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@@ -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 +1,194 @@
+---
+license: apache-2.0
+library_name: peft
+base_model: meta-llama/Llama-3.3-70B-Instruct
+base_model_relation: adapter
+pipeline_tag: text-generation
+language:
+  - en
+datasets:
+  - manifesta/brandvoice-marketing-briefs
+tags:
+  - lora
+  - peft
+  - marketing
+  - brand-voice
+  - copywriting
+  - style-transfer
+  - llama
+  - autoscientist
+  - adaption
+inference: false
+model-index:
+  - name: brandvoice-marketing-model
+    results:
+      - task:
+          type: text-generation
+          name: Marketing copy generation
+        dataset:
+          name: BrandVoice Marketing Briefs (held-out)
+          type: manifesta/brandvoice-marketing-briefs
+          split: test
+        metrics:
+          - type: copy-quality
+            name: Copy quality, Adaption judge (0 to 10)
+            value: 8.7
+          - type: improvement-percent
+            name: Quality improvement vs base
+            value: 24.3
+          - type: win-rate
+            name: Win rate vs base
+            value: 56.0
+          - type: percentile
+            name: Quality percentile
+            value: 30
+        source:
+          name: Adaption AutoScientist held-out evaluation
+          url: https://huggingface.co/manifesta/brandvoice-marketing-model
+---
+
+<p align="center">
+  <img src="https://huggingface.co/manifesta/brandvoice-marketing-model/resolve/main/assets/banner.png" width="100%" alt="BrandVoice: a brand-voice adapter for marketing copy">
+</p>
+
+[![Live demo](https://img.shields.io/badge/Live_demo-brand--voice--lab-000?logo=vercel&logoColor=white)](https://brand-voice-lab.vercel.app)
+[![Base model](https://img.shields.io/badge/Base-Llama_3.3_70B-1B72E8?logo=meta&logoColor=white)](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct)
+[![Dataset](https://img.shields.io/badge/%F0%9F%A4%97_Dataset-brandvoice--marketing--briefs-FFD21E)](https://huggingface.co/datasets/manifesta/brandvoice-marketing-briefs)
+[![Quality](https://img.shields.io/badge/Copy_quality-%2B24.3%25-2ea44f)](#evaluation)
+[![Win rate](https://img.shields.io/badge/Win_rate-56%25-2ea44f)](#evaluation)
+[![Built with AutoScientist](https://img.shields.io/badge/Built_with-Adaption_AutoScientist-7C3AED)](https://adaptionlabs.ai)
+
+# BrandVoice Marketing Model
+
+A LoRA adapter that makes Llama 3.3 70B write marketing copy in a specific brand's voice instead of the flat, voiceless prose every model defaults to. Give it a brief and a brand-voice fingerprint, and it writes the headline, tagline, body, or CTA the way that brand actually sounds.
+
+This is the piece most challenge entries skip. The dataset is public, the evaluation is public, and the trained weights are public too. You can load this adapter and reproduce the result yourself.
+
+## Contents
+
+- [TL;DR](#tldr)
+- [Quickstart](#quickstart)
+- [Evaluation](#evaluation)
+- [Training](#training)
+- [Intended use and limits](#intended-use-and-limits)
+- [Dataset](#dataset)
+- [License](#license)
+- [Citation](#citation)
+
+## TL;DR
+
+- **What it is:** a PEFT LoRA adapter on `meta-llama/Llama-3.3-70B-Instruct`. Small, mergeable, cheap to serve.
+- **What it does:** turns a marketing brief plus a 15-field brand-voice fingerprint into short on-brand copy.
+- **The number:** copy quality went 7.0 to 8.7, a +24.3% relative lift, with a 56% win rate against the exact base it was trained on, scored by Adaption's held-out judge.
+- **Built by:** MANIFESTA (Aivaras Navardauskas) for the Adaption AutoScientist Challenge, Marketing.
+
+## Quickstart
+
+Load the base model, then attach the adapter with PEFT.
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+
+base_id = "meta-llama/Llama-3.3-70B-Instruct"   # gated, request access first
+adapter_id = "manifesta/brandvoice-marketing-model"
+
+tokenizer = AutoTokenizer.from_pretrained(base_id)
+base = AutoModelForCausalLM.from_pretrained(base_id, torch_dtype=torch.bfloat16, device_map="auto")
+model = PeftModel.from_pretrained(base, adapter_id)
+model.eval()
+
+brief = "Write a launch tweet for our new oat-milk latte. Voice: dry, confident, a little funny."
+messages = [{"role": "user", "content": brief}]
+inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
+out = model.generate(inputs, max_new_tokens=200, temperature=0.7)
+print(tokenizer.decode(out[0], skip_special_tokens=True))
+```
+
+One-line alternative that reads the base from `adapter_config.json`:
+
+```python
+from peft import AutoPeftModelForCausalLM
+model = AutoPeftModelForCausalLM.from_pretrained("manifesta/brandvoice-marketing-model")
+```
+
+## Evaluation
+
+Evaluated on a held-out set of marketing briefs the adapter never saw in training, scored by Adaption's AutoScientist judge. The base model is the same Llama 3.3 70B the adapter was trained on, so this is a clean before-and-after, not a comparison against a weaker model.
+
+| Metric | Base | BrandVoice | Change |
+| --- | --- | --- | --- |
+| Copy quality (0 to 10) | 7.0 | 8.7 | +24.3% |
+| Win rate vs base | 44% | 56% | +12 pts |
+| Quality percentile | 8.4 | 30.0 | +21.6 |
+| Eval loss | 1.543 | 1.375 | -0.168 |
+
