Instructions to use deepforce/deepforce-coder-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use deepforce/deepforce-coder-v1 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="deepforce/deepforce-coder-v1", filename="deepforce-coder-v1-q4_k_m.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use deepforce/deepforce-coder-v1 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 deepforce/deepforce-coder-v1:Q4_K_M # Run inference directly in the terminal: llama cli -hf deepforce/deepforce-coder-v1:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf deepforce/deepforce-coder-v1:Q4_K_M # Run inference directly in the terminal: llama cli -hf deepforce/deepforce-coder-v1: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 deepforce/deepforce-coder-v1:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf deepforce/deepforce-coder-v1: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 deepforce/deepforce-coder-v1:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf deepforce/deepforce-coder-v1:Q4_K_M
Use Docker
docker model run hf.co/deepforce/deepforce-coder-v1:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use deepforce/deepforce-coder-v1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "deepforce/deepforce-coder-v1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "deepforce/deepforce-coder-v1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/deepforce/deepforce-coder-v1:Q4_K_M
- Ollama
How to use deepforce/deepforce-coder-v1 with Ollama:
ollama run hf.co/deepforce/deepforce-coder-v1:Q4_K_M
- Unsloth Studio
How to use deepforce/deepforce-coder-v1 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 deepforce/deepforce-coder-v1 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 deepforce/deepforce-coder-v1 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for deepforce/deepforce-coder-v1 to start chatting
- Pi
How to use deepforce/deepforce-coder-v1 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf deepforce/deepforce-coder-v1: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": "deepforce/deepforce-coder-v1:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use deepforce/deepforce-coder-v1 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf deepforce/deepforce-coder-v1: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 deepforce/deepforce-coder-v1:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use deepforce/deepforce-coder-v1 with Docker Model Runner:
docker model run hf.co/deepforce/deepforce-coder-v1:Q4_K_M
- Lemonade
How to use deepforce/deepforce-coder-v1 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull deepforce/deepforce-coder-v1:Q4_K_M
Run and chat with the model
lemonade run user.deepforce-coder-v1-Q4_K_M
List all available models
lemonade list
DeepForce Coder v1
A Salesforce-specialized AI coding assistant fine-tuned on Qwen 2.5 Coder 3B. Built specifically for Salesforce developers to generate, debug, review, and refactor Apex code and Lightning Web Components following enterprise best practices.
Capabilities
| Task | Description |
|---|---|
| Apex Generation | Write production-ready Apex classes, triggers, batch, scheduled, queueable |
| LWC Development | Create Lightning Web Components with SLDS conventions |
| Code Debug | Identify bugs with severity ratings and corrections |
| Code Review | Review code against Salesforce best practices |
| Refactoring | Simplify over-engineered code while preserving security |
| Test Classes | Generate comprehensive Apex test classes |
Best Practices Enforced
with sharingon all classesWITH USER_MODEon all SOQL queriesSecurity.stripInaccessible()before DMLtry-catchon all DML and calloutsDatabase.update/insert(records, false)for bulk DML- No SOQL or DML inside loops
- Bulkified trigger handlers with recursion guards
Model Details
- Base model: Qwen/Qwen2.5-Coder-3B-Instruct
- Fine-tuning: LoRA adapters across 8 specialized Salesforce tasks
- Training data: curated Salesforce-specific examples
- Quantization: Q4_K_M GGUF (1.80 GB)
- Context length: 6144 tokens
Quick Start
Ollama
ollama run hf.co/deepforce/deepforce-coder-v1:Q4_K_M
llama.cpp
llama serve -hf deepforce/deepforce-coder-v1:Q4_K_M
Python (llama-cpp-python)
from llama_cpp import Llama
llm = Llama.from_pretrained(
repo_id = "deepforce/deepforce-coder-v1",
filename = "deepforce-coder-v1-q4_k_m.gguf",
)
response = llm.create_chat_completion(messages=[
{"role": "system", "content": "You are DeepForce Coder, an expert Salesforce developer."},
{"role": "user", "content": "Write a simple Apex class that returns Accounts by industry."}
])
print(response["choices"][0]["message"]["content"])
Example Prompts
Generate Apex:
Write a trigger handler for Opportunity that creates a follow-up Task when StageName changes to Closed Won.
Debug Apex: Debug the following Apex code: [paste your code]
Review Apex: Review the following Apex code for best practices: [paste your code]
Generate LWC: Create an LWC component that displays a list of Accounts in a lightning-datatable.
Limitations
- v1 release โ some outputs may occasionally use Java syntax patterns
- Test class generation uses System.assertEquals instead of Assert class in some cases
- These will be fixed in v2
Training
Fine-tuned using Unsloth on Google Colab. Training data generated using Anthropic Claude API.
License
Apache 2.0 โ free for commercial and personal use.
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