Instructions to use j30231/Llama-3.3-70B-Instruct_Q2_K.gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use j30231/Llama-3.3-70B-Instruct_Q2_K.gguf with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf j30231/Llama-3.3-70B-Instruct_Q2_K.gguf:Q2_K # Run inference directly in the terminal: llama cli -hf j30231/Llama-3.3-70B-Instruct_Q2_K.gguf:Q2_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf j30231/Llama-3.3-70B-Instruct_Q2_K.gguf:Q2_K # Run inference directly in the terminal: llama cli -hf j30231/Llama-3.3-70B-Instruct_Q2_K.gguf:Q2_K
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 j30231/Llama-3.3-70B-Instruct_Q2_K.gguf:Q2_K # Run inference directly in the terminal: ./llama-cli -hf j30231/Llama-3.3-70B-Instruct_Q2_K.gguf:Q2_K
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 j30231/Llama-3.3-70B-Instruct_Q2_K.gguf:Q2_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf j30231/Llama-3.3-70B-Instruct_Q2_K.gguf:Q2_K
Use Docker
docker model run hf.co/j30231/Llama-3.3-70B-Instruct_Q2_K.gguf:Q2_K
- LM Studio
- Jan
- Ollama
How to use j30231/Llama-3.3-70B-Instruct_Q2_K.gguf with Ollama:
ollama run hf.co/j30231/Llama-3.3-70B-Instruct_Q2_K.gguf:Q2_K
- Unsloth Desktop
- Pi
How to use j30231/Llama-3.3-70B-Instruct_Q2_K.gguf with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf j30231/Llama-3.3-70B-Instruct_Q2_K.gguf:Q2_K
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "j30231/Llama-3.3-70B-Instruct_Q2_K.gguf:Q2_K" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use j30231/Llama-3.3-70B-Instruct_Q2_K.gguf with Docker Model Runner:
docker model run hf.co/j30231/Llama-3.3-70B-Instruct_Q2_K.gguf:Q2_K
- Lemonade
How to use j30231/Llama-3.3-70B-Instruct_Q2_K.gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull j30231/Llama-3.3-70B-Instruct_Q2_K.gguf:Q2_K
Run and chat with the model
lemonade run user.Llama-3.3-70B-Instruct_Q2_K.gguf-Q2_K
List all available models
lemonade list
- Hermes Agent
How to use j30231/Llama-3.3-70B-Instruct_Q2_K.gguf with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf j30231/Llama-3.3-70B-Instruct_Q2_K.gguf:Q2_K
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 j30231/Llama-3.3-70B-Instruct_Q2_K.gguf:Q2_K
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use j30231/Llama-3.3-70B-Instruct_Q2_K.gguf with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf j30231/Llama-3.3-70B-Instruct_Q2_K.gguf:Q2_K
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "j30231/Llama-3.3-70B-Instruct_Q2_K.gguf:Q2_K" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
| { | |
| "metadata": { | |
| "model_name": "llama-3.3-70b-instruct", | |
| "timestamp": "20241212-013440" | |
| }, | |
| "categories": { | |
| "STEM": { | |
| "subjects": [ | |
| { | |
| "subject": "high_school_chemistry", | |
| "correct_rate": 64.03940886699507 | |
| }, | |
| { | |
| "subject": "high_school_mathematics", | |
| "correct_rate": 46.666666666666664 | |
| }, | |
| { | |
| "subject": "abstract_algebra", | |
| "correct_rate": 48.0 | |
| }, | |
| { | |
| "subject": "computer_security", | |
| "correct_rate": 84.0 | |
| }, | |
| { | |
| "subject": "college_computer_science", | |
| "correct_rate": 61.61616161616161 | |
| }, | |
| { | |
| "subject": "college_chemistry", | |
| "correct_rate": 53.0 | |
| }, | |
| { | |
| "subject": "conceptual_physics", | |
| "correct_rate": 74.8936170212766 | |
| }, | |
| { | |
| "subject": "high_school_statistics", | |
| "correct_rate": 68.05555555555556 | |
| }, | |
| { | |
| "subject": "college_mathematics", | |
| "correct_rate": 44.0 | |
| }, | |
| { | |
| "subject": "college_biology", | |
| "correct_rate": 88.19444444444444 | |
| }, | |
| { | |
| "subject": "college_physics", | |
