Instructions to use brucoder/winter-frost-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use brucoder/winter-frost-2 with Transformers:

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

pipe = pipeline("text-generation", model="brucoder/winter-frost-2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("brucoder/winter-frost-2")
model = AutoModelForCausalLM.from_pretrained("brucoder/winter-frost-2", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use brucoder/winter-frost-2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "brucoder/winter-frost-2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "brucoder/winter-frost-2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/brucoder/winter-frost-2

SGLang

How to use brucoder/winter-frost-2 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 "brucoder/winter-frost-2" \
    --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": "brucoder/winter-frost-2",
		"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 "brucoder/winter-frost-2" \
        --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": "brucoder/winter-frost-2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use brucoder/winter-frost-2 with Docker Model Runner:
```
docker model run hf.co/brucoder/winter-frost-2
```

winter-frost-2 / README.md

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---
base_model: Qwen/Qwen2.5-7B-Instruct
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- qlora
- lora
- fine-tuned
- qwen2.5
---
# winter-frost-2

A fine-tune of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct), created by INEZA AIME BRUNO (brucoder). This is a standalone, merged checkpoint — the LoRA adapter has already been folded into the base model's weights, so it loads and runs like any other model, no adapter-loading step required.

## Quickstart

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
tokenizer = AutoTokenizer.from_pretrained("brucoder/winter-frost-2")
model = AutoModelForCausalLM.from_pretrained(
    "brucoder/winter-frost-2",
    torch_dtype=torch.float16,
    device_map="auto",
)
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Who made you?"},
]
inputs = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True,
    return_tensors="pt", return_dict=True
).to(model.device)
output = model.generate(**inputs, max_new_tokens=200, do_sample=True, temperature=0.7, top_p=0.9, pad_token_id=tokenizer.eos_token_id)
print(tokenizer.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
```

## Run it

### Google Colab (free GPU)

Paste into one cell:

```python
!pip install -q -U transformers accelerate bitsandbytes
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import torch
bnb_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4",
                                 bnb_4bit_compute_dtype=torch.float16, bnb_4bit_use_double_quant=True)
tokenizer = AutoTokenizer.from_pretrained("brucoder/winter-frost-2")
model = AutoModelForCausalLM.from_pretrained("brucoder/winter-frost-2",
                                              quantization_config=bnb_config, device_map="auto")
conversation = [{"role": "system", "content": "You are a helpful assistant."}]
print("Model loaded. Chat with winter-frost-2 (type 'quit' to stop).\n")
while True:
    user_input = input("You: ")
    if user_input.strip().lower() in ("quit", "exit"):
        break
    conversation.append({"role": "user", "content": user_input})
    inputs = tokenizer.apply_chat_template(conversation, tokenize=True, add_generation_prompt=True,
                                            return_tensors="pt", return_dict=True).to(model.device)
    output = model.generate(**inputs, max_new_tokens=300, do_sample=True, temperature=0.7,
                             top_p=0.9, pad_token_id=tokenizer.eos_token_id)
    response = tokenizer.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
    print(f"winter-frost-2: {response}\n")
    conversation.append({"role": "assistant", "content": response})
```

### Local machine

Requires Python 3.9+. A GPU with 16GB+ VRAM keeps this fast; it will also run on CPU, just slowly. First run downloads ~15GB of weights (cached after that):

```bash
pip install -q -U transformers accelerate torch
python3 -c "
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
tokenizer = AutoTokenizer.from_pretrained('brucoder/winter-frost-2')
model = AutoModelForCausalLM.from_pretrained('brucoder/winter-frost-2', torch_dtype=torch.float16, device_map='auto')
conversation = [{'role': 'system', 'content': 'You are a helpful assistant.'}]
print('Model loaded. Chat with winter-frost-2 (type quit to stop).')
while True:
    user_input = input('You: ')
    if user_input.strip().lower() in ('quit', 'exit'):
        break
    conversation.append({'role': 'user', 'content': user_input})
    inputs = tokenizer.apply_chat_template(conversation, tokenize=True, add_generation_prompt=True, return_tensors='pt', return_dict=True).to(model.device)
    output = model.generate(**inputs, max_new_tokens=300, do_sample=True, temperature=0.7, top_p=0.9, pad_token_id=tokenizer.eos_token_id)
    response = tokenizer.decode(output[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
    print('winter-frost-2:', response)
    conversation.append({'role': 'assistant', 'content': response})
"
```

## What this is

- **Base model:** Qwen/Qwen2.5-7B-Instruct
- **Fine-tuning method:** QLoRA (4-bit NF4 quantized base, fp16 compute, LoRA r=16, alpha=32) on a single T4 GPU
- **Target modules:** q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
- **Training data:** ~690 examples — 300 general instructions from [HuggingFaceH4/no_robots](https://huggingface.co/datasets/HuggingFaceH4/no_robots), 300 Python coding examples from [iamtarun/python_code_instructions_18k_alpaca](https://huggingface.co/datasets/iamtarun/python_code_instructions_18k_alpaca), and 90 custom identity examples
- **Training length:** 3 epochs, 132 steps, ~45 minutes
## What this is not
Not a new architecture and not trained from scratch. With ~690 training examples, this reliably shifts small, well-defined behaviors (like stating who created it) but should not be expected to meaningfully change general reasoning, coding ability, or knowledge compared to the base model.
## Adapter-only version
If you'd rather load the LoRA adapter separately on top of the base model instead of this merged version, it's available at [brucoder/winter-frost-2-adapter](https://huggingface.co/brucoder/winter-frost-2-adapter).
## Creator
INEZA AIME BRUNO (brucoder)
Instagram: [iabru.ceo](https://instagram.com/iabru.ceo)

	---
	base_model: Qwen/Qwen2.5-7B-Instruct
	language:
	- en
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- qlora
	- lora
	- fine-tuned
	- qwen2.5
	---

	# winter-frost-2

	A fine-tune of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct), created by INEZA AIME BRUNO (brucoder). This is a standalone, merged checkpoint — the LoRA adapter has already been folded into the base model's weights, so it loads and runs like any other model, no adapter-loading step required.

	## Quickstart

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer
	import torch

	tokenizer = AutoTokenizer.from_pretrained("brucoder/winter-frost-2")
	model = AutoModelForCausalLM.from_pretrained(
	"brucoder/winter-frost-2",
	torch_dtype=torch.float16,
	device_map="auto",
	)

	messages = [
	{"role": "system", "content": "You are a helpful assistant."},
	{"role": "user", "content": "Who made you?"},
	]
	inputs = tokenizer.apply_chat_template(
	messages, tokenize=True, add_generation_prompt=True,
	return_tensors="pt", return_dict=True
	).to(model.device)

	output = model.generate(**inputs, max_new_tokens=200, do_sample=True, temperature=0.7, top_p=0.9, pad_token_id=tokenizer.eos_token_id)
	print(tokenizer.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
	```

