Instructions to use aneeq-hashmi/SalesforceCoder-Qwen3.5-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aneeq-hashmi/SalesforceCoder-Qwen3.5-9B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="aneeq-hashmi/SalesforceCoder-Qwen3.5-9B",
	filename="files/mmproj/Qwen3.5-9B.BF16-mmproj.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use aneeq-hashmi/SalesforceCoder-Qwen3.5-9B 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 aneeq-hashmi/SalesforceCoder-Qwen3.5-9B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf aneeq-hashmi/SalesforceCoder-Qwen3.5-9B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf aneeq-hashmi/SalesforceCoder-Qwen3.5-9B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf aneeq-hashmi/SalesforceCoder-Qwen3.5-9B: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 aneeq-hashmi/SalesforceCoder-Qwen3.5-9B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf aneeq-hashmi/SalesforceCoder-Qwen3.5-9B: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 aneeq-hashmi/SalesforceCoder-Qwen3.5-9B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf aneeq-hashmi/SalesforceCoder-Qwen3.5-9B:Q4_K_M

Use Docker

docker model run hf.co/aneeq-hashmi/SalesforceCoder-Qwen3.5-9B:Q4_K_M

LM Studio
Jan
Ollama
How to use aneeq-hashmi/SalesforceCoder-Qwen3.5-9B with Ollama:
```
ollama run hf.co/aneeq-hashmi/SalesforceCoder-Qwen3.5-9B:Q4_K_M
```

Unsloth Studio

How to use aneeq-hashmi/SalesforceCoder-Qwen3.5-9B 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 aneeq-hashmi/SalesforceCoder-Qwen3.5-9B 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 aneeq-hashmi/SalesforceCoder-Qwen3.5-9B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for aneeq-hashmi/SalesforceCoder-Qwen3.5-9B to start chatting

How to use aneeq-hashmi/SalesforceCoder-Qwen3.5-9B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf aneeq-hashmi/SalesforceCoder-Qwen3.5-9B: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": "aneeq-hashmi/SalesforceCoder-Qwen3.5-9B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use aneeq-hashmi/SalesforceCoder-Qwen3.5-9B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf aneeq-hashmi/SalesforceCoder-Qwen3.5-9B: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 aneeq-hashmi/SalesforceCoder-Qwen3.5-9B:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use aneeq-hashmi/SalesforceCoder-Qwen3.5-9B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf aneeq-hashmi/SalesforceCoder-Qwen3.5-9B: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 "aneeq-hashmi/SalesforceCoder-Qwen3.5-9B: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"

Docker Model Runner
How to use aneeq-hashmi/SalesforceCoder-Qwen3.5-9B with Docker Model Runner:
```
docker model run hf.co/aneeq-hashmi/SalesforceCoder-Qwen3.5-9B:Q4_K_M
```

Lemonade

How to use aneeq-hashmi/SalesforceCoder-Qwen3.5-9B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull aneeq-hashmi/SalesforceCoder-Qwen3.5-9B:Q4_K_M

Run and chat with the model

lemonade run user.SalesforceCoder-Qwen3.5-9B-Q4_K_M

List all available models

lemonade list

SalesforceCoder-Qwen3.5-9B / README.md

aneeq-hashmi

Update README.md

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	---
	license: apache-2.0
	language:
	- en
	base_model:
	- Qwen/Qwen3.5-9B
	---
	# 🏛️ SalesforceCoder Qwen 3.5 (9B) - Structured Repository

	> [!IMPORTANT]
	> REPOSITORY RENAMED: This repository was formerly `SalesforceCoder-Qwen3.5-9B-Q4_K_M-GGUF`. All structured paths inside `files/` remain unchanged.

	> [!IMPORTANT]
	> PREFERRED MODEL: For most users, the Q4_K_M GGUF (located in `files/q4/`) is the recommended version. It maintains high architectural accuracy for Apex/SOQL while fitting comfortably within 6GB of VRAM, making it ideal for local development environments.

