Add files using upload-large-folder tool

.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

MANIFEST.json

@@ -0,0 +1,15 @@
+{
+  "model_id": "adaption_llama_4_scout_17b_16_agri_evidence_qa_158a7fd8",
+  "files": [
+    {"file": "adapter_config.json", "bytes": 1017, "sha256": "3d9dfbeea1070c31a5f9939b364dac9b1ff98c111a0b715b6ba3616b316dd5ed"},
+    {"file": "adapter_model.safetensors", "bytes": 893484120, "sha256": "df51ce45612a8764559364fd383302ba5cc02da5c5becb0b4432cabcff527000"},
+    {"file": "autoscientist_config.json", "bytes": 896, "sha256": "f55a12fafac6bfe5e1f6919ec3387632b5db3e6bfc14c45c49cec89eaa025813"},
+    {"file": "chat_template.jinja", "bytes": 4949, "sha256": "01a91bfb2e84c8055bf7b635898fef3cae0b69e42ac5634c212d308e0e9091bd"},
+    {"file": "config.json", "bytes": 3741, "sha256": "a4ca627fb832858b898649a5c3259261b28f40455facdd681d815613926f7176"},
+    {"file": "evaluation.json", "bytes": 1068, "sha256": "5af3f1376df6970ad7e60d5a9eccf62168b847c893727c5bbb0da7c309d17e0b"},
+    {"file": "special_tokens_map.json", "bytes": 105, "sha256": "6c9d226d2e9b9a4be651dd981f04ba736a4b0136eac6499d9eb37ff91794d722"},
+    {"file": "tokenizer.json", "bytes": 27948578, "sha256": "172c9eb4beafc72601690da3ccfcede5c2e6806a8d5ec1fca33e22acea8023a4"},
+    {"file": "tokenizer_config.json", "bytes": 432, "sha256": "6a0a583a4792e366c1efa0e8bdf59e1e38c3630154bcecd240fecb0fdbdedba1"},
+    {"file": "trainer_state.json", "bytes": 30607, "sha256": "8f25a9df1fead37629e0c029aad254a33733131499b9673bef5abbbf93f219dd"}
+  ]
+}

