Instructions to use prithivMLmods/Viper-Coder-HybridMini-v1.3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Viper-Coder-HybridMini-v1.3 with Transformers:

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

pipe = pipeline("text-generation", model="prithivMLmods/Viper-Coder-HybridMini-v1.3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/Viper-Coder-HybridMini-v1.3")
model = AutoModelForCausalLM.from_pretrained("prithivMLmods/Viper-Coder-HybridMini-v1.3")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use prithivMLmods/Viper-Coder-HybridMini-v1.3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Viper-Coder-HybridMini-v1.3"
# 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/Viper-Coder-HybridMini-v1.3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/Viper-Coder-HybridMini-v1.3

SGLang

How to use prithivMLmods/Viper-Coder-HybridMini-v1.3 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/Viper-Coder-HybridMini-v1.3" \
    --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/Viper-Coder-HybridMini-v1.3",
		"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/Viper-Coder-HybridMini-v1.3" \
        --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/Viper-Coder-HybridMini-v1.3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use prithivMLmods/Viper-Coder-HybridMini-v1.3 with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Viper-Coder-HybridMini-v1.3
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Viper-Coder-HybridMini-v1.3

Viper-Coder-HybridMini-v1.3 is based on the Qwen 2.5 7B modality architecture, designed to be the best for coding and reasoning tasks. It has been fine-tuned on a synthetic dataset leveraging the latest coding logits and CoT datasets, further optimizing its chain-of-thought (CoT) reasoning and logical problem-solving abilities. The model demonstrates significant improvements in context understanding, structured data processing, and long-context comprehension, making it ideal for complex coding tasks, instruction-following, and text generation.

Key Improvements

Best-in-Class Coding Proficiency: Enhanced understanding of programming languages, debugging, and code generation.
Fine-Tuned Instruction Following: Optimized for precise responses, structured outputs (e.g., JSON, YAML), and extended text generation (8K+ tokens).
Advanced Logical & Mathematical Reasoning: Improved multi-step problem-solving and theorem proving.
Long-Context Mastery: Handles up to 128K tokens with an output capability of 8K tokens per response.
Multilingual Code Support: Excels in Python, JavaScript, C++, Java, SQL, and other major programming languages, with documentation in 29+ languages.

Quickstart with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Viper-Coder-HybridMini-v1.3"

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

prompt = "Write a Python function to merge two sorted lists."
messages = [
    {"role": "system", "content": "You are an advanced AI assistant with expert-level coding and reasoning abilities."},
    {"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]
print(response)

Intended Use

Elite Coding & Debugging: Best-in-class model for writing, analyzing, and optimizing code.
Complex Algorithmic Reasoning: Solves intricate logic problems and algorithm-based challenges.
Scientific & Mathematical Computation: Advanced support for formulas, equations, and theorem verification.
Structured Data Processing: Seamlessly handles JSON, XML, SQL, and data pipeline automation.
Multilingual Programming Support: Proficient in Python, JavaScript, C++, Java, Go, and more.
Extended Technical Content Generation: Ideal for writing documentation, research papers, and technical blogs.

Limitations

Moderate Computational Demand: Requires GPUs/TPUs for smooth inference due to 7B parameters, but more lightweight than larger models.
Language-Specific Variability: Performance may vary across different programming languages.
Possible Error Propagation: Extended text outputs might introduce logical inconsistencies.
Limited Real-World Awareness: The model does not have access to real-time internet updates.
Prompt Sensitivity: Performance depends on how well the prompt is structured.