Token Classification
Transformers
GGUF
French
English
mistral
privacy
anonymization
pii
legal
compliance
gdpr
rgpd
ner
on-premise
sovereign-ai
slm
privamesh
imatrix
conversational
Instructions to use sallani/PrivaMesh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sallani/PrivaMesh with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="sallani/PrivaMesh") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("sallani/PrivaMesh") model = AutoModelForTokenClassification.from_pretrained("sallani/PrivaMesh") - llama-cpp-python
How to use sallani/PrivaMesh with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="sallani/PrivaMesh", filename="privamesh-legal-Q4_K_M.gguf", )
llm.create_chat_completion( messages = "\"My name is Sarah Jessica Parker but you can call me Jessica\"" )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use sallani/PrivaMesh with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf sallani/PrivaMesh:Q4_K_M # Run inference directly in the terminal: llama-cli -hf sallani/PrivaMesh:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf sallani/PrivaMesh:Q4_K_M # Run inference directly in the terminal: llama-cli -hf sallani/PrivaMesh:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf sallani/PrivaMesh:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf sallani/PrivaMesh:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf sallani/PrivaMesh:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf sallani/PrivaMesh:Q4_K_M
Use Docker
docker model run hf.co/sallani/PrivaMesh:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use sallani/PrivaMesh with Ollama:
ollama run hf.co/sallani/PrivaMesh:Q4_K_M
- Unsloth Studio new
How to use sallani/PrivaMesh with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for sallani/PrivaMesh to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for sallani/PrivaMesh to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for sallani/PrivaMesh to start chatting
- Pi new
How to use sallani/PrivaMesh with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf sallani/PrivaMesh:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "sallani/PrivaMesh:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use sallani/PrivaMesh with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf sallani/PrivaMesh:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default sallani/PrivaMesh:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use sallani/PrivaMesh with Docker Model Runner:
docker model run hf.co/sallani/PrivaMesh:Q4_K_M
- Lemonade
How to use sallani/PrivaMesh with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull sallani/PrivaMesh:Q4_K_M
Run and chat with the model
lemonade run user.PrivaMesh-Q4_K_M
List all available models
lemonade list
File size: 6,486 Bytes
