Instructions to use continuum-ai/qwen3.5-4b-code-forged-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use continuum-ai/qwen3.5-4b-code-forged-GGUF with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm
# if on a CUDA device, also pip install mlx[cuda]

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("continuum-ai/qwen3.5-4b-code-forged-GGUF")

prompt = "Once upon a time in"
text = generate(model, tokenizer, prompt=prompt, verbose=True)

llama-cpp-python

How to use continuum-ai/qwen3.5-4b-code-forged-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="continuum-ai/qwen3.5-4b-code-forged-GGUF",
	filename="qwen3.5-4b-code-forged-Q4_K_M.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use continuum-ai/qwen3.5-4b-code-forged-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf continuum-ai/qwen3.5-4b-code-forged-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf continuum-ai/qwen3.5-4b-code-forged-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf continuum-ai/qwen3.5-4b-code-forged-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf continuum-ai/qwen3.5-4b-code-forged-GGUF: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 continuum-ai/qwen3.5-4b-code-forged-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf continuum-ai/qwen3.5-4b-code-forged-GGUF: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 continuum-ai/qwen3.5-4b-code-forged-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf continuum-ai/qwen3.5-4b-code-forged-GGUF:Q4_K_M

Use Docker

docker model run hf.co/continuum-ai/qwen3.5-4b-code-forged-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use continuum-ai/qwen3.5-4b-code-forged-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "continuum-ai/qwen3.5-4b-code-forged-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "continuum-ai/qwen3.5-4b-code-forged-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/continuum-ai/qwen3.5-4b-code-forged-GGUF:Q4_K_M

Ollama
How to use continuum-ai/qwen3.5-4b-code-forged-GGUF with Ollama:
```
ollama run hf.co/continuum-ai/qwen3.5-4b-code-forged-GGUF:Q4_K_M
```

Unsloth Studio new

How to use continuum-ai/qwen3.5-4b-code-forged-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 continuum-ai/qwen3.5-4b-code-forged-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 continuum-ai/qwen3.5-4b-code-forged-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for continuum-ai/qwen3.5-4b-code-forged-GGUF to start chatting

MLX LM

How to use continuum-ai/qwen3.5-4b-code-forged-GGUF with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Generate some text
mlx_lm.generate --model "continuum-ai/qwen3.5-4b-code-forged-GGUF" --prompt "Once upon a time"

Docker Model Runner
How to use continuum-ai/qwen3.5-4b-code-forged-GGUF with Docker Model Runner:
```
docker model run hf.co/continuum-ai/qwen3.5-4b-code-forged-GGUF:Q4_K_M
```

Lemonade

How to use continuum-ai/qwen3.5-4b-code-forged-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull continuum-ai/qwen3.5-4b-code-forged-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.qwen3.5-4b-code-forged-GGUF-Q4_K_M

List all available models

lemonade list

+22.7% Better at Code

Qwen3.5-4B forged for code through Experiential Plasticity.

3.04 → 2.35 perplexity · 3 cycles

Every claim on this card is verified
Trust: self-attested · 2 benchmarks · 1 device tested
ForgeAlloy chain of custody · Download alloy · Merkle-chained

Qwen3.5-4B with cryptographic provenance via the ForgeAlloy chain of custody.

Benchmarks

Benchmark	Result	Verified
perplexity	22.7	Self-reported
humaneval	pending	Self-reported

What Changed (Base → Forged)

	Base	Forged	Delta
Perplexity (code)	3.04	2.35	-22.7% ✅
Training	General	code, 1000 steps	LR 2e-4, 3 cycles
Pipeline		train → quant → eval → quant	3 cycles

Runs On

Device	Format	Size	Speed
NVIDIA GeForce RTX 5090	fp16	—	Verified
MacBook Pro 32GB	fp16	8.0GB	Expected
MacBook Air 16GB	Q8_0	~4.0GB	Expected
MacBook Air 8GB	Q4_K_M	~2.5GB	Expected
iPhone / Android	Q4_K_M	~2.5GB	Expected

Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("continuum-ai/qwen3.5-4b-code-forged-GGUF",
    torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("continuum-ai/qwen3.5-4b-code-forged-GGUF")

inputs = tokenizer("def merge_sort(arr):", return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Methodology

Produced via GGUF quantization. Full methodology, ablations, and per-stage rationale are in the methodology paper and the companion MODEL_METHODOLOGY.md in this repository. The pipeline ran as train → quant → eval → quant over 3 cycles on NVIDIA GeForce RTX 5090.

Chain of Custody

Scan the QR or verify online. Download the alloy file to verify independently.

What	Proof
Model weights	`sha256:03dd512b17b85b9b4ee6614bc6dd46c08...`
Code that ran	`sha256:derivation-tool-o...`
Forged on	NVIDIA GeForce RTX 5090, 2026-04-08
Trust level	`self-attested`
Spec	ForgeAlloy — Rust/Python/TypeScript

Make Your Own

Forged with Continuum — a distributed AI world that runs on your hardware.

The Factory configurator lets you design and forge custom models visually — context extension, pruning, LoRA, quantization, vision/audio modalities. Pick your target devices, the system figures out what fits.

GitHub · All Models · Forge-Alloy