Text Generation
Transformers
GGUF
English
lean4
theorem-proving
formal-mathematics
TensorBlock
GGUF
conversational
Instructions to use tensorblock/BFS-Prover-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tensorblock/BFS-Prover-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="tensorblock/BFS-Prover-GGUF") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("tensorblock/BFS-Prover-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use tensorblock/BFS-Prover-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 tensorblock/BFS-Prover-GGUF:Q2_K # Run inference directly in the terminal: llama cli -hf tensorblock/BFS-Prover-GGUF:Q2_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf tensorblock/BFS-Prover-GGUF:Q2_K # Run inference directly in the terminal: llama cli -hf tensorblock/BFS-Prover-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 tensorblock/BFS-Prover-GGUF:Q2_K # Run inference directly in the terminal: ./llama-cli -hf tensorblock/BFS-Prover-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 tensorblock/BFS-Prover-GGUF:Q2_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf tensorblock/BFS-Prover-GGUF:Q2_K
Use Docker
docker model run hf.co/tensorblock/BFS-Prover-GGUF:Q2_K
- LM Studio
- Jan
- vLLM
How to use tensorblock/BFS-Prover-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tensorblock/BFS-Prover-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tensorblock/BFS-Prover-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/tensorblock/BFS-Prover-GGUF:Q2_K
- SGLang
How to use tensorblock/BFS-Prover-GGUF 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 "tensorblock/BFS-Prover-GGUF" \ --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": "tensorblock/BFS-Prover-GGUF", "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 "tensorblock/BFS-Prover-GGUF" \ --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": "tensorblock/BFS-Prover-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use tensorblock/BFS-Prover-GGUF with Ollama:
ollama run hf.co/tensorblock/BFS-Prover-GGUF:Q2_K
- Unsloth Studio
How to use tensorblock/BFS-Prover-GGUF 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 tensorblock/BFS-Prover-GGUF 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 tensorblock/BFS-Prover-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for tensorblock/BFS-Prover-GGUF to start chatting
- Pi
How to use tensorblock/BFS-Prover-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf tensorblock/BFS-Prover-GGUF:Q2_K
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": "tensorblock/BFS-Prover-GGUF:Q2_K" } ] } } }Run Pi
# Start Pi in your project directory: pi
- OpenClaw new
How to use tensorblock/BFS-Prover-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf tensorblock/BFS-Prover-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 "tensorblock/BFS-Prover-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"
- Docker Model Runner
How to use tensorblock/BFS-Prover-GGUF with Docker Model Runner:
docker model run hf.co/tensorblock/BFS-Prover-GGUF:Q2_K
- Lemonade
How to use tensorblock/BFS-Prover-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tensorblock/BFS-Prover-GGUF:Q2_K
Run and chat with the model
lemonade run user.BFS-Prover-GGUF-Q2_K
List all available models
lemonade list
- Hermes Agent
How to use tensorblock/BFS-Prover-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 tensorblock/BFS-Prover-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 tensorblock/BFS-Prover-GGUF:Q2_K
Run Hermes
hermes
- Atomic Chat
Upload folder using huggingface_hub
Browse files- .gitattributes +12 -0
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---
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| 2 |
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license: apache-2.0
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datasets:
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| 4 |
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- internlm/Lean-Workbook
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| 5 |
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- internlm/Lean-Github
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- AI-MO/NuminaMath-CoT
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language:
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- en
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| 9 |
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base_model: bytedance-research/BFS-Prover
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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| 13 |
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- lean4
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| 14 |
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- theorem-proving
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- formal-mathematics
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- TensorBlock
|
| 17 |
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- GGUF
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| 18 |
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---
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| 19 |
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| 20 |
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<div style="width: auto; margin-left: auto; margin-right: auto">
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| 21 |
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<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
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| 22 |
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</div>
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| 23 |
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<div style="display: flex; justify-content: space-between; width: 100%;">
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| 24 |
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<div style="display: flex; flex-direction: column; align-items: flex-start;">
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| 25 |
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<p style="margin-top: 0.5em; margin-bottom: 0em;">
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| 26 |
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Feedback and support: TensorBlock's <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a>
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| 27 |
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</p>
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| 28 |
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</div>
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| 29 |
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</div>
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| 30 |
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## bytedance-research/BFS-Prover - GGUF
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This repo contains GGUF format model files for [bytedance-research/BFS-Prover](https://huggingface.co/bytedance-research/BFS-Prover).
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The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4823](https://github.com/ggml-org/llama.cpp/commit/5bbe6a9fe9a8796a9389c85accec89dbc4d91e39).
