FarmBot v1 — model + honest benchmark results

README.md

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+---
+language: en
+license: mit
+base_model: HuggingFaceTB/SmolLM2-135M-Instruct
+tags:
+  - agriculture
+  - crop-disease
+  - africa
+  - farmbot
+  - lora
+  - int4
+---
+
+# FarmBot — SmolLM2-135M (INT4, LoRA Finetuned)
+
+A LoRA finetune of SmolLM2-135M-Instruct for crop disease assistance, covering 9 crops
+common in West/Central Africa. Quantized to INT4 (~111MB) for low-resource deployment.
+Built for the USAII Global AI Hackathon 2026.
+
+## Crops Covered
+Cassava, Cocoa, Cowpea, Maize, Groundnut, Mango, Plantain, Rice, Tomato
+
+## Benchmark Results (15-question held-out test, keyword-match scoring)
+
+| Bucket | Score |
+|---|---|
+| Overall | 7/15 (46.7%) |
+| Crop knowledge | 6/9 (67%) |
+| Greetings | 1/2 (50%) |
+| Out-of-scope | 0/4 (0%) |
+
+See `benchmark_results.json` for full per-question results.
+
+This is not a polished production model. Crop-knowledge answers are generally accurate
+and on-topic (correctly identifies fall armyworm, cassava mosaic, black pod disease, bunchy
+top, rice blast, cowpea aphids with reasonable treatment advice). Out-of-scope detection is
+weak in raw model output — the model often starts the correct decline phrase but drifts into
+unrelated crop advice instead of stopping. Greetings handling is inconsistent.
+
+## Known Limitations
+
+- Out-of-scope detection fails most of the time in raw model output — **a regex pre-filter
+  at the application layer is required** before deploying this model to reliably handle
+  off-topic questions (sports, prices, politics, human/animal health, etc.)
+- Responses can ramble past the useful answer and drift off-topic toward the end
+- 135M parameters — limited reasoning, trained narrowly on 9 crops only, will not generalize
+  to crops or diseases outside its training data
+- Should be treated as an early-stage assistive tool, not a substitute for an agricultural
+  extension officer
+
+## Training
+
+- Base: HuggingFaceTB/SmolLM2-135M-Instruct
+- LoRA: r=16, alpha=32, dropout=0.05, targeting q/k/v/o projections (~1.84M trainable params)
+- Data: ~95,000 quality-filtered examples (deduplicated, length-bounded, repetition-checked),
+  sampled from a larger 365k+ synthetic Q&A dataset generated via Mistral API
+- Hardware: Kaggle 2x T4
+- Quantization: INT4 nf4 with double quant
+
+## Recommended Inference Settings
+
+```python
+temperature=0.3, top_k=20, repetition_penalty=1.2
+max_new_tokens=120, min_new_tokens=15
+eos_token_id=tokenizer.convert_tokens_to_ids('<|im_end|>')
+```
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained("rufatronics/farmbot-crop-assistant")
+model = AutoModelForCausalLM.from_pretrained("rufatronics/farmbot-crop-assistant", device_map="auto")
+
+prompt = "<|im_start|>user\nmy maize leaves have holes<|im_end|>\n<|im_start|>assistant\n"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+out = model.generate(
+    **inputs, max_new_tokens=120, min_new_tokens=15,
+    temperature=0.3, top_k=20, do_sample=True, repetition_penalty=1.2,
+    pad_token_id=tokenizer.eos_token_id,
+    eos_token_id=tokenizer.convert_tokens_to_ids('<|im_end|>'),
+)
+print(tokenizer.decode(out[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True))
+```