| "correct_rate": 52.94117647058824 | |
| }, | |
| { | |
| "subject": "elementary_mathematics", | |
| "correct_rate": 64.81481481481481 | |
| }, | |
| { | |
| "subject": "high_school_biology", | |
| "correct_rate": 88.70967741935483 | |
| }, | |
| { | |
| "subject": "high_school_physics", | |
| "correct_rate": 57.615894039735096 | |
| }, | |
| { | |
| "subject": "machine_learning", | |
| "correct_rate": 56.25 | |
| }, | |
| { | |
| "subject": "astronomy", | |
| "correct_rate": 88.1578947368421 | |
| }, | |
| { | |
| "subject": "electrical_engineering", | |
| "correct_rate": 69.6551724137931 | |
| }, | |
| { | |
| "subject": "high_school_computer_science", | |
| "correct_rate": 79.0 | |
| } | |
| ], | |
| "correct_rate": 66.09 | |
| }, | |
| "humanities": { | |
| "subjects": [ | |
| { | |
| "subject": "world_religions", | |
| "correct_rate": 84.7953216374269 | |
| }, | |
| { | |
| "subject": "high_school_us_history", | |
| "correct_rate": 89.70588235294117 | |
| }, | |
| { | |
| "subject": "moral_disputes", | |
| "correct_rate": 77.74566473988439 | |
| }, | |
| { | |
| "subject": "high_school_world_history", | |
| "correct_rate": 88.60759493670885 | |
| }, | |
| { | |
| "subject": "formal_logic", | |
| "correct_rate": 62.698412698412696 | |
| }, | |
| { | |
| "subject": "international_law", | |
| "correct_rate": 85.12396694214877 | |
| }, | |
| { | |
| "subject": "jurisprudence", | |
| "correct_rate": 76.85185185185185 | |
| }, | |
| { | |
| "subject": "professional_law", | |
| "correct_rate": 59.58279009126467 | |
| }, | |
| { | |
| "subject": "logical_fallacies", | |
| "correct_rate": 83.43558282208589 | |
| }, | |
| { | |
| "subject": "philosophy", | |
| "correct_rate": 74.27652733118971 | |
| }, | |
| { | |
| "subject": "moral_scenarios", | |
| "correct_rate": 78.65921787709496 | |
| }, | |
| { | |
| "subject": "prehistory", | |
| "correct_rate": 84.25925925925925 | |
| }, | |
| { | |
| "subject": "high_school_european_history", | |
| "correct_rate": 84.84848484848484 | |
| } | |
| ], | |
| "correct_rate": 79.28 | |
| }, | |
| "social sciences": { | |
| "subjects": [ | |
| { | |
| "subject": "high_school_geography", | |
| "correct_rate": 86.36363636363636 | |
| }, | |
| { | |
| "subject": "high_school_psychology", | |
| "correct_rate": 91.19266055045871 | |
| }, | |
| { | |
| "subject": "sociology", | |
| "correct_rate": 87.56218905472637 | |
| }, | |
| { | |
| "subject": "high_school_microeconomics", | |
| "correct_rate": 86.5546218487395 | |
| }, | |
| { | |
| "subject": "professional_psychology", | |
| "correct_rate": 76.79738562091504 | |
| }, | |
| { | |
| "subject": "security_studies", | |
| "correct_rate": 77.55102040816327 | |
| }, | |
| { | |
| "subject": "us_foreign_policy", | |
| "correct_rate": 91.0 | |
| }, | |
| { | |
| "subject": "public_relations", | |
| "correct_rate": 70.9090909090909 | |
| }, | |
| { | |
| "subject": "high_school_government_and_politics", | |
| "correct_rate": 93.78238341968913 | |
| }, | |
| { | |
| "subject": "econometrics", | |
| "correct_rate": 61.40350877192983 | |
| }, | |
| { | |
| "subject": "human_sexuality", | |
| "correct_rate": 81.67938931297711 | |
| }, | |
| { | |
| "subject": "high_school_macroeconomics", | |
| "correct_rate": 80.51282051282051 | |
| } | |
| ], | |
| "correct_rate": 82.11 | |
| }, | |
| "other (business, health, misc.)": { | |
| "subjects": [ | |
| { | |
| "subject": "virology", | |
| "correct_rate": 53.6144578313253 | |
| }, | |
| { | |
| "subject": "college_medicine", | |
| "correct_rate": 72.25433526011561 | |
| }, | |
| { | |
| "subject": "global_facts", | |
| "correct_rate": 62.0 | |
| }, | |
| { | |
| "subject": "miscellaneous", | |
| "correct_rate": 87.35632183908046 | |
