: llama3
+library_name: transformers
+tags:
+- generated_from_trainer
+- Healthcare & Lifesciences
+- BioMed
+- Medical
+- CollAIborate
+base_model: meta-llama/Meta-Llama-3-8B-Instruct
+thumbnail: https://collaiborate.com/logo/logo-blue-bg-1.png
+model-index:
+- name: Collaiborator-MEDLLM-Llama-3-8B-v2
+  results: []
+datasets:
+- collaiborateorg/BioMedData
+---
+
+# Collaiborator-MEDLLM-Llama-3-8B-v2
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/653f5b93cd52f288490edc83/wIES_YhNPKn--AqcEmzRJ.png)
+This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on our custom "BioMedData" dataset.
+
+## Model details
+
+Model Name: Collaiborator-MEDLLM-Llama-3-8b-v2
+
+Base Model: Llama-3-8B-Instruct
+
+Parameter Count: 8 billion
+
+Training Data: Custom high-quality biomedical dataset
+
+Number of Entries in Dataset: 500,000+
+
+Dataset Composition: The dataset comprises both synthetic and manually curated samples, ensuring a diverse and comprehensive coverage of biomedical knowledge.
+
+## Model description
+
+Collaiborator-MEDLLM-Llama-3-8b-v2 is a specialized large language model designed for biomedical applications. It is finetuned from the Llama-3-8B-Instruct model using a custom dataset containing over 500,000 diverse entries. These entries include a mix of synthetic and manually curated data, ensuring high quality and broad coverage of biomedical topics.
+
+The model is trained to understand and generate text related to various biomedical fields, making it a valuable tool for researchers, clinicians, and other professionals in the biomedical domain.
+
+## Evaluation Metrics
+Collaiborator-MEDLLM-Llama-3-8b-v2 outperforms many of the leading LLMs and find below its metrics evaluated using the Eleuther AI Language Model Evaluation Harness framework against the tasks medmcqa, medqa_4options, mmlu_anatomy, mmlu_clinical_knowledge, mmlu_college_biology, mmlu_college_medicine, mmlu_medical_genetics, mmlu_professional_medicine and pubmedqa
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/65a78a59838b3acc53d656de/LAL2thZUhxtg56u5SYIGb.png)
+
+## Benchmark Results
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/653f5b93cd52f288490edc83/CxDpvXRSIhuG45TShELKw.png)
+
+## Quick Demo
+
+<video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/653f5b93cd52f288490edc83/piGRPwvcBTLmcgExL89zp.mp4"></video>
+
+## Intended uses & limitations
+
+Collaiborator-MEDLLM-Llama-3-8b-v2 is intended for a wide range of applications within the biomedical field, including:
+
+1. Research Support: Assisting researchers in literature review and data extraction from biomedical texts.
+2. Clinical Decision Support: Providing information to support clinical decision-making processes.
+3. Educational Tool: Serving as a resource for medical students and professionals seeking to expand their knowledge base.
+
+## Limitations and Ethical Considerations
+
+While Collaiborator-MEDLLM-Llama-3-8b-v2 performs well in various biomedical NLP tasks, users should be aware of the following limitations:
+
+Biases: The model may inherit biases present in the training data. Efforts have been made to curate a balanced dataset, but some biases may persist.
+
+Accuracy: The model's responses are based on patterns in the data it has seen and may not always be accurate or up-to-date. Users should verify critical information from reliable sources.
+
+Ethical Use: The model should be used responsibly, particularly in clinical settings where the stakes are high. It should complement, not replace, professional judgment and expertise.
+
+
+## Training and evaluation
+
+Collaiborator-MEDLLM-Llama-3-8b-v2 was trained using NVIDIA A40 GPU's, which provides the computational power necessary for handling large-scale data and model parameters efficiently. Rigorous evaluation protocols have been implemented to benchmark its performance against similar models, ensuring its robustness and reliability in real-world applications.
+
+## How to use
+
+import transformers
+import torch
+
+model_id = "collaiborateorg/Collaiborator-MEDLLM-Llama-3-8B-v2"
+
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model_id,
+    model_kwargs={"torch_dtype": torch.bfloat16},
+    device_map="auto",
+)
+
+messages = [
+    {"role": "system", "content": "You are an expert trained on healthcare and biomedical domain!"},
+    {"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?"},
+]
+
+prompt = pipeline.tokenizer.apply_chat_template(
+        messages, 
+        tokenize=False, 
+        add_generation_prompt=True
+)
+
+terminators = [
+    pipeline.tokenizer.eos_token_id,
+    pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
+]
+
+outputs = pipeline(
+    prompt,
+    max_new_tokens=256,
+    eos_token_id=terminators,
+    do_sample=True,
+    temperature=0.6,
+    top_p=0.9,
+)
+
+print(outputs[0]["generated_text"][len(prompt):])
+
+### Contact Information
+
+For further information, inquiries, or issues related to Biomed-LLM, please contact:
+
+Email: info@collaiborate.com
+
+Website: https://www.collaiborate.com
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.0002
+- train_batch_size: 12
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_ratio: 0.03
+- mixed_precision_training: Native AMP
+
+### Framework versions
+
+- PEFT 0.11.0
+- Transformers 4.40.2
+- Pytorch 2.1.2
+- Datasets 2.19.1
+- Tokenizers 0.19.1
+
+### Citation
+
+If you use Collaiborator-MEDLLM-Llama-3-8b in your research or applications, please cite it as follows:
+
+@misc{Collaiborator_MEDLLM,
+  author = Collaiborator,
+  title = {Collaiborator-MEDLLM-Llama-3-8b: A High-Performance Biomedical Language Model},
+  year = {2024},
+  howpublished = {https://huggingface.co/collaiborateorg/Collaiborator-MEDLLM-Llama-3-8B-v2},
+}
+