Text Generation
Transformers
Safetensors
MLX
llama
conversational
custom_code
text-generation-inference
4-bit precision
Instructions to use hunterbown/Stable-DiffCoder-8B-Instruct-mlx-4Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hunterbown/Stable-DiffCoder-8B-Instruct-mlx-4Bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="hunterbown/Stable-DiffCoder-8B-Instruct-mlx-4Bit", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("hunterbown/Stable-DiffCoder-8B-Instruct-mlx-4Bit", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("hunterbown/Stable-DiffCoder-8B-Instruct-mlx-4Bit", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - MLX
How to use hunterbown/Stable-DiffCoder-8B-Instruct-mlx-4Bit with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("hunterbown/Stable-DiffCoder-8B-Instruct-mlx-4Bit") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
- vLLM
How to use hunterbown/Stable-DiffCoder-8B-Instruct-mlx-4Bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "hunterbown/Stable-DiffCoder-8B-Instruct-mlx-4Bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "hunterbown/Stable-DiffCoder-8B-Instruct-mlx-4Bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/hunterbown/Stable-DiffCoder-8B-Instruct-mlx-4Bit
- SGLang
How to use hunterbown/Stable-DiffCoder-8B-Instruct-mlx-4Bit with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "hunterbown/Stable-DiffCoder-8B-Instruct-mlx-4Bit" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "hunterbown/Stable-DiffCoder-8B-Instruct-mlx-4Bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "hunterbown/Stable-DiffCoder-8B-Instruct-mlx-4Bit" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "hunterbown/Stable-DiffCoder-8B-Instruct-mlx-4Bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - MLX LM
How to use hunterbown/Stable-DiffCoder-8B-Instruct-mlx-4Bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "hunterbown/Stable-DiffCoder-8B-Instruct-mlx-4Bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "hunterbown/Stable-DiffCoder-8B-Instruct-mlx-4Bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "hunterbown/Stable-DiffCoder-8B-Instruct-mlx-4Bit", "messages": [ {"role": "user", "content": "Hello"} ] }' - Docker Model Runner
How to use hunterbown/Stable-DiffCoder-8B-Instruct-mlx-4Bit with Docker Model Runner:
docker model run hf.co/hunterbown/Stable-DiffCoder-8B-Instruct-mlx-4Bit
Upload chat_template.jinja with huggingface_hub
Browse files- chat_template.jinja +6 -0
chat_template.jinja
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{% set has_system = messages[0]['role'] == 'system' %}{% if not has_system %}{{ bos_token + 'system
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You are an AI programming assistant, utilizing the Seed-Coder model, developed by ByteDance Seed, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer.
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' + eos_token }}{% endif %}{% for message in messages %}{{ bos_token + message['role'] + '
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' + message['content'] | trim + eos_token }}{% endfor %}{% if add_generation_prompt %}{{ bos_token + 'assistant
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'}}{% endif %}
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