README.md

@@ -1,3 +1,153 @@
 ---
+base_model: togethercomputer/Llama-4-Scout-17B-16E-Instruct_bnb_4bit
+library_name: peft
+pipeline_tag: text-generation
 license: llama4
+language:
+- en
+- es
+tags:
+- agriculture
+- climate
+- crop-calendar
+- evidence-grounding
+- bilingual
+- lora
+- autoscientist
+- adaption
+datasets:
+- AgroVeritas-Evidence-QA
 ---
+
+# AgroVeritas-Scout-17B
+
+> **Agricultural intelligence you can audit.**
+
+AgroVeritas-Scout-17B is a bilingual English–Spanish agricultural reasoning adapter for Llama 4 Scout, trained with Adaption AutoScientist. It is designed to answer regional crop-calendar and historical climate questions while making the evidence, reasoning, limitations, and need for local confirmation explicit.
+
+## The result that matters
+
+On Adaption's held-out **Agriculture category tasks**, the fine-tuned model achieved a **78% win rate** versus **23% for its frozen base model**.
+
+| Model | Agriculture win rate |
+|---|---:|
+| Frozen Llama 4 Scout baseline | 23% |
+| **AgroVeritas adapted model** | **78%** |
+
+That is a **+55 percentage-point improvement**, a **+239% relative lift**, and **3.39× the baseline win rate** under the platform's head-to-head evaluation.
+
+The training dataset also reached **A / 10.0 out of 10** on Adaption quality evaluation. The final same-run data score improved from **9.0 to 10.0 (+11.1% relative)**; the project as a whole progressed from an early **C / 5.0** prototype to the final **A / 10.0** release.
+
+The exact exported training artifact contains **28,220 rows**: **17,094 agriculture-core examples** and **11,126 AutoScientist expansion examples**, a **39.4% general-purpose diversity buffer**.
+
+## Why AgroVeritas exists
+
+Agricultural answers fail when models blur three different things: a published crop calendar, historical climate, and current field reality. AgroVeritas teaches a strict evidence contract:
+
+1. Give the answer that is supported by the record.
+2. Name the evidence used.
+3. Show the reasoning link.
+4. State what the evidence cannot establish.
+5. Direct the user to current local confirmation before operational decisions.
+
+This turns a generic assistant into an auditable agricultural evidence layer rather than an unqualified recommendation engine.
+
+## Model details
+
+- **AutoScientist model ID:** `adaption_llama_4_scout_17b_16_agri_evidence_qa_158a7fd8`
+- **Architecture:** Llama 4 Scout 17B active / 16 experts, 109B total parameters.
+- **Exported base reference:** `togethercomputer/Llama-4-Scout-17B-16E-Instruct_bnb_4bit`
+- **Adaptation:** PEFT LoRA supervised fine-tuning.
+- **Adapter rank / alpha:** 64 / 128.
+- **Languages:** English and Latin American Spanish.
+- **Domain:** Agriculture.
+- **Framework:** PEFT 0.15.1.
+- **License:** Llama 4 Community License. Base-model access and acceptable-use terms apply.
+
+## Training recipe
+
+| Parameter | Value |
+|---|---|
+| Epochs | 3 |
+| Learning rate | `1e-4` |
+| Scheduler | cosine |
+| Minimum LR ratio | 0.1 |
+| Warmup ratio | 0.1 |
+| Weight decay | 0.01 |
+| Maximum gradient norm | 1.0 |
+| LoRA rank | 64 |
+| LoRA alpha | 128 |
+| LoRA dropout | 0 |
+| Training method | SFT |
+| Train on inputs | false |
+| Evaluation checkpoints | 5 |
+
+LoRA was injected into `q_proj`, `k_proj`, `v_proj`, `o_proj`, and the shared-expert and feed-forward gate/up/down projections. Training completed 159 global steps across 3 epochs. Exported trainer metrics show validation loss moving from **0.7566 at the first recorded evaluation to 0.6824 at completion**.
+
+## Evaluation notes
+
+- The 78% value is the Adaption Agriculture category win rate shown for the selected `158a7fd8` run.
+- It is a held-out platform evaluation; organizer prompts are not disclosed.
+- The weaker retry ending in `2329bca3` is not this release.
+- The C→A claim describes successive project dataset versions; it is not substituted for the final same-run A→A evaluation.
+- No private AgroVeritas holdout or private evaluation fact group entered adaptation or training.
+
+## Intended use
+
+- Evidence-grounded crop-calendar questions.
+- Regional and historical climate explanation.
+- Bilingual agricultural assistants and research prototypes.
+- Workflows that need visible provenance, limitations, and calibrated uncertainty.
+
+## Out-of-scope use and safety
+
+Do not treat model output as live weather, field scouting, diagnosis, pesticide instructions, financial advice, or a guarantee of yield. Crop calendars and climatology can be outdated or locally incomplete. Verify material decisions with current authoritative forecasts, label instructions, local regulations, agronomists, and extension services.
+
+## Load the adapter
+
+The repository contains a PEFT adapter, not a standalone copy of the Llama 4 base model. You must separately obtain access to a compatible Llama 4 Scout checkpoint and comply with its license.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+
+base_id = "togethercomputer/Llama-4-Scout-17B-16E-Instruct_bnb_4bit"
+adapter_id = "REPLACE_WITH_HF_MODEL_REPO"
+
+tokenizer = AutoTokenizer.from_pretrained(adapter_id)
+base = AutoModelForCausalLM.from_pretrained(
+    base_id,
+    device_map="auto",
+    trust_remote_code=True,
+)
+model = PeftModel.from_pretrained(base, adapter_id)
+
+messages = [{
+    "role": "user",
+    "content": "Using cited crop-calendar and historical climate evidence, explain the planting window for maize in my region. State limitations and what I should verify locally."
+}]
+inputs = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    return_tensors="pt",
+).to(model.device)
+output = model.generate(inputs, max_new_tokens=600, do_sample=False)
+print(tokenizer.decode(output[0][inputs.shape[-1]:], skip_special_tokens=True))
+```
+
+## Open release
+
+The exact adapted dataset and this adapter are released publicly on both Hugging Face and Kaggle. The companion AutoScientist interface demonstrates bilingual evidence-bounded responses, and the submission film shows the problem, method, and verified platform results.
+
+## Citation
+
+```bibtex
+@software{agroveritas_scout_2026,
+  title        = {AgroVeritas-Scout-17B: Evidence-Bounded Bilingual Agriculture},
+  author       = {Sinisterra, Mariana},
+  year         = {2026},
+  note         = {Fine-tuned with AutoScientist by Adaption}
+}
+```
+
+Built with **Adaptive Data** and **AutoScientist** by Adaption.

