Instructions to use teclabs/llama-capro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use teclabs/llama-capro 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 teclabs/llama-capro:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf teclabs/llama-capro:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf teclabs/llama-capro:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf teclabs/llama-capro: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 teclabs/llama-capro:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf teclabs/llama-capro: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 teclabs/llama-capro:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf teclabs/llama-capro:Q4_K_M

Use Docker

docker model run hf.co/teclabs/llama-capro:Q4_K_M

LM Studio
Jan

vLLM

How to use teclabs/llama-capro with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/teclabs/llama-capro:Q4_K_M

Ollama
How to use teclabs/llama-capro with Ollama:
```
ollama run hf.co/teclabs/llama-capro:Q4_K_M
```
Unsloth Desktop

How to use teclabs/llama-capro with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf teclabs/llama-capro:Q4_K_M

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": "teclabs/llama-capro:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use teclabs/llama-capro with Docker Model Runner:
```
docker model run hf.co/teclabs/llama-capro:Q4_K_M
```

Lemonade

How to use teclabs/llama-capro with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull teclabs/llama-capro:Q4_K_M

Run and chat with the model

lemonade run user.llama-capro-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use teclabs/llama-capro with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf teclabs/llama-capro: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 teclabs/llama-capro:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use teclabs/llama-capro with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf teclabs/llama-capro:Q4_K_M

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 "teclabs/llama-capro:Q4_K_M" \
  --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"

teclabs commited on Nov 14, 2025

Commit

678e29e

verified ·

1 Parent(s): fd7b0be

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	@@ -0,0 +1,107 @@

+---
+license: llama3.1
+base_model: meta-llama/Llama-3.1-8B-Instruct
+tags:
+  - llama
+  - llama-3
+  - gguf
+  - quantized
+  - indian-accounting
+  - ind-as
+  - finance
+  - accounting
+  - ca
+language:
+  - en
+pipeline_tag: text-generation
+---
+# Llama-CAPRO (CA Professional) - Indian Accounting Standards Expert
+CAPRO is a specialized AI assistant for Chartered Accountants and finance professionals, fine-tuned on Indian Accounting Standards (Ind AS).
+## Model Details
+- **Base Model**: meta-llama/Llama-3.1-8B-Instruct
+- **Fine-tuned Adapter**: [0xadityam/llama-aica](https://huggingface.co/0xadityam/llama-aica)
+- **Domain**: Indian Accounting Standards (Ind AS)
+- **Format**: GGUF (Ollama, LM Studio, llama.cpp ready)
+- **Training**: LoRA fine-tuned on 269 Ind AS examples
+## Available Quantizations
+| Format | Size | Use Case | RAM Required |
+|--------|------|----------|--------------|
+| F16 | ~15GB | Maximum quality | 20GB+ |
+| Q5_K_M | ~5.5GB | Good quality, laptops | 8-12GB |
+| Q4_K_M | ~4.5GB | Balanced, edge devices | 6-8GB |
+## Quick Start with Ollama
+Download Q4_K_M (recommended)
+wget https://huggingface.co/teclabs/llama-capro/resolve/main/llama-capro-q4_k_m.gguf
+Create model
+ollama create llama-capro -f Modelfile-q4-k-m
+Run
+ollama run llama-capro
+## Sample Queries
+- "What is the objective of Ind AS 1?"
+- "Explain revenue recognition under Ind AS 115"
+- "What are disclosure requirements for financial instruments?"
+- "How should goodwill be accounted for under Ind AS?"
+- "What is the difference between Ind AS and IFRS?"
+## Modelfile (Ollama)
+FROM llama-capro-q4_k_m.gguf
+PARAMETER temperature 0.7
+PARAMETER top_p 0.9
+PARAMETER stop "<|eot_id|>"
+PARAMETER num_ctx 2048
+SYSTEM "You are CAPRO (CA Professional), an expert on Indian Accounting Standards (Ind AS). Provide accurate answers about Ind AS regulations and accounting policies."
+## Use with LM Studio
+1. Download LM Studio from [lmstudio.ai](https://lmstudio.ai)
+2. Click "Import" → "Import GGUF"
+3. Select the downloaded .gguf file
+4. Start chatting
+## Training Details
+- **LoRA Rank**: 64
+- **LoRA Alpha**: 16
+- **Training Epochs**: 3
+- **Learning Rate**: 2e-4
+- **Max Sequence Length**: 2048
+- **Training Data**: 269 examples covering Ind AS 1 and related standards
+## Limitations
+- Specialized for Indian Accounting Standards
+- Not suitable for other accounting standards (IFRS, US GAAP)
+- Responses should be verified with official documents
+- Not for legal or investment advice
+## License
+Llama 3.1 Community License
+## Acknowledgements
+- Base model: [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct)
+- LoRA adapter: [0xadityam/llama-aica](https://huggingface.co/0xadityam/llama-aica)
+- Conversion: llama.cpp
+---
+**CAPRO** - CA Professional AI Assistant
+Generated: 2025-11-14