	## 📂 Repository Structure

	\| Path \| Description \|
	\| :--- \| :--- \|
	\| `files/q4/` \| Primary Quant (Q4_K_M). Optimized for VRAM < 6GB. \|
	\| `files/q5/` & `files/q8/` \| High-fidelity quants for 12GB+ VRAM environments. \|
	\| `files/mmproj/` \| Multimodal projector GGUF for vision-capable inference. \|
	\| `files/f16/` \| Full model weights (Safetensors) and BF16 adapters. \|
	\| `files/supporting/` \| Tokenizer, chat templates, and base JSON configs. \|

	## 🦙 Ollama / Local Inference Quick Run

	To use the preferred model with Ollama:

	1. Download the Q4 GGUF:

	You can manually download it from `files/q4/` or use this `curl` command:
	```
	powershell
	curl -L "[https://huggingface.co/aneeq-hashmi/SalesforceCoder-Qwen3.5-9B/resolve/main/files/q4/SalesforceCoder-Qwen3.5-9B.Q4_K_M.gguf](https://huggingface.co/aneeq-hashmi/SalesforceCoder-Qwen3.5-9B/resolve/main/files/q4/SalesforceCoder-Qwen3.5-9B.Q4_K_M.gguf)" -o SalesforceCoder-Qwen3.5-9B.Q4_K_M.gguf
	```
	2. Prepare the Modelfile:

	Ensure your Modelfile is in the same directory and the FROM line points to the file you just downloaded:

	```
	Dockerfile
	FROM ./SalesforceCoder-Qwen3.5-9B.Q4_K_M.gguf
	PARAMETER num_ctx 204800
	```
	3. Create & Run:

	```
	PowerShell
	ollama create SalesforceCoder -f Modelfile
	ollama run SalesforceCoder
	```
	---
	## 📖 Table of Contents
	- [🏛️ Model Overview](#-salesforcecoder-qwen-35-9b---structured-repository)
	- [📂 Repository Structure](#-repository-structure)
	- [🦙 Ollama Quick Start](#-ollama--local-inference-quick-run)
	- [📝 Model Information](#model-information)
	- [Technical Profile](#technical-profile)
	- [🛠️ System Prompt (Apex Rules)](#system-prompt-apex-rules)
	- [Code Quality & Security](#2-code-quality--security)
	- [Testing Requirements](#3-testing-requirements)
	- [⚙️ Inference & Runtime Config](#inference--runtime-config)
	- [🚀 Deployment Guides](#deployment-guides)
	- [llama.cpp](#-llamacpp)
	- [LM Studio](#-lm-studio)
	- [Ollama](#-ollama)
	- [🐋 Execution via Docker](#-execution-via-docker-the-no-install-way)
	- [🏛️ Architecture Note](#️-architecture-note)
	- [⚖️ Acknowledgments & Licensing](#️-acknowledgments--licensing)
	- [Dataset Attribution](#dataset-attribution)
	---
	# Model Information

	## Description
	SalesforceCoder-Qwen3.5-9B is a fine-tuned variant of Qwen3.5-9B, purpose-built for Salesforce Enterprise Architecture, Apex development, and troubleshooting.

	The model was trained on curated Salesforce Q&A, StackExchange, and GitHub data, specifically optimized for domain-specific practical problem coverage.

	It also incorporates [Gianloko's Apex Code dataset](https://huggingface.co/datasets/Gianloko/apex-coder-training-data), providing exposure to real-world Salesforce coding patterns.

	### Core Strengths
	- Compilable Apex: Generates triggers, handlers, and test classes following bulkification and governor-aware best practices.
	- Advanced Architecture: Optimized for multi-org strategy, Service Layer patterns, and secure integration designs.
	- Deep Debugging: Diagnoses recursion, SOQL-in-loops, and flaky tests with concrete, testable fixes.
	- Unit Testing: Prioritizes `seeAllData=false`, `Test.startTest()/Test.stopTest()`, and robust test data builders (aiming for 90%+ coverage).
	- Security First: Enforces CRUD/FLS checks, Named Credentials, and sanitizes dynamic SOQL.
	- 200k Context: Large window allows for ingesting entire multi-file repositories or massive debug logs for cross-file analysis.

