Upload llama31-8b-ade-sft-v2 adapter (exact_match 0.715, positive_f1 0.860)

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,157 @@
+---
+license: llama3.1
+base_model: meta-llama/Llama-3.1-8B-Instruct
+library_name: peft
+pipeline_tag: text-generation
+language:
+  - en
+tags:
+  - medical
+  - biomedical
+  - adverse-drug-events
+  - ade
+  - pharmacovigilance
+  - distillation
+  - lora
+  - peft
+  - llama-3.1
+datasets:
+  - ade-benchmark-corpus/ade_corpus_v2
+model-index:
+  - name: llama31-8b-ade-sft-v2
+    results:
+      - task:
+          type: text-generation
+          name: ADE Binary QA + span extraction
+        dataset:
+          type: ade-benchmark-corpus/ade_corpus_v2
+          name: ade_corpus_v2 (200 held-out)
+        metrics:
+          - type: exact_match
+            value: 0.715
+            name: exact_match (answer ∈ {yes,no,abstain})
+          - type: f1
+            value: 0.860
+            name: positive_f1 (answer=yes)
+          - type: precision
+            value: 0.785
+            name: positive_precision
+          - type: recall
+            value: 0.950
+            name: positive_recall
+          - type: f1
+            value: 0.883
+            name: span_drug_token_f1 (positives only)
+          - type: f1
+            value: 0.866
+            name: span_event_token_f1 (positives only)
+---
+
+# llama31-8b-ade-sft-v2
+
+A LoRA adapter for [`meta-llama/Llama-3.1-8B-Instruct`](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) that answers adverse drug event (ADE) questions on single-sentence clinical text and extracts the implicated drug and event as structured JSON. Distilled from a Vertex-hosted Llama 3.3 70B teacher; trained with QLoRA on ~3k teacher-labeled sentences from `ade_corpus_v2`.
+
+**⚠️ Not clinical grade.** This is a research / educational artifact. Do not use for patient-care decisions.
+
+## Intended use
+
+Given a short clinical vignette (one or a few sentences), produce a JSON object:
+
+```json
+{
+  "answer": "yes | no | abstain",
+  "drug": "<drug name or empty>",
+  "event": "<adverse event or empty>",
+  "evidence": "<quoted or closely paraphrased text>",
+  "short_justification": "<one short sentence>",
+  "confidence": 0.0
+}
+```
+
+- `answer` is `yes` only when the text supports a causally plausible drug-event relationship.
+- `abstain` is reserved for cases where the text names no plausible drug or no plausible event. Temporal co-occurrence with a clear external cause (e.g., "on metformin, slipped and fractured ankle") should be `no`, not `abstain`.
+
+## Evaluation
+
+Held-out split (200 rows, balanced 100 positive / 100 negative) sampled from `ade_corpus_v2` and never seen during training. Compared against a v1 baseline that did not use few-shots or hard negatives.
+
+| Metric | v1 | **v2 (this model)** |
+|---|---|---|
+| exact_match (yes/no/abstain) | 0.555 | **0.715** |
+| abstain_rate | 0.315 | **0.135** |
+| positive_f1 | 0.884 | 0.860 |
+| positive_precision | 0.798 | 0.785 |
+| positive_recall | 0.990 | 0.950 |
+| span_drug_exact_match (pos) | 0.940 | 0.840 |
+| span_drug_token_f1 (pos) | 0.952 | 0.883 |
+| span_event_exact_match (pos) | 0.660 | 0.710 |
+| span_event_token_f1 (pos) | 0.816 | 0.866 |
+
+**Tradeoff to know.** v2 adds 600 "hard negatives" (drug mentioned, answer=no) to teach calibrated abstention. This halved the abstain rate and added 16 pts of exact_match, but cost ~10 pts of drug-span exact match vs v1 — the model learned to be more cautious about emitting a drug name. If your use case needs drug extraction on positives above all else, the earlier v1 checkpoint may be preferable.