4c46869 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 | {
"_name_or_path": "privamesh/privamesh-legal",
"architectures": ["MistralForTokenClassification"],
"model_type": "mistral",
"base_model": "mistralai/Mistral-7B-v0.1",
"num_labels": 97,
"id2label": {
"0": "O",
"1": "B-PERSON_NAME", "2": "I-PERSON_NAME", "3": "E-PERSON_NAME", "4": "S-PERSON_NAME",
"5": "B-LEGAL_COUNSEL", "6": "I-LEGAL_COUNSEL", "7": "E-LEGAL_COUNSEL", "8": "S-LEGAL_COUNSEL",
"9": "B-JUDGE_NAME", "10": "I-JUDGE_NAME", "11": "E-JUDGE_NAME", "12": "S-JUDGE_NAME",
"13": "B-SIGNATORY", "14": "I-SIGNATORY", "15": "E-SIGNATORY", "16": "S-SIGNATORY",
"17": "B-WITNESS", "18": "I-WITNESS", "19": "E-WITNESS", "20": "S-WITNESS",
"21": "B-COMPANY_NAME", "22": "I-COMPANY_NAME", "23": "E-COMPANY_NAME", "24": "S-COMPANY_NAME",
"25": "B-COMPANY_ID", "26": "I-COMPANY_ID", "27": "E-COMPANY_ID", "28": "S-COMPANY_ID",
"29": "B-COURT_NAME", "30": "I-COURT_NAME", "31": "E-COURT_NAME", "32": "S-COURT_NAME",
"33": "B-BAR_ASSOCIATION", "34": "I-BAR_ASSOCIATION", "35": "E-BAR_ASSOCIATION", "36": "S-BAR_ASSOCIATION",
"37": "B-CONTRACT_AMOUNT", "38": "I-CONTRACT_AMOUNT", "39": "E-CONTRACT_AMOUNT", "40": "S-CONTRACT_AMOUNT",
"41": "B-BANK_ACCOUNT", "42": "I-BANK_ACCOUNT", "43": "E-BANK_ACCOUNT", "44": "S-BANK_ACCOUNT",
"45": "B-PENALTY_AMOUNT", "46": "I-PENALTY_AMOUNT", "47": "E-PENALTY_AMOUNT", "48": "S-PENALTY_AMOUNT",
"49": "B-PRIVATE_ADDRESS", "50": "I-PRIVATE_ADDRESS", "51": "E-PRIVATE_ADDRESS", "52": "S-PRIVATE_ADDRESS",
"53": "B-PRIVATE_EMAIL", "54": "I-PRIVATE_EMAIL", "55": "E-PRIVATE_EMAIL", "56": "S-PRIVATE_EMAIL",
"57": "B-PRIVATE_PHONE", "58": "I-PRIVATE_PHONE", "59": "E-PRIVATE_PHONE", "60": "S-PRIVATE_PHONE",
"61": "B-CONTRACT_DATE", "62": "I-CONTRACT_DATE", "63": "E-CONTRACT_DATE", "64": "S-CONTRACT_DATE",
"65": "B-DEADLINE", "66": "I-DEADLINE", "67": "E-DEADLINE", "68": "S-DEADLINE",
"69": "B-CASE_NUMBER", "70": "I-CASE_NUMBER", "71": "E-CASE_NUMBER", "72": "S-CASE_NUMBER",
"73": "B-DATA_SUBJECT", "74": "I-DATA_SUBJECT", "75": "E-DATA_SUBJECT", "76": "S-DATA_SUBJECT",
"77": "B-DPO_IDENTITY", "78": "I-DPO_IDENTITY", "79": "E-DPO_IDENTITY", "80": "S-DPO_IDENTITY",
"81": "B-PROCESSING_PURPOSE","82": "I-PROCESSING_PURPOSE","83": "E-PROCESSING_PURPOSE","84": "S-PROCESSING_PURPOSE",
"85": "B-AUDIT_REFERENCE", "86": "I-AUDIT_REFERENCE", "87": "E-AUDIT_REFERENCE", "88": "S-AUDIT_REFERENCE",
"89": "B-REGULATORY_BODY", "90": "I-REGULATORY_BODY", "91": "E-REGULATORY_BODY", "92": "S-REGULATORY_BODY",
"93": "B-DIRIGEANT", "94": "I-DIRIGEANT", "95": "E-DIRIGEANT", "96": "S-DIRIGEANT"
},
"label2id": {
"O": 0,
"B-PERSON_NAME": 1, "I-PERSON_NAME": 2, "E-PERSON_NAME": 3, "S-PERSON_NAME": 4,
"B-LEGAL_COUNSEL": 5, "I-LEGAL_COUNSEL": 6, "E-LEGAL_COUNSEL": 7, "S-LEGAL_COUNSEL": 8,
