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Model: deep-div/MediLlama-3.2 Source: Original Platform
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README.md
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---
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library_name: transformers
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tags:
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- unsloth
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- trl
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- sft
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license: apache-2.0
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language:
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- en
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base_model:
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- meta-llama/Llama-3.2-3B-Instruct
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pipeline_tag: text-generation
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metrics:
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- accuracy
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- bleu
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- rouge
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---
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# Model Card for MediLlama-3.2
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A fine-tuned version of Meta's LLaMA 3.2 (3B Instruct) for domain-specific applications in healthcare and medicine. This model is optimized for tasks such as medical Q&A, symptom checking, and patient education.
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## Model Details
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### Model Description
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This model is a domain-adapted version of LLaMA 3.2 3B Instruct. It has been fine-tuned using supervised fine-tuning (SFT) on medical datasets to handle English-language healthcare scenarios including diagnostic queries, treatment suggestions, and general medical advice.
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- **Developed by:** InferenceLab
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- **Model type:** Medical Chatbot
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- **Language(s) (NLP):** English
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- **License:** Apache 2.0
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- **Finetuned from model:** meta-llama/Llama-3.2-3B-Instruct
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## Uses
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### Direct Use
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MediLlama-3.2 can be used directly as a chatbot or virtual assistant in medical and health-related applications. Ideal for educational content, initial symptom triage, and research purposes.
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### Downstream Use
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Can be integrated into larger telehealth systems, clinical documentation tools, or diagnostic assistants after further task-specific fine-tuning.
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### Out-of-Scope Use
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- Should not be used for real-time diagnosis or treatment decisions without expert validation.
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- Not suitable for high-risk or life-threatening emergency response.
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- Not trained on pediatric or highly specialized medical domains.
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## Bias, Risks, and Limitations
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While the model is trained on medical data, it may still exhibit:
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- Biases from source data
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- Hallucinations or incorrect suggestions
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- Outdated or non-region-specific medical advice
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### Recommendations
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Users should validate outputs with certified medical professionals. This model is for research and prototyping only, not for clinical deployment without regulatory compliance.
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## How to Get Started with the Model
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```python
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import torch
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from transformers import pipeline
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model_id = "InferenceLab/MediLlama-3.2"
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pipe = pipeline(
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"text-generation",
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model=model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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messages = [
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{"role": "system", "content": "You are a helpful Medical assistant."},
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{"role": "user", "content": "Hi! How are you?"},
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]
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outputs = pipe(
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messages,
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max_new_tokens=256,
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)
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print(outputs[0]["generated_text"][-1])
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````
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## Training Details
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### Training Data
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Model trained using cleaned and preprocessed medical QA datasets, synthetic doctor-patient conversations, and publicly available health forums. Protected health information (PHI) was removed.
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### Training Procedure
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Supervised fine-tuning (SFT) using TRL and Unsloth libraries.
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#### Preprocessing
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Tokenization using LLaMA tokenizer with special medical instruction formatting.
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#### Training Hyperparameters
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* **Training regime:** bf16 mixed precision
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* **Learning rate:** 1e-5
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#### Speeds, Sizes, Times
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* **Training time:** \~12 hours on 4×A100 GPUs
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## Evaluation
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### Testing Data, Factors & Metrics
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#### Testing Data
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Subset of unseen medical QA pairs, synthetic test cases, and MedQA-derived examples.
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#### Factors
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* Input prompt complexity
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* Use of medical terminology
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* Chat length
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#### Metrics
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* **Accuracy:** 81.3%
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* **BLEU:** 34.5
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* **ROUGE-L:** 62.2
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### Results
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#### Summary
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Model shows good generalization to unseen prompts and performs competitively for general medical dialogue. Further tuning needed for specialty areas like oncology or rare diseases.
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## Model Examination
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Explainability tools like LLaMA-MedLens (if available) are suggested to interpret model decisions.
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## Environmental Impact
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* **Hardware Type:** 4×NVIDIA A100 40GB
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* **Hours used:** 12
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* **Cloud Provider:** AWS
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* **Compute Region:** us-west-2
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* **Carbon Emitted:** \~35.8 kg CO2eq (estimated)
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## Technical Specifications
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### Model Architecture and Objective
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* Based on Meta LLaMA 3.2 3B Instruct
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* Decoder-only transformer
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* Objective: Causal Language Modeling (CLM) with instruction fine-tuning
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### Compute Infrastructure
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#### Hardware
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* 4×NVIDIA A100 40GB
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#### Software
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* Python 3.10
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* Transformers (v4.40+)
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* TRL
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* Unsloth
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* PyTorch 2.1
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## Glossary
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* **SFT**: Supervised Fine-Tuning
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* **BLEU**: Bilingual Evaluation Understudy
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* **ROUGE**: Recall-Oriented Understudy for Gisting Evaluation
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## More Information
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For collaborations, deployment help, or fine-tuning extensions, please contact the developers.
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## Model Card Authors
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* InferenceLab Team
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config.json
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 128000,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 3072,
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"initializer_range": 0.02,
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"intermediate_size": 8192,
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"max_position_embeddings": 131072,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 24,
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"num_hidden_layers": 28,
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"num_key_value_heads": 8,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": {
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"factor": 32.0,
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"high_freq_factor": 4.0,
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"low_freq_factor": 1.0,
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"original_max_position_embeddings": 8192,
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"rope_type": "llama3"
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"rope_theta": 500000.0,
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"tie_word_embeddings": true,
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"torch_dtype": "float16",
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"transformers_version": "4.51.3",
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"unsloth_fixed": true,
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"unsloth_version": "2025.5.6",
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"use_cache": true,
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"vocab_size": 128256
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}
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generation_config.json
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"temperature": 0.6,
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"top_p": 0.9,
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"transformers_version": "4.51.3"
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}
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23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
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||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|finetune_right_pad_id|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:145cc0211ce61596383d8ae33aa07aa5555cb220e67064f0e36bf4beaffa025d
|
||||
size 17210200
|
||||
2067
tokenizer_config.json
Normal file
2067
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user