benchmark_results.json

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+{
+  "overall": "7/15 (46.7%)",
+  "crop_knowledge": "6/9 (67%)",
+  "greetings": "1/2 (50%)",
+  "out_of_scope": "0/4 (0%)",
+  "details": [
+    {
+      "passed": true,
+      "score": 0.6666666666666666,
+      "bucket": "crop_knowledge"
+    },
+    {
+      "passed": true,
+      "score": 0.6666666666666666,
+      "bucket": "crop_knowledge"
+    },
+    {
+      "passed": true,
+      "score": 0.6666666666666666,
+      "bucket": "crop_knowledge"
+    },
+    {
+      "passed": true,
+      "score": 1.0,
+      "bucket": "crop_knowledge"
+    },
+    {
+      "passed": false,
+      "score": 0.3333333333333333,
+      "bucket": "crop_knowledge"
+    },
+    {
+      "passed": false,
+      "score": 0.0,
+      "bucket": "crop_knowledge"
+    },
+    {
+      "passed": true,
+      "score": 1.0,
+      "bucket": "crop_knowledge"
+    },
+    {
+      "passed": true,
+      "score": 1.0,
+      "bucket": "crop_knowledge"
+    },
+    {
+      "passed": false,
+      "score": 0.0,
+      "bucket": "crop_knowledge"
+    },
+    {
+      "passed": true,
+      "score": 1.0,
+      "bucket": "greetings"
+    },
+    {
+      "passed": false,
+      "score": 0.3333333333333333,
+      "bucket": "greetings"
+    },
+    {
+      "passed": false,
+      "score": 0.3333333333333333,
+      "bucket": "out_of_scope"
+    },
+    {
+      "passed": false,
+      "score": 0.3333333333333333,
+      "bucket": "out_of_scope"
+    },
+    {
+      "passed": false,
+      "score": 0.0,
+      "bucket": "out_of_scope"
+    },
+    {
+      "passed": false,
+      "score": 0.3333333333333333,
+      "bucket": "out_of_scope"
+    }
+  ]
+}

config.json

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+{
+  "_name_or_path": "/kaggle/working/farmbot_smol",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 576,
+  "initializer_range": 0.041666666666666664,
+  "intermediate_size": 1536,
+  "is_llama_config": true,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 9,
+  "num_hidden_layers": 30,
+  "num_key_value_heads": 3,
+  "pad_token_id": 2,
+  "pretraining_tp": 1,
+  "quantization_config": {
+    "_load_in_4bit": true,
+    "_load_in_8bit": false,
+    "bnb_4bit_compute_dtype": "float16",
+    "bnb_4bit_quant_storage": "uint8",
+    "bnb_4bit_quant_type": "nf4",
+    "bnb_4bit_use_double_quant": true,
+    "llm_int8_enable_fp32_cpu_offload": false,
+    "llm_int8_has_fp16_weight": false,
+    "llm_int8_skip_modules": null,
+    "llm_int8_threshold": 6.0,
+    "load_in_4bit": true,
+    "load_in_8bit": false,
+    "quant_method": "bitsandbytes"
+  },
+  "rms_norm_eps": 1e-05,
+  "rope_interleaved": false,
+  "rope_scaling": null,
+  "rope_theta": 100000,
+  "tie_word_embeddings": true,
+  "torch_dtype": "float16",
+  "transformers.js_config": {
+    "kv_cache_dtype": {
+      "fp16": "float16",
+      "q4f16": "float16"
+    }
+  },
+  "transformers_version": "4.46.3",
+  "use_cache": false,
+  "vocab_size": 49152
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 2,
+  "transformers_version": "4.46.3"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3865c97294f848b9e1b3856be83a17ca00bcf3b59ccbf49906103e6f58e71209
+size 111879472

special_tokens_map.json

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+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>"
+  ],
+  "bos_token": {
+    "content": "<|im_start|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<repo_name>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "content": "<reponame>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "5": {
+      "content": "<file_sep>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "6": {
+      "content": "<filename>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "7": {
+      "content": "<gh_stars>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "8": {
+      "content": "<issue_start>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "9": {
+      "content": "<issue_comment>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+    "10": {
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+    "16": {
+      "content": "<empty_output>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>"
+  ],
+  "bos_token": "<|im_start|>",
+  "chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "max_length": 1024,
+  "model_max_length": 8192,
+  "pad_token": "<|im_end|>",
+  "stride": 0,
+  "tokenizer_class": "GPT2Tokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "<|endoftext|>",
+  "vocab_size": 49152
+}