+
+## Usage
+
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+import torch
+
+base_id = "meta-llama/Llama-3.1-8B-Instruct"
+adapter_id = "Ventali/llama31-8b-ade-sft-v2"
+
+bnb = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_compute_dtype=torch.bfloat16,
+)
+tokenizer = AutoTokenizer.from_pretrained(base_id)
+model = AutoModelForCausalLM.from_pretrained(base_id, quantization_config=bnb, device_map="auto")
+model = PeftModel.from_pretrained(model, adapter_id)
+model.eval()
+
+messages = [
+    {"role": "system", "content": "You are a careful biomedical assistant. For each case, return a compact JSON answer grounded in the provided evidence. If the evidence is insufficient, abstain."},
+    {"role": "user", "content": "Case: The patient developed diffuse urticaria three days after starting amoxicillin.\n\nIs this consistent with a possible adverse drug event? Identify the drug and event if so, or abstain if the evidence is insufficient."},
+]
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+with torch.no_grad():
+    out = model.generate(**inputs, max_new_tokens=256, do_sample=False, pad_token_id=tokenizer.eos_token_id)
+print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
+```
+
+For Apple Silicon you can fuse the adapter into the base and run via `mlx-lm`:
+
+```bash
+pip install mlx-lm
+mlx_lm.fuse --model meta-llama/Llama-3.1-8B-Instruct \
+  --adapter-path <local-adapter-dir> \
+  --save-path ~/models/llama31-ade-mlx
+mlx_lm.generate --model ~/models/llama31-ade-mlx --prompt "..."
+```
+
+## Training
+
+- Base: `meta-llama/Llama-3.1-8B-Instruct`, loaded in 4-bit (NF4, double-quant, bf16 compute).
+- LoRA: r=32, alpha=64, dropout=0.05, target modules {q,k,v,o,gate,up,down}_proj. 41.9M trainable params (0.52% of base).
+- Data: 2,999 (prompt, teacher JSON) pairs. Prompts drawn from `ade_corpus_v2` as 1,200 positive (from `drug_ade_relation`) + 1,200 easy-negative + 600 hard-negative (classification label=0 rows whose text mentions a drug from the positive-split vocabulary). Teacher: Vertex AI managed `llama-3.3-70b-instruct-maas` (temperature 0.2), seeded with 3 yes/no/abstain few-shots and prompted to reserve abstention for cases with no plausible drug or no plausible event.
+- Filter: required non-empty `answer` and `evidence`, `confidence ≥ 0.65`, evidence-source word overlap ≥ 0.6. 2,999/3,000 retained.
+- Optimizer: AdamW, lr=2e-4, warmup_ratio=0.03, weight_decay=0.01, bf16, gradient_checkpointing on.
+- 3 epochs with `load_best_model_at_end=True` on `eval_loss`; the epoch-1 checkpoint (eval_loss 0.506) was restored, eclipsing the overfit epochs 2–3 (0.547, 0.676).
+- Hardware: single A100 40GB on GCP `a2-highgpu-1g`. Training wall time ~94 min.
+
+## Limitations
+
+- Trained on single-sentence, literature-style clinical text. Longer narratives (discharge summaries, EHR free-text) are out of distribution and will likely perform worse.
+- Teacher labels are synthetic. A clinician-reviewed eval set was not used; regressions against human judgment have not been measured.
+- The model occasionally produces an empty `drug` or `event` field on positive cases, which is a regression from v1 on drug-span extraction. See the tradeoff note above.
+- English only.
+
+## Reproducibility
+
+Full pipeline (seed building, teacher generation config, filter, SFT prep, training, evaluation) lives at https://github.com/ventali/medical-distill. Commit [`547629f`](https://github.com/ventali/medical-distill/commit/547629f) records this adapter's metrics.
+
+## License
+
+Inherits the [Llama 3.1 Community License](https://llama.meta.com/llama3_1/license/) from the base model.