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<div style="text-align: left; margin: 20px 0;">
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| 38 |
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<a href="https://tensorblock.co/waitlist/client" style="display: inline-block; padding: 10px 20px; background-color: #007bff; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;">
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| 39 |
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Run them on the TensorBlock client using your local machine ↗
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| 40 |
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</a>
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| 41 |
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</div>
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| 42 |
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| 43 |
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## Prompt template
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| 44 |
+
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| 45 |
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```
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| 46 |
+
<|im_start|>system
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| 47 |
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{system_prompt}<|im_end|>
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| 48 |
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<|im_start|>user
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| 49 |
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{prompt}<|im_end|>
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| 50 |
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<|im_start|>assistant
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| 51 |
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```
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| 52 |
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| 53 |
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## Model file specification
|
| 54 |
+
|
| 55 |
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| Filename | Quant type | File Size | Description |
|
| 56 |
+
| -------- | ---------- | --------- | ----------- |
|
| 57 |
+
| [BFS-Prover-Q2_K.gguf](https://huggingface.co/tensorblock/BFS-Prover-GGUF/blob/main/BFS-Prover-Q2_K.gguf) | Q2_K | 3.016 GB | smallest, significant quality loss - not recommended for most purposes |
|
| 58 |
+
| [BFS-Prover-Q3_K_S.gguf](https://huggingface.co/tensorblock/BFS-Prover-GGUF/blob/main/BFS-Prover-Q3_K_S.gguf) | Q3_K_S | 3.492 GB | very small, high quality loss |
|
| 59 |
+
| [BFS-Prover-Q3_K_M.gguf](https://huggingface.co/tensorblock/BFS-Prover-GGUF/blob/main/BFS-Prover-Q3_K_M.gguf) | Q3_K_M | 3.808 GB | very small, high quality loss |
|
| 60 |
+
| [BFS-Prover-Q3_K_L.gguf](https://huggingface.co/tensorblock/BFS-Prover-GGUF/blob/main/BFS-Prover-Q3_K_L.gguf) | Q3_K_L | 4.088 GB | small, substantial quality loss |
|
| 61 |
+
| [BFS-Prover-Q4_0.gguf](https://huggingface.co/tensorblock/BFS-Prover-GGUF/blob/main/BFS-Prover-Q4_0.gguf) | Q4_0 | 4.431 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
|
| 62 |
+
| [BFS-Prover-Q4_K_S.gguf](https://huggingface.co/tensorblock/BFS-Prover-GGUF/blob/main/BFS-Prover-Q4_K_S.gguf) | Q4_K_S | 4.458 GB | small, greater quality loss |
|
| 63 |
+
| [BFS-Prover-Q4_K_M.gguf](https://huggingface.co/tensorblock/BFS-Prover-GGUF/blob/main/BFS-Prover-Q4_K_M.gguf) | Q4_K_M | 4.683 GB | medium, balanced quality - recommended |
|
| 64 |
+
| [BFS-Prover-Q5_0.gguf](https://huggingface.co/tensorblock/BFS-Prover-GGUF/blob/main/BFS-Prover-Q5_0.gguf) | Q5_0 | 5.315 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
|
| 65 |
+
| [BFS-Prover-Q5_K_S.gguf](https://huggingface.co/tensorblock/BFS-Prover-GGUF/blob/main/BFS-Prover-Q5_K_S.gguf) | Q5_K_S | 5.315 GB | large, low quality loss - recommended |
|
| 66 |
+
| [BFS-Prover-Q5_K_M.gguf](https://huggingface.co/tensorblock/BFS-Prover-GGUF/blob/main/BFS-Prover-Q5_K_M.gguf) | Q5_K_M | 5.445 GB | large, very low quality loss - recommended |
|
| 67 |
+
| [BFS-Prover-Q6_K.gguf](https://huggingface.co/tensorblock/BFS-Prover-GGUF/blob/main/BFS-Prover-Q6_K.gguf) | Q6_K | 6.254 GB | very large, extremely low quality loss |
|
| 68 |
+
| [BFS-Prover-Q8_0.gguf](https://huggingface.co/tensorblock/BFS-Prover-GGUF/blob/main/BFS-Prover-Q8_0.gguf) | Q8_0 | 8.099 GB | very large, extremely low quality loss - not recommended |
|
| 69 |
+
|
| 70 |
+
|
| 71 |
+
## Downloading instruction
|
| 72 |
+
|
| 73 |
+
### Command line
|
| 74 |
+
|
| 75 |
+
Firstly, install Huggingface Client
|
| 76 |
+
|
| 77 |
+
```shell
|
| 78 |
+
pip install -U "huggingface_hub[cli]"
|
| 79 |
+
```
|
| 80 |
+
|
| 81 |
+
Then, downoad the individual model file the a local directory
|
| 82 |
+
|
| 83 |
+
```shell
|
| 84 |
+
huggingface-cli download tensorblock/BFS-Prover-GGUF --include "BFS-Prover-Q2_K.gguf" --local-dir MY_LOCAL_DIR
|
| 85 |
+
```
|
| 86 |
+
|
| 87 |
+
If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:
|
| 88 |
+
|
| 89 |
+
```shell
|
| 90 |
+
huggingface-cli download tensorblock/BFS-Prover-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
|
| 91 |
+
```
|