adapter_config.json

@@ -0,0 +1,42 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "togethercomputer/Llama-4-Scout-17B-16E-Instruct_bnb_4bit",
+  "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": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "k_proj",
+    "shared_expert.down_proj",
+    "o_proj",
+    "shared_expert.gate_proj",
+    "feed_forward.gate_proj",
+    "shared_expert.up_proj",
+    "feed_forward.up_proj",
+    "v_proj",
+    "q_proj",
+    "feed_forward.down_proj"
+  ],
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:df51ce45612a8764559364fd383302ba5cc02da5c5becb0b4432cabcff527000
+size 893484120

autoscientist_config.json

@@ -0,0 +1,25 @@
+{
+  "model": "meta-llama/Llama-4-Scout-17B-16E-Instruct",
+  "exported_base_reference": "togethercomputer/Llama-4-Scout-17B-16E-Instruct_bnb_4bit",
+  "winning_autoscientist_model_id": "adaption_llama_4_scout_17b_16_agri_evidence_qa_158a7fd8",
+  "hyperparams": {
+    "base_model_size": "109B",
+    "n_epochs": 3,
+    "batch_size": "max",
+    "learning_rate": 0.0001,
+    "lora": true,
+    "lora_r": 64,
+    "lora_alpha": 128,
+    "lora_dropout": 0,
+    "lora_trainable_modules": "q_proj,k_proj,v_proj,o_proj,shared_expert.gate_proj,shared_expert.up_proj,shared_expert.down_proj,feed_forward.gate_proj,feed_forward.up_proj,feed_forward.down_proj",
+    "lr_scheduler_type": "cosine",
+    "min_lr_ratio": 0.1,
+    "scheduler_num_cycles": 0.5,
+    "warmup_ratio": 0.1,
+    "max_grad_norm": 1,
+    "weight_decay": 0.01,
+    "n_evals": 5,
+    "training_method": "sft",
+    "train_on_inputs": false
+  }
+}

