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Model: mims-harvard/ATHENA-R1-Qwen3-8B
Source: Original Platform

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README.md

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+---
+license: mit
+language:
+- en
+base_model: Qwen/Qwen3-8B
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- agent
+- biomedical
+- treatment-reasoning
+- tool-use
+- reinforcement-learning
+- qwen3
+---
+
+# ATHENA-R1-Qwen3-8B
+
+**Project page:** [athena.openscientist.ai](https://athena.openscientist.ai/) · **Code:** [mims-harvard/ATHENA](https://github.com/mims-harvard/ATHENA)
+
+**ATHENA-R1** is an AI agent for treatment reasoning, trained through
+reinforcement learning over a universe of 212 biomedical tools. It performs
+*multi-step* reasoning — identifying what evidence is needed, selecting tools,
+and incorporating retrieved evidence into subsequent steps — with tool calls
+served through the
+[ToolUniverse](https://github.com/mims-harvard/ToolUniverse) (FDA labeling,
+Open Targets, ChEMBL, EuropePMC, etc.).
+
+Given a clinical question, the model performs *multi-step* tool calls,
+synthesises the evidence, and returns a free-form answer grounded in
+authoritative biomedical sources.
+
+## Quick start
+
+The model is exposed through the
+[`athena-r1`](https://github.com/mims-harvard/ATHENA) Python package,
+which handles the tool-call protocol and conversation management. Two
+services back the agent: vLLM (model server) and ToolUniverse (tool server).
+
+```bash
+# 1. Install
+pip install "athena-r1[vllm,web] @ git+https://github.com/mims-harvard/ATHENA.git"
+
+# 2. Start backing services
+bash scripts/launch_tooluniverse.sh         # → :8080
+bash scripts/launch_vllm.sh 8000 mims-harvard/ATHENA-R1-Qwen3-8B
+
+# 3. Run the agent (Python)
+python -c "
+from athena_r1 import AthenaR1
+agent = AthenaR1(
+    model='mims-harvard/ATHENA-R1-Qwen3-8B',
+    vllm_url='http://0.0.0.0:8000/v1',
+    tool_server='http://0.0.0.0:8080',
+)
+print(agent.answer('Dose adjustment for metformin in CKD eGFR 35?').answer)
+"
+```
+
+For a chat UI (bundled browser demo with live-streamed reasoning):
+
+```bash
+python web/agui_server.py    # → http://localhost:8090/ (AG-UI server + demo)
+```
+
+For an OpenAI-compatible API endpoint:
+
+```bash
+python web/openai_server.py    # → http://localhost:9000/v1/chat/completions
+```
+
+## Inference settings (paper-canonical)
+
+| Parameter | Value |
+|---|---|
+| temperature | 0.7 |
+| top_p | 0.95 |
+| top_k | 20 |
+| min_p | 0.0 |
+| presence_penalty | 0 |
+| max_round | 40 |
+| concurrent Qs | 4 |
+
+## Evaluation
+
+Open-ended setting: each question is answered free-form, then mapped to one of
+the original answer choices.
+
+| Benchmark | n | ATHENA-R1 | GPT-5 |
+|---|---|---|---|
+| DrugPC (open-ended drug reasoning) | 3,168 | **94.7%** | 76.9% |
+| TreatmentPC (patient-specific treatment) | 456 | **82.9%** | 72.2% |
+
+ATHENA-R1 exceeds GPT-5 by 17.8 points on DrugPC and 10.7 on TreatmentPC.
+
+See the
+[`docs/eval_results.md`](https://github.com/mims-harvard/ATHENA/blob/main/docs/eval_results.md)
+file in the code repo for the full benchmark tables and the two-level
+self-learning ablation.
+
+## How it works
+
+1. **Stage 1 — multi-step tool reasoning**: the model emits
+   `<tool_call>...</tool_call>` blocks; the runtime dispatches them through
+   ToolUniverse, appends results to the conversation, and re-prompts. Loop
+   continues until `[FinalAnswer]` or `max_round` is hit.
+2. **Stage 2 (eval only) — option mapping**: a *separate* function call
+   maps the free-form answer to an MCQ letter. Two backends supported:
+   the local ATHENA-R1 model (self-extraction) or Azure GPT-5
+   (external reader).
+
+## Intended use
+
+ATHENA-R1 is a research artifact for treatment-reasoning research and
+decision support. It is not a medical device and must not be used for direct
+patient care.
+
+## Citation
+
+```bibtex
+@article{gao2026athena,
+  title   = {An AI agent for treatment reasoning over a biomedical tool universe},
+  author  = {Gao, Shanghua and ... and Zitnik, Marinka},
+  journal = {arXiv preprint},
+  year    = {2026}
+}
+```
+
+## License
+
+MIT.
+
+## Acknowledgements
+
+Evidence retrieval is powered by
+[ToolUniverse](https://github.com/mims-harvard/ToolUniverse), a library of
+curated biomedical tools.

added_tokens.json

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+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
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+  "<|fim_suffix|>": 151661,
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+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system
+' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '
+
+' }}
+    {%- endif %}
+    {{- "# Tools
+
+You may call one or more functions to assist with the user query.
+
+You are provided with function signatures within <tools></tools> XML tags:
+<tools>" }}
+    {%- for tool in tools %}
+        {{- "
+" }}
+        {{- tool }}
+    {%- endfor %}
+    {{- "
+</tools>
+
+For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
+<tool_call>
+" }}
+    {{- '{"name": <function-name>, "arguments": <args-json-object>}
+</tool_call><|im_end|>
+' }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system
+' + messages[0].content + '<|im_end|>
+' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '
+' + content + '<|im_end|>' + '
+' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('
+').split('<think>')[-1].lstrip('
+') %}
+                {%- set content = content.split('</think>')[-1].lstrip('
+') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.last or (not loop.last and reasoning_content) %}
+            {{- '<|im_start|>' + message.role + '
+<think>
+' + reasoning_content.strip('
+') + '
+</think>
+
+' + content.lstrip('
+') }}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '
+' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '
+' }}
+                {%- endif %}
+                {%- set raw_tool_call = tool_call %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>
+' }}
+                {# --- 解析成功：按原有 JSON 格式输出 --- #}
+                {%- if (tool_call is mapping) and ('name' in tool_call) and ('arguments' in tool_call) %}
+{"name": "{{ tool_call.name }}", "arguments": {%- if tool_call.arguments is string -%}{{ tool_call.arguments }}{%- else -%}{{ tool_call.arguments | tojson }}{%- endif -%}}
+                {# --- 解析失败：把原始输入原样塞进 <tool_call> --- #}
+                {%- else %}
+{%- if raw_tool_call is string -%}{{ raw_tool_call }}{%- else -%}{{ raw_tool_call | tojson }}{%- endif -%}
+                {%- endif %}
+                {{- '
+</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>
+' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '
+<tool_response>
+' }}
+        {{- content }}
+        {{- '
+</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>
+' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant
+' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>
+
+</think>
+
+' }}
+    {%- endif %}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 12288,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 40960,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "pad_token_id": 151643,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.52.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

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+  "do_sample": true,
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+    151643
+  ],
+  "pad_token_id": 151643,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "4.52.0"
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