adapter_config.json

@@ -0,0 +1,48 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": null,
+  "base_model_name_or_path": "meta-llama/Llama-3.1-8B-Instruct",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 64,
+  "lora_bias": false,
+  "lora_dropout": 0.05,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "peft_version": "0.19.1",
+  "qalora_group_size": 16,
+  "r": 32,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "v_proj",
+    "up_proj",
+    "o_proj",
+    "q_proj",
+    "k_proj",
+    "down_proj",
+    "gate_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3a846d9693f4bc75f02e0b9e7b846e50d37bca3656051eae0d1faf125bb0b9ee
+size 335604696

chat_template.jinja

@@ -0,0 +1,109 @@
+{{- bos_token }}
+{%- if custom_tools is defined %}
+    {%- set tools = custom_tools %}
+{%- endif %}
+{%- if not tools_in_user_message is defined %}
+    {%- set tools_in_user_message = true %}
+{%- endif %}
+{%- if not date_string is defined %}
+    {%- set date_string = "26 Jul 2024" %}
+{%- endif %}
+{%- if not tools is defined %}
+    {%- set tools = none %}
+{%- endif %}
+
+{#- This block extracts the system message, so we can slot it into the right place. #}
+{%- if messages[0]['role'] == 'system' %}
+    {%- set system_message = messages[0]['content']|trim %}
+    {%- set messages = messages[1:] %}
+{%- else %}
+    {%- set system_message = "" %}
+{%- endif %}
+
+{#- System message + builtin tools #}
+{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
+{%- if builtin_tools is defined or tools is not none %}
+    {{- "Environment: ipython\n" }}
+{%- endif %}
+{%- if builtin_tools is defined %}
+    {{- "Tools: " + builtin_tools | reject('equalto', 'code_interpreter') | join(", ") + "\n\n"}}
+{%- endif %}
+{{- "Cutting Knowledge Date: December 2023\n" }}
+{{- "Today Date: " + date_string + "\n\n" }}
+{%- if tools is not none and not tools_in_user_message %}
+    {{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+{%- endif %}
+{{- system_message }}
+{{- "<|eot_id|>" }}
+
+{#- Custom tools are passed in a user message with some extra guidance #}
+{%- if tools_in_user_message and not tools is none %}
+    {#- Extract the first user message so we can plug it in here #}
+    {%- if messages | length != 0 %}
+        {%- set first_user_message = messages[0]['content']|trim %}
+        {%- set messages = messages[1:] %}
+    {%- else %}
+        {{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
+{%- endif %}
+    {{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
+    {{- "Given the following functions, please respond with a JSON for a function call " }}
+    {{- "with its proper arguments that best answers the given prompt.\n\n" }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+    {{- first_user_message + "<|eot_id|>"}}
+{%- endif %}
+
+{%- for message in messages %}
+    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
+        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
+    {%- elif 'tool_calls' in message %}
+        {%- if not message.tool_calls|length == 1 %}
+            {{- raise_exception("This model only supports single tool-calls at once!") }}
+        {%- endif %}
+        {%- set tool_call = message.tool_calls[0].function %}
+        {%- if builtin_tools is defined and tool_call.name in builtin_tools %}
+            {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
+            {{- "<|python_tag|>" + tool_call.name + ".call(" }}
+            {%- for arg_name, arg_val in tool_call.arguments | items %}
+                {{- arg_name + '="' + arg_val + '"' }}
+                {%- if not loop.last %}
+                    {{- ", " }}
+                {%- endif %}
+                {%- endfor %}
+            {{- ")" }}
+        {%- else  %}
+            {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
+            {{- '{"name": "' + tool_call.name + '", ' }}
+            {{- '"parameters": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- "}" }}
+        {%- endif %}
+        {%- if builtin_tools is defined %}
+            {#- This means we're in ipython mode #}
+            {{- "<|eom_id|>" }}
+        {%- else %}
+            {{- "<|eot_id|>" }}
+        {%- endif %}
+    {%- elif message.role == "tool" or message.role == "ipython" %}
+        {{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
+        {%- if message.content is mapping or message.content is iterable %}
+            {{- message.content | tojson }}
+        {%- else %}
+            {{- message.content }}
+        {%- endif %}
+        {{- "<|eot_id|>" }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
+{%- endif %}

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:384a7e7c676f7be2e5d2e8449c508be9b00e5b18c5b3c39ebc626e96b3f4b988
+size 17210019

tokenizer_config.json

@@ -0,0 +1,14 @@
+{
+  "backend": "tokenizers",
+  "bos_token": "<|begin_of_text|>",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|eot_id|>",
+  "is_local": false,
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 131072,
+  "pad_token": "<|eot_id|>",
+  "tokenizer_class": "TokenizersBackend"
+}