chat_template.jinja

@@ -0,0 +1,123 @@
+{{- 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 %}
+    {%- if strftime_now is defined %}
+        {%- set date_string = strftime_now("%d %b %Y") %}
+    {%- else %}
+        {%- set date_string = "26 Jul 2024" %}
+    {%- endif %}
+{%- 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' %}    
+    {%- if messages[0]['content'] is string %}
+        {%- set system_message = messages[0]['content']|trim %}
+    {%- else %}
+        {#- FIXME: The processor requires an array, always. #}
+        {%- set system_message = messages[0]['content'][0]['text']|trim %}
+    {%- endif %}
+    {%- set messages = messages[1:] %}
+    {%- set user_supplied_system_message = true %}
+{%- else %}
+    {%- set system_message = "" %}
+    {%- set user_supplied_system_message = false %}
+{%- endif %}
+
+{#- System message if the user supplied one #}
+{%- if user_supplied_system_message %}
+    {{- "<|header_start|>system<|header_end|>\n\n" }}
+    {%- if tools is not none %}
+        {{- "Environment: ipython\n" }}
+    {%- endif %}
+    {%- 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|>" }}
+{%- endif %}
+
+{#- 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 %}
+    {{- '<|header_start|>user<|header_end|>\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|>"}}
+{%- endif %}
+
+{%- for message in messages %}
+    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
+    {{- '<|header_start|>' + message['role'] + '<|header_end|>\n\n' }}
+        {%- if message['content'] is string %}
+            {{- message['content'] }}
+        {%- else %}
+            {%- for content in message['content'] %}
+                {%- if content['type'] == 'image' %}
+                    {{- '<|image|>' }}
+                {%- elif content['type'] == 'text' %}
+                    {{- content['text'] }}
+                {%- endif %}
+            {%- endfor %}
+        {%- endif %}
+        {{- "<|eot|>" }}
+    {%- elif 'tool_calls' in message and message.tool_calls|length > 0 %}
+       {{- '<|header_start|>assistant<|header_end|>\n\n' -}}
+       {{- '<|python_start|>' }}
+        {%- if message['content'] is string %}
+            {{- message['content'] }}
+        {%- else %}
+            {%- for content in message['content'] %}
+                {%- if content['type'] == 'image' %}
+                    {{- '<|image|>' }}
+                {%- elif content['type'] == 'text' %}
+                    {{- content['text'] }}
+                {%- endif %}
+            {%- endfor %}
+        {%- endif %}
+       {{- '<|python_end|>' }}
+        {%- for tool_call in message.tool_calls %}
+           {{- '{"name": "' + tool_call.function.name + '", ' }}
+           {{- '"parameters": ' }}
+           {{- tool_call.function.arguments | tojson }}
+           {{- "}" }}
+        {%- endfor %}
+       {{- "<|eot|>" }}
+    {%- elif message.role == "tool" or message.role == "ipython" %}
+        {{- "<|header_start|>ipython<|header_end|>\n\n" }}
+        {%- if message.content is mapping or message.content is iterable %}
+            {{- message.content | tojson }}
+        {%- else %}
+            {{- message.content }}
+        {%- endif %}
+        {{- "<|eot|>" }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|header_start|>assistant<|header_end|>\n\n' }}
+{%- endif %}

config.json

@@ -0,0 +1,215 @@
+{
+  "architectures": [
+    "Llama4ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_chunk_size": 8192,
+  "attention_dropout": 0.0,
+  "attn_scale": 0.1,
+  "attn_temperature_tuning": true,
+  "bos_token_id": 200000,
+  "cache_implementation": "hybrid",
+  "dtype": "bfloat16",
+  "eos_token_id": 200008,
+  "floor_scale": 8192,
+  "for_llm_compressor": false,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "interleave_moe_layer_step": 1,
+  "intermediate_size": 8192,
+  "intermediate_size_mlp": 16384,
+  "layer_types": [
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+  "pad_token_id": 200018,
+  "quantization_config": {
+    "_load_in_4bit": true,
+    "_load_in_8bit": false,
+    "bnb_4bit_compute_dtype": "bfloat16",
+    "bnb_4bit_quant_storage": "bfloat16",
+    "bnb_4bit_quant_type": "nf4",
+    "bnb_4bit_use_double_quant": false,
+    "llm_int8_enable_fp32_cpu_offload": false,
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+    "quant_method": "bitsandbytes"
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+  "rope_parameters": {
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+  "torch_dtype": "bfloat16"
+}

evaluation.json

@@ -0,0 +1,30 @@
+{
+  "release_model_id": "adaption_llama_4_scout_17b_16_agri_evidence_qa_158a7fd8",
+  "category": "Agriculture",
+  "evaluation_source": "Adaption AutoScientist held-out Agriculture category tasks",
+  "base_win_rate_percent": 23,
+  "adapted_win_rate_percent": 78,
+  "absolute_improvement_percentage_points": 55,
+  "relative_lift_percent": 239.1,
+  "adapted_to_base_ratio": 3.391,
+  "training": {
+    "global_steps": 159,
+    "epochs": 3,
+    "first_recorded_eval_loss": 0.756591796875,
+    "final_eval_loss": 0.682373046875
+  },
+  "data_quality": {
+    "same_run_score_before": 9.0,
+    "same_run_score_after": 10.0,
+    "same_run_relative_improvement_percent": 11.1,
+    "grade_before": "A",
+    "grade_after": "A",
+    "percentile_before": 43.9,
+    "percentile_after": 57.7
+  },
+  "claim_boundaries": [
+    "The held-out evaluator prompts are private and are not published.",
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