"B-JUDGE_NAME": 9, "I-JUDGE_NAME": 10, "E-JUDGE_NAME": 11, "S-JUDGE_NAME": 12,
"B-SIGNATORY": 13, "I-SIGNATORY": 14, "E-SIGNATORY": 15, "S-SIGNATORY": 16,
"B-WITNESS": 17, "I-WITNESS": 18, "E-WITNESS": 19, "S-WITNESS": 20,
"B-COMPANY_NAME": 21, "I-COMPANY_NAME": 22, "E-COMPANY_NAME": 23, "S-COMPANY_NAME": 24,
"B-COMPANY_ID": 25, "I-COMPANY_ID": 26, "E-COMPANY_ID": 27, "S-COMPANY_ID": 28,
"B-COURT_NAME": 29, "I-COURT_NAME": 30, "E-COURT_NAME": 31, "S-COURT_NAME": 32,
"B-BAR_ASSOCIATION": 33, "I-BAR_ASSOCIATION": 34, "E-BAR_ASSOCIATION": 35, "S-BAR_ASSOCIATION": 36,
"B-CONTRACT_AMOUNT": 37, "I-CONTRACT_AMOUNT": 38, "E-CONTRACT_AMOUNT": 39, "S-CONTRACT_AMOUNT": 40,
"B-BANK_ACCOUNT": 41, "I-BANK_ACCOUNT": 42, "E-BANK_ACCOUNT": 43, "S-BANK_ACCOUNT": 44,
"B-PENALTY_AMOUNT": 45, "I-PENALTY_AMOUNT": 46, "E-PENALTY_AMOUNT": 47, "S-PENALTY_AMOUNT": 48,
"B-PRIVATE_ADDRESS": 49, "I-PRIVATE_ADDRESS": 50, "E-PRIVATE_ADDRESS": 51, "S-PRIVATE_ADDRESS": 52,
"B-PRIVATE_EMAIL": 53, "I-PRIVATE_EMAIL": 54, "E-PRIVATE_EMAIL": 55, "S-PRIVATE_EMAIL": 56,
"B-PRIVATE_PHONE": 57, "I-PRIVATE_PHONE": 58, "E-PRIVATE_PHONE": 59, "S-PRIVATE_PHONE": 60,
"B-CONTRACT_DATE": 61, "I-CONTRACT_DATE": 62, "E-CONTRACT_DATE": 63, "S-CONTRACT_DATE": 64,
"B-DEADLINE": 65, "I-DEADLINE": 66, "E-DEADLINE": 67, "S-DEADLINE": 68,
"B-CASE_NUMBER": 69, "I-CASE_NUMBER": 70, "E-CASE_NUMBER": 71, "S-CASE_NUMBER": 72,
"B-DATA_SUBJECT": 73, "I-DATA_SUBJECT": 74, "E-DATA_SUBJECT": 75, "S-DATA_SUBJECT": 76,
"B-DPO_IDENTITY": 77, "I-DPO_IDENTITY": 78, "E-DPO_IDENTITY": 79, "S-DPO_IDENTITY": 80,
"B-PROCESSING_PURPOSE": 81,"I-PROCESSING_PURPOSE": 82,"E-PROCESSING_PURPOSE": 83,"S-PROCESSING_PURPOSE": 84,
"B-AUDIT_REFERENCE": 85, "I-AUDIT_REFERENCE": 86, "E-AUDIT_REFERENCE": 87, "S-AUDIT_REFERENCE": 88,
"B-REGULATORY_BODY": 89, "I-REGULATORY_BODY": 90, "E-REGULATORY_BODY": 91, "S-REGULATORY_BODY": 92,
"B-DIRIGEANT": 93, "I-DIRIGEANT": 94, "E-DIRIGEANT": 95, "S-DIRIGEANT": 96
},
"hidden_size": 4096,
"intermediate_size": 14336,
"max_position_embeddings": 32768,
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"rms_norm_eps": 1e-05,
"rope_theta": 10000.0,
"sliding_window": 4096,
"vocab_size": 32000,
"torch_dtype": "bfloat16",
"transformers_version": "4.40.0",
"task_specific_params": {
"token-classification": {
"scheme": "BIOES",
"operating_points": {
"high_recall": {"threshold": 0.35},
"balanced": {"threshold": 0.50},
"high_precision": {"threshold": 0.70}
}
}
},
"privamesh": {
"version": "1.0.0",
"domain": "legal",
"languages": ["fr", "en"],
"regulatory_coverage": ["RGPD", "DORA", "NIS2", "ISO27001", "ISO42001", "EU_AI_ACT"],
"mesh_role": "specialist",
"orchestrator_compatible": true,
"next_models": ["privamesh-finance", "privamesh-medical", "privamesh-orchestrator"]
}
}
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