| }, | |
| { | |
| "subject": "medical_genetics", | |
| "correct_rate": 84.0 | |
| }, | |
| { | |
| "subject": "human_aging", | |
| "correct_rate": 78.47533632286996 | |
| }, | |
| { | |
| "subject": "nutrition", | |
| "correct_rate": 83.33333333333334 | |
| }, | |
| { | |
| "subject": "marketing", | |
| "correct_rate": 88.88888888888889 | |
| }, | |
| { | |
| "subject": "anatomy", | |
| "correct_rate": 71.85185185185186 | |
| }, | |
| { | |
| "subject": "professional_medicine", | |
| "correct_rate": 88.23529411764706 | |
| }, | |
| { | |
| "subject": "professional_accounting", | |
| "correct_rate": 56.02836879432624 | |
| }, | |
| { | |
| "subject": "management", | |
| "correct_rate": 82.52427184466019 | |
| }, | |
| { | |
| "subject": "clinical_knowledge", | |
| "correct_rate": 80.75471698113208 | |
| }, | |
| { | |
| "subject": "business_ethics", | |
| "correct_rate": 74.0 | |
| } | |
| ], | |
| "correct_rate": 75.95 | |
| } | |
| }, | |
| "subjects": { | |
| "high_school_geography": { | |
| "correct_rate": 86.36363636363636, | |
| "category": "social sciences" | |
| }, | |
| "virology": { | |
| "correct_rate": 53.6144578313253, | |
| "category": "other (business, health, misc.)" | |
| }, | |
| "world_religions": { | |
| "correct_rate": 84.7953216374269, | |
| "category": "humanities" | |
| }, | |
| "college_medicine": { | |
| "correct_rate": 72.25433526011561, | |
| "category": "other (business, health, misc.)" | |
| }, | |
| "high_school_chemistry": { | |
| "correct_rate": 64.03940886699507, | |
| "category": "STEM" | |
| }, | |
| "high_school_mathematics": { | |
| "correct_rate": 46.666666666666664, | |
| "category": "STEM" | |
| }, | |
| "global_facts": { | |
| "correct_rate": 62.0, | |
| "category": "other (business, health, misc.)" | |
| }, | |
| "high_school_psychology": { | |
| "correct_rate": 91.19266055045871, | |
| "category": "social sciences" | |
| }, | |
| "abstract_algebra": { | |
| "correct_rate": 48.0, | |
| "category": "STEM" | |
| }, | |
| "computer_security": { | |
| "correct_rate": 84.0, | |
| "category": "STEM" | |
| }, | |
| "sociology": { | |
| "correct_rate": 87.56218905472637, | |
| "category": "social sciences" | |
| }, | |
| "college_computer_science": { | |
| "correct_rate": 61.61616161616161, | |
| "category": "STEM" | |
| }, | |
| "high_school_us_history": { | |
| "correct_rate": 89.70588235294117, | |
| "category": "humanities" | |
| }, | |
| "miscellaneous": { | |
| "correct_rate": 87.35632183908046, | |
| "category": "other (business, health, misc.)" | |
| }, | |
| "high_school_microeconomics": { | |
| "correct_rate": 86.5546218487395, | |
| "category": "social sciences" | |
| }, | |
| "college_chemistry": { | |
| "correct_rate": 53.0, | |
| "category": "STEM" | |
| }, | |
| "moral_disputes": { | |
| "correct_rate": 77.74566473988439, | |
| "category": "humanities" | |
| }, | |
| "medical_genetics": { | |
| "correct_rate": 84.0, | |
| "category": "other (business, health, misc.)" | |
| }, | |
| "professional_psychology": { | |
| "correct_rate": 76.79738562091504, | |
| "category": "social sciences" | |
| }, | |
| "high_school_world_history": { | |
| "correct_rate": 88.60759493670885, | |
| "category": "humanities" | |
| }, | |
| "security_studies": { | |
| "correct_rate": 77.55102040816327, | |
| "category": "social sciences" | |
| }, | |
| "conceptual_physics": { | |
| "correct_rate": 74.8936170212766, | |
| "category": "STEM" | |
| }, | |
| "us_foreign_policy": { | |
| "correct_rate": 91.0, | |
| "category": "social sciences" | |
| }, | |
| "human_aging": { | |
| "correct_rate": 78.47533632286996, | |
| "category": "other (business, health, misc.)" | |
| }, | |
| "public_relations": { | |