	### Technical Profile
	- Memory Footprint: ~6 GB RAM (Q4 version).
	- Context Window: Up to 200,000 tokens.
	- Developer: Aneeq Hashmi
	- License: Apache-2.0
	---

	# System Prompt (Apex Rules)

	You are a Salesforce Enterprise Architect. Follow these strict rules on every response:

	### 1. Role & Tone
	- Act as a senior reviewer. Be concise, pragmatic, and solution-focused.
	- Prioritize "Truth over Reassurance" regarding governor limits.

	### 2. Code Quality & Security
	- Visibility: Default to `with sharing` for all classes.
	- Security: Enforce CRUD/FLS checks on all DML/SOQL operations.
	- Best Practices: Ensure bulkification, SOQL outside loops, and proper exception handling.
	- Sanitization: Use bind variables and `escapeSingleQuotes` for dynamic SOQL.

	### 3. Testing Requirements
	- Provide comprehensive test classes with ≥ 90% coverage.
	- Verify positive, negative, and bulk scenarios.
	- Use `Test.startTest()` and `Test.stopTest()` for all asynchronous logic.

	---

	# Inference & Runtime Config

	To ensure deterministic and syntactically correct Apex, use these parameters:

	\| Parameter \| Recommended Value \| Purpose \|
	\| :--- \| :--- \| :--- \|
	\| Temperature \| `0.0 – 0.2` \| Precise, deterministic code output. \|
	\| Top_P \| `0.9` \| Balance between variety and relevance. \|
	\| Repeat Penalty \| `1.1 – 1.2` \| Reduce boilerplate in long classes. \|
	\| Context Window \| Up to `200,000` \| Support for full-org analysis. \|

	---

	# Deployment Guides

	### 🦙 llama.cpp
	```bash
	./main -m files/q4/SalesforceCoder-Qwen3.5-9B.Q4_K_M.gguf -c 200000 --temp 0.0 --top_p 0.9 --repeat_penalty 1.1
	```
	### 💻 LM Studio
	1. Import: Move the model folder into your LM Studio models directory.
	2. Context: Under Hardware Settings, set the context limit to the maximum supported by your VRAM (up to 200k).
	3. Parameters: Set Temperature to `0.1` and Repeat Penalty to `1.1`.
	4. System Prompt: Paste the Enterprise Architect system prompt from the section above into the System Instruction box.

	### 🚀 Ollama
	Ensure you are in the root directory where the `Modelfile` is located. This command will build the model and reference the structured paths automatically:

	```bash
	ollama create SalesforceCoder -f Modelfile
	```
	# 🐋 Execution via Docker (The "No-Install" Way)

	Since you are running without a local Ollama installation, use this specific two-step command to build your model inside a container. Run this from your `C:\unsloth\source-repo` directory:

	### 1. Start the Container
	This maps your local folder (`${PWD}`) to the container's `/root/repo` path so it can access the weights.

	```powershell
	docker run -d -v ${PWD}:/root/repo -p 11434:11434 --name ollama-sf ollama/ollama
	```
	### 2. Create the Model
	This triggers the build process using the Modelfile and the 200k context configuration.

	```powerShell
	docker exec -it ollama-sf ollama create SalesforceCoder -f /root/repo/Modelfile
	```
	### 🏛️ Architecture Note
	The volume mapping (-v ${PWD}:/root/repo) is critical. It allows the container to resolve the FROM ./files/q4/... path defined in your Modelfile. Without this, the model creation will fail with a "file not found" error.

	# ⚖️ Acknowledgments & Licensing

	### Base Model
	This model is built upon Qwen 3.5 (9B) by the Qwen Team, licensed under Apache 2.0.

	### Dataset Attribution
	A significant portion of the fine-tuning for this model utilized the [Apex Coder Training Data](https://huggingface.co/datasets/Gianloko/apex-coder-training-data) created by Gianloko.

	* License: Apache 2.0
	* Usage: This dataset provided the foundational patterns for Apex trigger logic, bulkification, and Salesforce-specific unit testing. We are grateful to Gianloko for providing this high-quality open-source resource for the Salesforce developer community.

	### Repository License
	The modifications, fine-tuning configurations, and repository structure provided here are licensed under the Apache License 2.0.

	---
	Note: This model is an independent research project and is not affiliated with, sponsored by, or endorsed by Salesforce, Inc.