Instructions to use Z-Nadjib/Llama-3.2-3B-ClauseLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Z-Nadjib/Llama-3.2-3B-ClauseLM with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Z-Nadjib/Llama-3.2-3B-ClauseLM")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Z-Nadjib/Llama-3.2-3B-ClauseLM")
model = AutoModelForCausalLM.from_pretrained("Z-Nadjib/Llama-3.2-3B-ClauseLM", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Z-Nadjib/Llama-3.2-3B-ClauseLM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Z-Nadjib/Llama-3.2-3B-ClauseLM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Z-Nadjib/Llama-3.2-3B-ClauseLM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Z-Nadjib/Llama-3.2-3B-ClauseLM

SGLang

How to use Z-Nadjib/Llama-3.2-3B-ClauseLM with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Z-Nadjib/Llama-3.2-3B-ClauseLM" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Z-Nadjib/Llama-3.2-3B-ClauseLM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Z-Nadjib/Llama-3.2-3B-ClauseLM" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Z-Nadjib/Llama-3.2-3B-ClauseLM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use Z-Nadjib/Llama-3.2-3B-ClauseLM with Docker Model Runner:
```
docker model run hf.co/Z-Nadjib/Llama-3.2-3B-ClauseLM
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Llama-3.2-3B CUAD Clause Extractor

A 3B parameter model that reads a commercial contract and returns a JSON object with the passages belonging to each of the 41 clause categories of CUAD: parties, dates, renewal terms, governing law, termination rights, restrictive covenants, licences, liability caps, insurance, audit rights and more. Every value is a verbatim span of the contract.

It is Llama-3.2-3B-Instruct fine-tuned with QLoRA on the CUAD training contracts, with the LoRA weights merged into the 16-bit base. Built with Llama.

Code, training and evaluation: github.com/Z-Nadjib/ClauseLM-ContractExtraction-CUAD

Results

102 contracts of the official CUAD test split, document level. A span is correct when its word-level Jaccard similarity with a gold span of the same field is at least 0.5.

System	Span F1 (micro)	Span F1 (macro)	Presence F1	Grounded spans	Usable JSON	Latency / contract
This model	61.7	50.4	83.8	97.4%	100%	27.5 s
Llama-3.2-3B-Instruct, zero-shot	9.6	5.0	49.5	20.9%	100%	40.5 s
Qwen2.5-1.5B + QLoRA (same recipe)	57.2	43.2	80.9	98.6%	100%	18.0 s
Legal-BERT NER (token classification)	42.7	30.2	80.8	100%	n/a	0.08 s

Latency is measured on one RTX 5080 (16 GB) with transformers in bf16, all windows of a contract in one batch. At an on-demand rental price of USD 0.40 per GPU hour this is USD 0.0031 per contract; the electricity cost at 360 W and USD 0.25 per kWh is USD 0.00069.

Best fields: governing law (92 F1), document name (88), agreement date (82), expiration date (82). Weakest fields: affiliate license of the licensor (0), IP ownership assignment (17), post-termination services (18), volume restriction (24).

Output format

Given a contract excerpt of up to about 6,000 characters, the model returns a compact JSON object containing only the fields it found. Values are lists of strings.

{"document_name": ["SUPPLY CONTRACT"], "insurance": ["To be covered by the Seller for 110% invoice value against All Risks and War Risk."]}

Field keys: document_name, parties, agreement_date, effective_date, expiration_date, renewal_term, notice_period_to_terminate_renewal, governing_law, most_favored_nation, non_compete, exclusivity, no_solicit_of_customers, competitive_restriction_exception, no_solicit_of_employees, non_disparagement, termination_for_convenience, rofr_rofo_rofn, change_of_control, anti_assignment, revenue_profit_sharing, price_restrictions, minimum_commitment, volume_restriction, ip_ownership_assignment, joint_ip_ownership, license_grant, non_transferable_license, affiliate_license_licensor, affiliate_license_licensee, unlimited_all_you_can_eat_license, irrevocable_or_perpetual_license, source_code_escrow, post_termination_services, audit_rights, uncapped_liability, cap_on_liability, liquidated_damages, warranty_duration, insurance, covenant_not_to_sue, third_party_beneficiary.

How to use

The model was trained with a fixed system prompt that lists the field definitions. The simplest way to get identical inputs, long-document chunking, grammar-constrained decoding and merging is the project package:

from contract_extractor.inference.base import ChunkedExtractor
from contract_extractor.inference.hf_backend import HFBackend

backend = HFBackend("Z-Nadjib/Llama-3.2-3B-ClauseLM", constrained=True)
extractor = ChunkedExtractor(backend, chunk_size=6000, chunk_overlap=600, batch_size=8)
result = extractor.extract(open("contract.txt").read())
print(result.extraction)

With transformers only, for one excerpt:

from transformers import AutoModelForCausalLM, AutoTokenizer
from contract_extractor.prompts import build_messages  # or copy SYSTEM_PROMPT from the repository

tokenizer = AutoTokenizer.from_pretrained("Z-Nadjib/Llama-3.2-3B-ClauseLM")
model = AutoModelForCausalLM.from_pretrained("Z-Nadjib/Llama-3.2-3B-ClauseLM", dtype="auto", device_map="auto")
prompt = tokenizer.apply_chat_template(build_messages(excerpt), tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to(model.device)
output = model.generate(**inputs, max_new_tokens=2048, do_sample=False, repetition_penalty=1.0)
print(tokenizer.decode(output[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Use greedy decoding without repetition penalty, as set in the bundled generation config: the outputs copy the input text, and a repetition penalty degrades copying.

Training


Data	CUAD v1, 367 training contracts, 41 held out for validation (official train split)
Examples	3,402 windows of 6,000 characters with 600 characters of overlap, 21% without any clause
Method	QLoRA (4-bit NF4 base), LoRA r=32, alpha=32, all linear projections
Framework	Unsloth 2026.10.9, TRL 1.15.0, transformers 5.17.0
Optimisation	AdamW 8-bit, learning rate 2e-4, cosine schedule, 3% warmup, effective batch 16, 2 epochs scheduled
Selected checkpoint	step 180 of 426 (0.85 epoch), lowest validation loss
Loss	completion (JSON) tokens only
Sequence length	4,096 tokens
Hardware	one NVIDIA RTX 5080 (16 GB), about 50 minutes

Evaluation

The full protocol, per-field scores and the constrained decoding ablation are in the project repository.

Limitations

Trained on English contracts filed with the US SEC. Other languages, jurisdictions and document types are out of distribution.
Spans are verbatim and not normalized (dates are returned as written).
Clause categories with few training examples (for example Source Code Escrow or Unlimited/All-You-Can-Eat License) are less reliable.
The output is an aid for contract review, not legal advice.

License

Llama 3.2 Community License, inherited from Llama-3.2-3B-Instruct (license, acceptable use policy). Training data: CUAD, CC BY 4.0, The Atticus Project.

Citation

@article{hendrycks2021cuad,
  title   = {CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
  author  = {Hendrycks, Dan and Burns, Collin and Chen, Anya and Ball, Spencer},
  journal = {NeurIPS Datasets and Benchmarks},
  year    = {2021}
}

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Safetensors

Model size

3B params

Tensor type

BF16

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Llama-3.2-3B-ClauseLM