| "correct_rate": 70.9090909090909, | |
| "category": "social sciences" | |
| }, | |
| "high_school_statistics": { | |
| "correct_rate": 68.05555555555556, | |
| "category": "STEM" | |
| }, | |
| "formal_logic": { | |
| "correct_rate": 62.698412698412696, | |
| "category": "humanities" | |
| }, | |
| "nutrition": { | |
| "correct_rate": 83.33333333333334, | |
| "category": "other (business, health, misc.)" | |
| }, | |
| "marketing": { | |
| "correct_rate": 88.88888888888889, | |
| "category": "other (business, health, misc.)" | |
| }, | |
| "college_mathematics": { | |
| "correct_rate": 44.0, | |
| "category": "STEM" | |
| }, | |
| "international_law": { | |
| "correct_rate": 85.12396694214877, | |
| "category": "humanities" | |
| }, | |
| "anatomy": { | |
| "correct_rate": 71.85185185185186, | |
| "category": "other (business, health, misc.)" | |
| }, | |
| "jurisprudence": { | |
| "correct_rate": 76.85185185185185, | |
| "category": "humanities" | |
| }, | |
| "college_biology": { | |
| "correct_rate": 88.19444444444444, | |
| "category": "STEM" | |
| }, | |
| "college_physics": { | |
| "correct_rate": 52.94117647058824, | |
| "category": "STEM" | |
| }, | |
| "professional_law": { | |
| "correct_rate": 59.58279009126467, | |
| "category": "humanities" | |
| }, | |
| "high_school_government_and_politics": { | |
| "correct_rate": 93.78238341968913, | |
| "category": "social sciences" | |
| }, | |
| "professional_medicine": { | |
| "correct_rate": 88.23529411764706, | |
| "category": "other (business, health, misc.)" | |
| }, | |
| "logical_fallacies": { | |
| "correct_rate": 83.43558282208589, | |
| "category": "humanities" | |
| }, | |
| "professional_accounting": { | |
| "correct_rate": 56.02836879432624, | |
| "category": "other (business, health, misc.)" | |
| }, | |
| "philosophy": { | |
| "correct_rate": 74.27652733118971, | |
| "category": "humanities" | |
| }, | |
| "moral_scenarios": { | |
| "correct_rate": 78.65921787709496, | |
| "category": "humanities" | |
| }, | |
| "management": { | |
| "correct_rate": 82.52427184466019, | |
| "category": "other (business, health, misc.)" | |
| }, | |
| "elementary_mathematics": { | |
| "correct_rate": 64.81481481481481, | |
| "category": "STEM" | |
| }, | |
| "prehistory": { | |
| "correct_rate": 84.25925925925925, | |
| "category": "humanities" | |
| }, | |
| "high_school_biology": { | |
| "correct_rate": 88.70967741935483, | |
| "category": "STEM" | |
| }, | |
| "high_school_physics": { | |
| "correct_rate": 57.615894039735096, | |
| "category": "STEM" | |
| }, | |
| "machine_learning": { | |
| "correct_rate": 56.25, | |
| "category": "STEM" | |
| }, | |
| "high_school_european_history": { | |
| "correct_rate": 84.84848484848484, | |
| "category": "humanities" | |
| }, | |
| "econometrics": { | |
| "correct_rate": 61.40350877192983, | |
| "category": "social sciences" | |
| }, | |
| "clinical_knowledge": { | |
| "correct_rate": 80.75471698113208, | |
| "category": "other (business, health, misc.)" | |
| }, | |
| "human_sexuality": { | |
| "correct_rate": 81.67938931297711, | |
| "category": "social sciences" | |
| }, | |
| "high_school_macroeconomics": { | |
| "correct_rate": 80.51282051282051, | |
| "category": "social sciences" | |
| }, | |
| "astronomy": { | |
| "correct_rate": 88.1578947368421, | |
| "category": "STEM" | |
| }, | |
| "electrical_engineering": { | |
| "correct_rate": 69.6551724137931, | |
| "category": "STEM" | |
| }, | |
| "high_school_computer_science": { | |
| "correct_rate": 79.0, | |
| "category": "STEM" | |
| }, | |
| "business_ethics": { | |
| "correct_rate": 74.0, | |
| "category": "other (business, health, misc.)" | |
| } | |
| }, | |
| "overall_correct_rate": 74.89 | |
| } |