初始化项目，由ModelHub XC社区提供模型
Model: DipaHealth/DipaMed-1 Source: Original Platform
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README.md
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---
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license: llama3.1
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base_model: meta-llama/Llama-3.1-8B
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language:
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- en
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pipeline_tag: text-generation
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tags:
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- medical
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- clinical
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- nigeria
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- healthcare
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- africa
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- llama
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- global-health
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---
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# DipaMed-1
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**A language model specialised on Nigerian clinical guidelines.**
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DipaMed-1 adapts Meta's Llama-3.1-8B to Nigerian medicine, grounded in Federal Ministry of Health (FMOH) and Nigeria Centre for Disease Control (NCDC) clinical guidelines. It is built to give locally-appropriate clinical guidance that reflects Nigerian disease priorities, the Nigerian Essential Medicines List, and national treatment protocols.
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- **Developed by:** Destiny Ebhodaghe Ibhate (DipaHealth)
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- **Base model:** meta-llama/Llama-3.1-8B
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- **Language:** English
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- **License:** Llama 3.1 Community License
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> **Intended use:** clinical decision *support* for trained health workers. DipaMed-1 is **not** an autonomous diagnostic system and must not be used to make patient-care decisions without a qualified clinician.
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## Highlights - where DipaMed-1 leads
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On [NigeriaMedQA](https://huggingface.co/datasets/DipaHealth/NigeriaMedQA), DipaMed-1 **outperforms its base model on the Nigeria-specific clinical topics it was built for:**
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| Topic | DipaMed-1 | Base Llama-3.1-8B | Improvement |
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|---|---|---|---|
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| Mental health | **95.8%** | 87.5% | **+8.3** |
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| Maternal emergencies | **90.6%** | 84.4% | **+6.2** |
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| Drug availability | **72.8%** | 67.0% | **+5.8** |
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| Hypertension | **68.5%** | 64.8% | **+3.7** |
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| Tuberculosis-HIV | **82.4%** | 79.4% | **+3.0** |
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| Lassa fever | **79.5%** | 76.9% | **+2.6** |
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| Sickle cell disease | **81.8%** | 80.0% | **+1.8** |
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| Outbreak diseases | **62.1%** | 60.3% | **+1.8** |
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These are the diseases and decisions that matter most in Nigerian practice. Across the full benchmark, DipaMed-1 performs comparably to the base model overall (76.3% vs 77.0%), while delivering these gains where Nigerian specialisation counts.
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## What makes it different
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General medical models are trained on North American and European data. DipaMed-1 is grounded in **Nigerian** guidelines, giving Nigeria-appropriate answers a general model cannot: correct local first-line treatments, Essential-Medicines-List-aware choices, and NCDC/FMOH-aligned protocols.
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## How it was built
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| Stage | Purpose | Data |
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|---|---|---|
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| **Continued pretraining** | Absorb Nigerian medical knowledge | 156 million words of Nigerian biomedical text (PubMed abstracts, open-access PMC full-text, clinical guidelines); approx. 254M tokens |
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| **Instruction tuning** | Learn to answer clinical questions | Q&A generated from and independently verified against real Nigerian guidelines, plus cleaned expert-created sources (PubMedQA, MedQA-USMLE, WikiDoc) |
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Training used QLoRA (rank 16), a learning-rate sweep with model selection on a **held-out validation set**, and completion-only loss masking. The evaluation benchmark was kept fully uncontaminated and used only once for final scoring. The pretraining corpus is not released; its construction methodology is described in the accompanying paper.
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## Usage
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "DipaHealth/DipaMed-1"
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model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
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tok = AutoTokenizer.from_pretrained(model_id)
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messages = [
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{"role": "system", "content": "You are DipaMed-1, a clinical AI assistant grounded in Nigerian guidelines."},
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{"role": "user", "content": "First-line treatment for uncomplicated malaria in a non-pregnant adult in Nigeria?"},
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]
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input_ids = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
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out = model.generate(input_ids=input_ids, max_new_tokens=256, do_sample=False, pad_token_id=tok.eos_token_id)
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print(tok.decode(out[0][input_ids.shape[1]:], skip_special_tokens=True))
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```
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## Recommended deployment: retrieve, then answer (RAG)
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For production, wrap DipaMed-1 in a retrieval-augmented (RAG) pipeline over the Nigerian guidelines: retrieve the relevant passage, give it to the model, and have it answer **from that passage with a citation**. This substantially improves factual reliability, especially for exact drug doses where all language models are unreliable from memory, and lets the system cite its source. A dedicated DipaMed RAG service is planned as a separate release.
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## Limitations and responsible use
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- **Exact doses:** do not rely on DipaMed-1 for precise dosing without retrieval support; language models do not reliably memorise numeric dose tables.
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- **Decision support only:** it assists clinicians and must not make autonomous clinical decisions.
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- **Scale:** at 8B parameters it will not match frontier models on general medicine; its strength is Nigerian domain specialisation.
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- **Errors and bias:** like all language models it can produce confident but incorrect answers; verify outputs against source guidelines.
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- **Scope:** English, text-only in this version. Speech and Nigerian-language support are planned.
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## Citation
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```bibtex
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@misc{dipamed2026,
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title = {DipaMed-1: A Nigerian Guideline-Specialised Clinical Language Model},
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author = {Ibhate, Destiny Ebhodaghe},
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year = {2026},
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howpublished = {\url{https://huggingface.co/DipaHealth/DipaMed-1}}
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}
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```
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## Acknowledgements
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Built on Meta Llama-3.1-8B. Evaluated with [NigeriaMedQA](https://huggingface.co/datasets/DipaHealth/NigeriaMedQA). Grounded in FMOH and NCDC clinical guidelines.
