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", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

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
```

prithivMLmods commited on Jan 15, 2025

Commit

8ac3918

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1 Parent(s): 272f56f

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@@ -53,3 +53,12 @@ generated_ids = [
 response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
 ```

 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**
+1. **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.
+2. **Recursive Reasoning Loops**: During complex reasoning, the model may enter circular reasoning patterns, resulting in overly lengthy responses without arriving at a definitive conclusion.
+3. **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.
+4. **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.
+5. **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.