Text Generation
MLX
GGUF
Rust
4b
agentic-coding
alloy-backfilled
android
apple-silicon
attested
bash
c
chain-of-custody
chinese
code
code-completion
code-generation
code-infill
coder
coding
consumer-gpu
cpp
cryptographically-verified
css
delta-forge
derivative
edge-inference
embedded
english
forge-alloy
function-calling
ggml
go
html
iphone
java
javascript
kotlin
llama-cpp
lm-studio
local-inference
macbook
mobile
multilingual
ollama
on-device
php
programming
python
q4-k-m
q8-0
quantized
qwen
qwen3
qwen3.5
raspberry-pi
reproducible
ruby
software-engineering
sql
swift
typescript
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
- MLX
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
Ctrl+K