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chat_template.jinja
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{{- bos_token }}
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{%- if custom_tools is defined %}
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{%- set tools = custom_tools %}
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{%- endif %}
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{%- if not tools_in_user_message is defined %}
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{%- set tools_in_user_message = true %}
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{%- endif %}
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{%- if not date_string is defined %}
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{%- set date_string = "26 Jul 2024" %}
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{%- endif %}
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{%- if not tools is defined %}
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{%- set tools = none %}
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{%- endif %}
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{#- This block extracts the system message, so we can slot it into the right place. #}
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{%- if messages[0]['role'] == 'system' %}
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{%- set system_message = messages[0]['content']|trim %}
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{%- set messages = messages[1:] %}
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{%- else %}
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{%- set system_message = "" %}
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{%- endif %}
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{#- System message + builtin tools #}
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{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
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{%- if builtin_tools is defined or tools is not none %}
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{{- "Environment: ipython\n" }}
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{%- endif %}
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{%- if builtin_tools is defined %}
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{{- "Tools: " + builtin_tools | reject('equalto', 'code_interpreter') | join(", ") + "\n\n"}}
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{%- endif %}
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{{- "Cutting Knowledge Date: December 2023\n" }}
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{{- "Today Date: " + date_string + "\n\n" }}
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{%- if tools is not none and not tools_in_user_message %}
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{{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
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{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
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{{- "Do not use variables.\n\n" }}
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{%- for t in tools %}
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{{- t | tojson(indent=4) }}
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{{- "\n\n" }}
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{%- endfor %}
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{%- endif %}
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{{- system_message }}
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{{- "<|eot_id|>" }}
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{#- Custom tools are passed in a user message with some extra guidance #}
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{%- if tools_in_user_message and not tools is none %}
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{#- Extract the first user message so we can plug it in here #}
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{%- if messages | length != 0 %}
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{%- set first_user_message = messages[0]['content']|trim %}
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{%- set messages = messages[1:] %}
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{%- else %}
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{{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
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{%- endif %}
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{{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
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{{- "Given the following functions, please respond with a JSON for a function call " }}
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{{- "with its proper arguments that best answers the given prompt.\n\n" }}
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{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
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{{- "Do not use variables.\n\n" }}
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{%- for t in tools %}
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{{- t | tojson(indent=4) }}
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{{- "\n\n" }}
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{%- endfor %}
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{{- first_user_message + "<|eot_id|>"}}
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{%- endif %}
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{%- for message in messages %}
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{%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
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{{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
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{%- elif 'tool_calls' in message %}
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{%- if not message.tool_calls|length == 1 %}
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{{- raise_exception("This model only supports single tool-calls at once!") }}
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{%- endif %}
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{%- set tool_call = message.tool_calls[0].function %}
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{%- if builtin_tools is defined and tool_call.name in builtin_tools %}
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{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
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{{- "<|python_tag|>" + tool_call.name + ".call(" }}
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{%- for arg_name, arg_val in tool_call.arguments | items %}
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{{- arg_name + '="' + arg_val + '"' }}
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{%- if not loop.last %}
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{{- ", " }}
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{%- endif %}
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{%- endfor %}
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{{- ")" }}
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{%- else %}
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{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
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{{- '{"name": "' + tool_call.name + '", ' }}
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{{- '"parameters": ' }}
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{{- tool_call.arguments | tojson }}
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{{- "}" }}
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{%- endif %}
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{%- if builtin_tools is defined %}
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{#- This means we're in ipython mode #}
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{{- "<|eom_id|>" }}
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{%- else %}
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{{- "<|eot_id|>" }}
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{%- endif %}
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{%- elif message.role == "tool" or message.role == "ipython" %}
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{{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
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{%- if message.content is mapping or message.content is iterable %}
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{{- message.content | tojson }}
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{%- else %}
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{{- message.content }}
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{%- endif %}
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{{- "<|eot_id|>" }}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
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{%- endif %}
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config.json
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config.json
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{
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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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"dtype": "bfloat16",
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"eos_token_id": 128001,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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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": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"pad_token_id": null,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_parameters": {
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"factor": 8.0,
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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_theta": 500000.0,
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"rope_type": "llama3"
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},
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"tie_word_embeddings": false,
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"transformers_version": "5.13.0",
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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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{
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"_from_model_config": true,
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"bos_token_id": 128000,
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"do_sample": true,
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"eos_token_id": 128001,
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"temperature": 0.6,
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"top_p": 0.9,
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"transformers_version": "5.13.0"
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}
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model.safetensors
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:3638e75f62677b6314fee2316d8f3320f535f86896447c9ed060b69eb8cae2af
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size 16060556616
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BIN
tokenizer.json
(Stored with Git LFS)
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BIN
tokenizer.json
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tokenizer_config.json
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{
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"backend": "tokenizers",
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"bos_token": "<|begin_of_text|>",
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"clean_up_tokenization_spaces": true,
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||||||
|
"eos_token": "<|eot_id|>",
|
||||||
|
"is_local": false,
|
||||||
|
"local_files_only": false,
|
||||||
|
"model_input_names": [
|
||||||
|
"input_ids",
|
||||||
|
"attention_mask"
|
||||||
|
],
|
||||||
|
"model_max_length": 131072,
|
||||||
|
"tokenizer_class": "TokenizersBackend"
|
||||||
|
}
|
||||||
Reference in New Issue
Block a user