+Win rate is head-to-head preference on the same briefs, judged blind. The +24.3% is the relative lift in mean judge score (7.0 to 8.7). One judge, one held-out split, reported honestly. No leaderboard cherry-picking.
+
+## Training
+
+Adapted with Adaption AutoScientist (Adaptive Data). The platform synthesizes a fitting prompt for each completion, conditions on the 15-field voice fingerprint, and trains the adapter.
+
+| Hyperparameter | Value |
+| --- | --- |
+| Base model | `meta-llama/Llama-3.3-70B-Instruct` |
+| Method | LoRA (PEFT) |
+| LoRA rank | 8 |
+| LoRA alpha | 8 |
+| LoRA dropout | 0.0 |
+| Target modules | `q_proj`, `v_proj` |
+| Learning rate | 3e-4 |
+| Epochs | 1 |
+| Precision | bfloat16 |
+| Weights export | Together AI |
+
+Recipe: `deduplication` and `prompt_rephrase` on, no `reasoning_traces` (copy is not chain-of-thought). A universal marketing blueprint rode on every completion: lead with the reader's benefit, be concrete, match the format and length, one clear call to action, never invent fake stats or claims.
+
+Honest note: this is a first, light run. One epoch and a small rank-8 adapter. The lift is real and the full pipeline (ingest, adapt, evaluate, export, publish) is proven end to end. Bigger runs are headroom, not a different method. We would rather publish a true 24.3% than a dressed-up number you cannot reproduce.
+
+## Intended use and limits
+
+**Use it for**
+
+- Generating on-brand marketing copy from a brief plus a voice fingerprint.
+- Rewriting a rough draft into a target brand's voice (style transfer).
+- Powering copywriting assistants and on-brand-ness tooling.
+
+**Do not use it for**
+
+- Passing generated text off as a brand's official communication.
+- Deceptive, fabricated, or unsubstantiated advertising claims.
+- Any high-stakes use (legal, medical, financial advice). This is a style model, not a source of truth.
+
+**Limits**
+
+- Voice fingerprints are inferred from public copy, not signed off by brands. A strong descriptive prior, not ground truth.
+- English only. Best at short form (headlines, taglines, short body), weaker on long narrative.
+- It shifts style, not the base model's knowledge or safety profile. Keep the base safety stack in place.
+- Strongly stylized voices can pull outputs toward edgy phrasing. Bound it with the blueprint and safety controls in production.
+
+## Dataset
+
+Trained on [BrandVoice Marketing Briefs](https://huggingface.co/datasets/manifesta/brandvoice-marketing-briefs), released CC0. 6,403 lines of real marketing copy from 83 brands, each line carrying a 15-field voice fingerprint (tone, formality, humor, rhythm, personality, signature moves, do and don't rules, vocabulary, voice summary, format, length, category, task) at a 100% fill rate. A split of brands is held out for evaluation, so the reported gain is on unseen voices.
+
+## License
+
+- **Adapter weights:** Apache-2.0.
+- **Base model:** governed by the [Llama 3.3 Community License](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct). You must comply with it to load and use the base.
+- **Training dataset:** CC0-1.0.
+- Brand names are referenced descriptively. Trademarks belong to their owners. Not affiliated with or endorsed by any brand named.
+
+## Citation
+
+```bibtex
+@misc{brandvoice2026,
+  title  = {BrandVoice: a brand-voice adapter and dataset for marketing copy},
+  author = {Navardauskas, Aivaras},
+  year   = {2026},
+  note   = {MANIFESTA. Adapted with Adaptive Data by Adaption, AutoScientist Challenge, Marketing.},
+  howpublished = {\url{https://huggingface.co/manifesta/brandvoice-marketing-model}}
+}
+```
+
+Credit: Adaptive Data by Adaption. Dataset and adapter built by MANIFESTA (Aivaras Navardauskas) for the AutoScientist Challenge.

adapter_config.json

@@ -0,0 +1,34 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference",
+  "bias": "none",
+  "corda_config": null,
+  "eva_config": null,
+  "exclude_modules": [],
+  "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": 8,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 8,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "v_proj",
+    "q_proj"
+  ],
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d29ea5a3bab3593fa6c88e829e34d042162ceebf52c1741959efa36b5afe619f
+size 65578776

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 %}

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "dtype": "bfloat16",
+  "eos_token_id": 128009,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 8192,
+  "initializer_range": 0.02,
+  "intermediate_size": 28672,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 64,
+  "num_hidden_layers": 80,
+  "num_key_value_heads": 8,
+  "pad_token_id": 128009,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 8.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.57.0",
+  "use_cache": false,
+  "vocab_size": 128256,
+  "torch_dtype": "bfloat16"
+}

special_tokens_map.json

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+    "lstrip": false,
+    "normalized": false,
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+    "normalized": false,
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+  "pad_token": "<|eot_id|>"
+}

tokenizer.json

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