Instructions to use theprint/Tom-Qwen-7B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use theprint/Tom-Qwen-7B-Instruct with PEFT:
```
Task type is invalid.
```

How to use theprint/Tom-Qwen-7B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="theprint/Tom-Qwen-7B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("theprint/Tom-Qwen-7B-Instruct")
model = AutoModelForCausalLM.from_pretrained("theprint/Tom-Qwen-7B-Instruct")
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]:]))

llama-cpp-python

How to use theprint/Tom-Qwen-7B-Instruct with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="theprint/Tom-Qwen-7B-Instruct",
	filename="gguf/Tom-Qwen-7B-Instruct-f16.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use theprint/Tom-Qwen-7B-Instruct with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf theprint/Tom-Qwen-7B-Instruct:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf theprint/Tom-Qwen-7B-Instruct:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf theprint/Tom-Qwen-7B-Instruct:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf theprint/Tom-Qwen-7B-Instruct: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 theprint/Tom-Qwen-7B-Instruct:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf theprint/Tom-Qwen-7B-Instruct: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 theprint/Tom-Qwen-7B-Instruct:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf theprint/Tom-Qwen-7B-Instruct:Q4_K_M

Use Docker

docker model run hf.co/theprint/Tom-Qwen-7B-Instruct:Q4_K_M

LM Studio
Jan

vLLM

How to use theprint/Tom-Qwen-7B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "theprint/Tom-Qwen-7B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "theprint/Tom-Qwen-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/theprint/Tom-Qwen-7B-Instruct:Q4_K_M

SGLang

How to use theprint/Tom-Qwen-7B-Instruct 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 "theprint/Tom-Qwen-7B-Instruct" \
    --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": "theprint/Tom-Qwen-7B-Instruct",
		"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 "theprint/Tom-Qwen-7B-Instruct" \
        --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": "theprint/Tom-Qwen-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use theprint/Tom-Qwen-7B-Instruct with Ollama:
```
ollama run hf.co/theprint/Tom-Qwen-7B-Instruct:Q4_K_M
```

Unsloth Studio new

How to use theprint/Tom-Qwen-7B-Instruct 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 theprint/Tom-Qwen-7B-Instruct 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 theprint/Tom-Qwen-7B-Instruct to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for theprint/Tom-Qwen-7B-Instruct to start chatting

Pi new

How to use theprint/Tom-Qwen-7B-Instruct with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf theprint/Tom-Qwen-7B-Instruct: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": "theprint/Tom-Qwen-7B-Instruct:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use theprint/Tom-Qwen-7B-Instruct with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf theprint/Tom-Qwen-7B-Instruct: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 theprint/Tom-Qwen-7B-Instruct:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use theprint/Tom-Qwen-7B-Instruct with Docker Model Runner:
```
docker model run hf.co/theprint/Tom-Qwen-7B-Instruct:Q4_K_M
```

Lemonade

How to use theprint/Tom-Qwen-7B-Instruct with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull theprint/Tom-Qwen-7B-Instruct:Q4_K_M

Run and chat with the model

lemonade run user.Tom-Qwen-7B-Instruct-Q4_K_M

List all available models

lemonade list

Tom-Qwen-7B-Instruct

A fine-tuned 7B parameter model specialized for step-by-step instruction and conversation.

Model Details

This model is a fine-tuned version of Qwen/Qwen2.5-7B-Instruct using the Unsloth framework with LoRA (Low-Rank Adaptation) for efficient training.

Developed by: theprint
Model type: Causal Language Model (Fine-tuned with LoRA)
Language: en
License: apache-2.0
Base model: Qwen/Qwen2.5-7B-Instruct
Fine-tuning method: LoRA with rank 128

GGUF Quantized Versions

You can find quantized gguf versions of this model in the /gguf-folder.

Quantized GGUF versions are in the gguf/ directory for use with llama.cpp:

Tom-Qwen-7B-Instruct-f16.gguf (14531.9 MB) - 16-bit float (original precision, largest file)
Tom-Qwen-7B-Instruct-q3_k_m.gguf (3632.0 MB) - 3-bit quantization (medium quality)
Tom-Qwen-7B-Instruct-q4_k_m.gguf (4466.1 MB) - 4-bit quantization (medium, recommended for most use cases)
Tom-Qwen-7B-Instruct-q5_k_m.gguf (5192.6 MB) - 5-bit quantization (medium, good quality)
Tom-Qwen-7B-Instruct-q6_k.gguf (5964.5 MB) - 6-bit quantization (high quality)
Tom-Qwen-7B-Instruct-q8_0.gguf (7723.4 MB) - 8-bit quantization (very high quality)

Intended Use

Conversation, brainstorming, and general instruction following

Training Details

Training Data

Synthesized data set created specifically for this, focused on practical tips and well being.

Dataset: theprint/Tom-4.2k-alpaca
Format: alpaca

Training Procedure

Training epochs: 3
LoRA rank: 128
Learning rate: 0.0002
Batch size: 4
Framework: Unsloth + transformers + PEFT
Hardware: NVIDIA RTX 5090

Usage

from unsloth import FastLanguageModel
import torch

# Load model and tokenizer
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="theprint/Tom-Qwen-7B-Instruct",
    max_seq_length=4096,
    dtype=None,
    load_in_4bit=True,
)

# Enable inference mode
FastLanguageModel.for_inference(model)

# Example usage
inputs = tokenizer(["Your prompt here"], return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Alternative Usage (Standard Transformers)

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "theprint/Tom-Qwen-7B-Instruct",
    torch_dtype=torch.float16,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("theprint/Tom-Qwen-7B-Instruct")

# Example usage
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Your question here"}
]

inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
outputs = model.generate(inputs, max_new_tokens=256, temperature=0.7, do_sample=True)
response = tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True)
print(response)

Using with llama.cpp

# Download a quantized version (q4_k_m recommended for most use cases)
wget https://huggingface.co/theprint/Tom-Qwen-7B-Instruct/resolve/main/gguf/Tom-Qwen-7B-Instruct-q4_k_m.gguf

# Run with llama.cpp
./llama.cpp/main -m Tom-Qwen-7B-Instruct-q4_k_m.gguf -p "Your prompt here" -n 256

Limitations

May hallucinate or provide incorrect information. Not suitable for critical decision making.

Citation

If you use this model, please cite:

@misc{tom_qwen_7b_instruct,
  title={Tom-Qwen-7B-Instruct: Fine-tuned Qwen/Qwen2.5-7B-Instruct},
  author={theprint},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/theprint/Tom-Qwen-7B-Instruct}
}

Acknowledgments

Base model: Qwen/Qwen2.5-7B-Instruct
Training dataset: theprint/Tom-4.2k-alpaca
Fine-tuning framework: Unsloth
Quantization: llama.cpp

Downloads last month: 5

Safetensors

Model size

8B params

Tensor type

F16

Model tree for theprint/Tom-Qwen-7B-Instruct

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Adapter

(2082)

this model

Adapters

3 models

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Tom-Qwen-7B-Instruct