Instructions to use unclemusclez/Unsloth-Qwen2.5-Coder-1.5B-Devinator-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use unclemusclez/Unsloth-Qwen2.5-Coder-1.5B-Devinator-v1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="unclemusclez/Unsloth-Qwen2.5-Coder-1.5B-Devinator-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("unclemusclez/Unsloth-Qwen2.5-Coder-1.5B-Devinator-v1")
model = AutoModelForMultimodalLM.from_pretrained("unclemusclez/Unsloth-Qwen2.5-Coder-1.5B-Devinator-v1")
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]:]))

PEFT
How to use unclemusclez/Unsloth-Qwen2.5-Coder-1.5B-Devinator-v1 with PEFT:
```
Task type is invalid.
```

llama-cpp-python

How to use unclemusclez/Unsloth-Qwen2.5-Coder-1.5B-Devinator-v1 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="unclemusclez/Unsloth-Qwen2.5-Coder-1.5B-Devinator-v1",
	filename="Unsloth-Qwen2.5-Coder-1.5B-Instruct-Devinator.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use unclemusclez/Unsloth-Qwen2.5-Coder-1.5B-Devinator-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 unclemusclez/Unsloth-Qwen2.5-Coder-1.5B-Devinator-v1
# Run inference directly in the terminal:
llama cli -hf unclemusclez/Unsloth-Qwen2.5-Coder-1.5B-Devinator-v1

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf unclemusclez/Unsloth-Qwen2.5-Coder-1.5B-Devinator-v1
# Run inference directly in the terminal:
llama cli -hf unclemusclez/Unsloth-Qwen2.5-Coder-1.5B-Devinator-v1

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 unclemusclez/Unsloth-Qwen2.5-Coder-1.5B-Devinator-v1
# Run inference directly in the terminal:
./llama-cli -hf unclemusclez/Unsloth-Qwen2.5-Coder-1.5B-Devinator-v1

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 unclemusclez/Unsloth-Qwen2.5-Coder-1.5B-Devinator-v1
# Run inference directly in the terminal:
./build/bin/llama-cli -hf unclemusclez/Unsloth-Qwen2.5-Coder-1.5B-Devinator-v1

Use Docker

docker model run hf.co/unclemusclez/Unsloth-Qwen2.5-Coder-1.5B-Devinator-v1

LM Studio
Jan

vLLM

How to use unclemusclez/Unsloth-Qwen2.5-Coder-1.5B-Devinator-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "unclemusclez/Unsloth-Qwen2.5-Coder-1.5B-Devinator-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": "unclemusclez/Unsloth-Qwen2.5-Coder-1.5B-Devinator-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/unclemusclez/Unsloth-Qwen2.5-Coder-1.5B-Devinator-v1

SGLang

How to use unclemusclez/Unsloth-Qwen2.5-Coder-1.5B-Devinator-v1 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 "unclemusclez/Unsloth-Qwen2.5-Coder-1.5B-Devinator-v1" \
    --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": "unclemusclez/Unsloth-Qwen2.5-Coder-1.5B-Devinator-v1",
		"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 "unclemusclez/Unsloth-Qwen2.5-Coder-1.5B-Devinator-v1" \
        --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": "unclemusclez/Unsloth-Qwen2.5-Coder-1.5B-Devinator-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use unclemusclez/Unsloth-Qwen2.5-Coder-1.5B-Devinator-v1 with Ollama:
```
ollama run hf.co/unclemusclez/Unsloth-Qwen2.5-Coder-1.5B-Devinator-v1
```

Unsloth Studio

How to use unclemusclez/Unsloth-Qwen2.5-Coder-1.5B-Devinator-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 unclemusclez/Unsloth-Qwen2.5-Coder-1.5B-Devinator-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 unclemusclez/Unsloth-Qwen2.5-Coder-1.5B-Devinator-v1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for unclemusclez/Unsloth-Qwen2.5-Coder-1.5B-Devinator-v1 to start chatting

How to use unclemusclez/Unsloth-Qwen2.5-Coder-1.5B-Devinator-v1 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf unclemusclez/Unsloth-Qwen2.5-Coder-1.5B-Devinator-v1

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": "unclemusclez/Unsloth-Qwen2.5-Coder-1.5B-Devinator-v1"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use unclemusclez/Unsloth-Qwen2.5-Coder-1.5B-Devinator-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 unclemusclez/Unsloth-Qwen2.5-Coder-1.5B-Devinator-v1

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 unclemusclez/Unsloth-Qwen2.5-Coder-1.5B-Devinator-v1

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use unclemusclez/Unsloth-Qwen2.5-Coder-1.5B-Devinator-v1 with Docker Model Runner:
```
docker model run hf.co/unclemusclez/Unsloth-Qwen2.5-Coder-1.5B-Devinator-v1
```

Lemonade

How to use unclemusclez/Unsloth-Qwen2.5-Coder-1.5B-Devinator-v1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull unclemusclez/Unsloth-Qwen2.5-Coder-1.5B-Devinator-v1

Run and chat with the model

lemonade run user.Unsloth-Qwen2.5-Coder-1.5B-Devinator-v1-{{QUANT_TAG}}

List all available models

lemonade list

Unsloth-Qwen2.5-Coder-1.5B-Devinator-v1

File size: 1,390 Bytes

dad7b92

{
    "model": "unsloth/Qwen2.5-Coder-1.5B-Instruct",
    "project_name": "unsloth-Qwen2-5-Coder-1-5B-Instruct-Devin-nsa-1",
    "data_path": "skratos115/opendevin_DataDevinator",
    "train_split": "train",
    "valid_split": null,
    "add_eos_token": true,
    "block_size": 1024,
    "model_max_length": 8192,
    "padding": "right",
    "trainer": "sft",
    "use_flash_attention_2": false,
    "log": "tensorboard",
    "disable_gradient_checkpointing": false,
    "logging_steps": -1,
    "eval_strategy": "epoch",
    "save_total_limit": 1,
    "auto_find_batch_size": false,
    "mixed_precision": "bf16",
    "lr": 1e-6,
    "epochs": 3,
    "batch_size": 5,
    "warmup_ratio": 0.1,
    "gradient_accumulation": 256,
    "optimizer": "adamw_torch",
    "scheduler": "linear",
    "weight_decay": 0.0,
    "max_grad_norm": 1.0,
    "seed": 69420,
    "chat_template": "none",
    "quantization": null,
    "target_modules": "all-linear",
    "merge_adapter": true,
    "peft": true,
    "lora_r": 16,
    "lora_alpha": 32,
    "lora_dropout": 0.05,
    "model_ref": null,
    "dpo_beta": 0.1,
    "max_prompt_length": 8192,
    "max_completion_length": null,
    "prompt_text_column": null,
    "text_column": "solution",
    "rejected_text_column": null,
    "push_to_hub": false,
    "username": null,
    "token": null,
    "unsloth": false,
    "distributed_backend": "none"
}