Instructions to use prithivMLmods/Blaze.1-32B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Blaze.1-32B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="prithivMLmods/Blaze.1-32B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/Blaze.1-32B-Instruct")
model = AutoModelForCausalLM.from_pretrained("prithivMLmods/Blaze.1-32B-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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use prithivMLmods/Blaze.1-32B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Blaze.1-32B-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": "prithivMLmods/Blaze.1-32B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/Blaze.1-32B-Instruct

SGLang

How to use prithivMLmods/Blaze.1-32B-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 "prithivMLmods/Blaze.1-32B-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": "prithivMLmods/Blaze.1-32B-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 "prithivMLmods/Blaze.1-32B-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": "prithivMLmods/Blaze.1-32B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use prithivMLmods/Blaze.1-32B-Instruct with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Blaze.1-32B-Instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Blaze.1-32B-Instruct

Blaze.1-32B-Instruct is based on the QwQ-32B-Preview model, fine-tuned using synthetic data for mathematical reasoning and conditional reasoning to handle complex reasoning problems. The model may unexpectedly mix languages or switch between them, affecting response clarity. Additionally, it may enter recursive reasoning loops, resulting in lengthy responses without a conclusive answer, as it focuses on maintaining a continuous chain of thought reasoning.

Quickstart Chat Template

Here provides a code snippet with apply_chat_template to show you how to load the tokenizer and model and how to generate contents.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Blaze.1-32B-Instruct"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

Intended Use

Blaze.1-32B-Instruct is designed to assist with complex reasoning tasks, including mathematical problem-solving, logical reasoning, and step-by-step explanations. It is particularly useful for applications requiring conditional reasoning, structured content generation, and language understanding across multiple domains. The model is also fine-tuned for conversational AI, making it well-suited for virtual assistants, educational tools, and research purposes. Additionally, it supports tasks involving multilingual understanding, making it valuable in environments where language switching or code-mixed text processing is required.

Limitations

Language Mixing and Code-Switching Issues: The model may unexpectedly switch between languages or mix them within a single response, potentially reducing the clarity of outputs.
Recursive Reasoning Loops: During complex reasoning, the model may enter circular reasoning patterns, resulting in overly lengthy responses without arriving at a definitive conclusion.
Overfitting to Training Data: Since Blaze.1-32B-Instruct is fine-tuned on specific synthetic datasets, its performance might be biased toward certain types of problems and may generalize poorly on entirely new tasks.
Context Sensitivity: While the model is trained for step-by-step reasoning, it may occasionally lose track of the context in longer conversations, leading to irrelevant or incomplete answers.
Resource Intensity: As a large model (32B parameters), it requires significant computational resources for both inference and deployment, which may limit its usability in low-resource environments.