	## Run it

	### Google Colab (free GPU)

	Paste into one cell:

	```python
	!pip install -q -U transformers accelerate bitsandbytes

	from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
	import torch

	bnb_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4",
	bnb_4bit_compute_dtype=torch.float16, bnb_4bit_use_double_quant=True)

	tokenizer = AutoTokenizer.from_pretrained("brucoder/winter-frost-2")
	model = AutoModelForCausalLM.from_pretrained("brucoder/winter-frost-2",
	quantization_config=bnb_config, device_map="auto")

	conversation = [{"role": "system", "content": "You are a helpful assistant."}]
	print("Model loaded. Chat with winter-frost-2 (type 'quit' to stop).\n")

	while True:
	user_input = input("You: ")
	if user_input.strip().lower() in ("quit", "exit"):
	break
	conversation.append({"role": "user", "content": user_input})
	inputs = tokenizer.apply_chat_template(conversation, tokenize=True, add_generation_prompt=True,
	return_tensors="pt", return_dict=True).to(model.device)
	output = model.generate(**inputs, max_new_tokens=300, do_sample=True, temperature=0.7,
	top_p=0.9, pad_token_id=tokenizer.eos_token_id)
	response = tokenizer.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
	print(f"winter-frost-2: {response}\n")
	conversation.append({"role": "assistant", "content": response})
	```

	### Local machine

	Requires Python 3.9+. A GPU with 16GB+ VRAM keeps this fast; it will also run on CPU, just slowly. First run downloads ~15GB of weights (cached after that):

	```bash
	pip install -q -U transformers accelerate torch
	python3 -c "
	from transformers import AutoModelForCausalLM, AutoTokenizer
	import torch

	tokenizer = AutoTokenizer.from_pretrained('brucoder/winter-frost-2')
	model = AutoModelForCausalLM.from_pretrained('brucoder/winter-frost-2', torch_dtype=torch.float16, device_map='auto')

	conversation = [{'role': 'system', 'content': 'You are a helpful assistant.'}]
	print('Model loaded. Chat with winter-frost-2 (type quit to stop).')

	while True:
	user_input = input('You: ')
	if user_input.strip().lower() in ('quit', 'exit'):
	break
	conversation.append({'role': 'user', 'content': user_input})
	inputs = tokenizer.apply_chat_template(conversation, tokenize=True, add_generation_prompt=True, return_tensors='pt', return_dict=True).to(model.device)
	output = model.generate(**inputs, max_new_tokens=300, do_sample=True, temperature=0.7, top_p=0.9, pad_token_id=tokenizer.eos_token_id)
	response = tokenizer.decode(output[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
	print('winter-frost-2:', response)
	conversation.append({'role': 'assistant', 'content': response})
	"
	```

	## What this is

	- Base model: Qwen/Qwen2.5-7B-Instruct
	- Fine-tuning method: QLoRA (4-bit NF4 quantized base, fp16 compute, LoRA r=16, alpha=32) on a single T4 GPU
	- Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
	- Training data: ~690 examples — 300 general instructions from [HuggingFaceH4/no_robots](https://huggingface.co/datasets/HuggingFaceH4/no_robots), 300 Python coding examples from [iamtarun/python_code_instructions_18k_alpaca](https://huggingface.co/datasets/iamtarun/python_code_instructions_18k_alpaca), and 90 custom identity examples
	- Training length: 3 epochs, 132 steps, ~45 minutes

	## What this is not

	Not a new architecture and not trained from scratch. With ~690 training examples, this reliably shifts small, well-defined behaviors (like stating who created it) but should not be expected to meaningfully change general reasoning, coding ability, or knowledge compared to the base model.

	## Adapter-only version

	If you'd rather load the LoRA adapter separately on top of the base model instead of this merged version, it's available at [brucoder/winter-frost-2-adapter](https://huggingface.co/brucoder/winter-frost-2-adapter).

	## Creator

	INEZA AIME BRUNO (brucoder)
	Instagram: [iabru.ceo](https://instagram.com/iabru.ceo)