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Model: nshportun/usa-immigration-llama-3.2-3b-v3 Source: Original Platform
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README.md
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README.md
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---
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language:
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- en
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license: llama3.2
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base_model: meta-llama/Llama-3.2-3B-Instruct
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library_name: transformers
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tags:
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- legal
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- immigration
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- fine-tuned
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- llama
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- united-states
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- lora
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datasets:
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- nshportun/usa-immigration-law-qa
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pipeline_tag: text-generation
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---
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# USA Immigration Law --- Llama 3.2 3B Fine-Tuned
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> **A 3B fine-tuned model that outperforms the Llama 3 8B zero-shot baseline on U.S. immigration law Q&A
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> (+27% mean score, 4x more fully-correct answers).**
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Fine-tuned from [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct)
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on the [nshportun/usa-immigration-law-qa](https://huggingface.co/datasets/nshportun/usa-immigration-law-qa)
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dataset --- **17,058 source-grounded Q&A pairs** covering all major U.S. immigration subdomains.
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## Benchmark Results
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Evaluated on 101 held-out questions scored 0-3 by Claude Sonnet 4.6 as judge:
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| Model | Mean Score (0-3) | % Fully Correct (3) |
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|-------|-----------------|---------------------|
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| Claude Sonnet 4.6 (zero-shot) | 1.515 | 24.8% |
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| **Llama 3.2 3B fine-tuned (this model)** | **1.079** | **16.8%** |
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| Llama 3 8B zero-shot | 0.851 | 4.0% |
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Domain-specific fine-tuning at 3B scale delivers **+27% higher mean score** and **4x more fully-correct answers**
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compared to a larger general-purpose 8B model.
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## Training Details
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| Setting | Value |
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|---------|-------|
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| LoRA rank (r) | 32 |
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| LoRA alpha | 64 |
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| Target modules | q_proj, v_proj, k_proj, o_proj |
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| LoRA dropout | 0.05 |
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| Epochs | 2 |
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| Learning rate | 5e-5 |
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| Batch size | 2 |
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| Max sequence length | 1024 |
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| Training pairs | 16,065 |
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| Infrastructure | ml.g5.2xlarge (AWS SageMaker) |
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## Usage
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_id = "nshportun/usa-immigration-llama-3.2-3b-v3"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
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messages = [
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{"role": "system", "content": "You are an expert on U.S. immigration law and policy. Answer accurately based on USCIS, 8 CFR, and BIA sources."},
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{"role": "user", "content": "What is the filing fee for Form I-485?"},
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]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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out = model.generate(**inputs, max_new_tokens=300, do_sample=False)
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print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
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```
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## Dataset
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The model was trained on [nshportun/usa-immigration-law-qa](https://huggingface.co/datasets/nshportun/usa-immigration-law-qa),
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a dataset of 17,058 source-grounded Q&A pairs from official U.S. immigration sources (USCIS Policy Manual,
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8 CFR/INA, BIA Precedent Decisions, USCIS Forms, DHS/CBP Statistics).
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## Disclaimer
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For **research and educational purposes only**. Not legal advice.
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Always consult a licensed immigration attorney.
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"FileName": "__script_info__.json",
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"Version": "1.2.3"
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}
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config.json
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}
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||||
24
special_tokens_map.json
Normal file
24
special_tokens_map.json
Normal file
@@ -0,0 +1,24 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<|begin_of_text|>",
|
||||
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||||
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|
||||
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|
||||
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|
||||
},
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
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|
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|
||||
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||||
"single_word": false
|
||||
},
|
||||
"unk_token": "<unk>"
|
||||
}
|
||||
410570
tokenizer.json
Normal file
410570
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
2085
tokenizer_config.json
Normal file
2085
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user