Instructions to use TevunahAi/NextCoder-32B-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TevunahAi/NextCoder-32B-FP8 with Transformers:

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

pipe = pipeline("text-generation", model="TevunahAi/NextCoder-32B-FP8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TevunahAi/NextCoder-32B-FP8")
model = AutoModelForCausalLM.from_pretrained("TevunahAi/NextCoder-32B-FP8", 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 TevunahAi/NextCoder-32B-FP8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TevunahAi/NextCoder-32B-FP8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TevunahAi/NextCoder-32B-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TevunahAi/NextCoder-32B-FP8

SGLang

How to use TevunahAi/NextCoder-32B-FP8 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 "TevunahAi/NextCoder-32B-FP8" \
    --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": "TevunahAi/NextCoder-32B-FP8",
		"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 "TevunahAi/NextCoder-32B-FP8" \
        --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": "TevunahAi/NextCoder-32B-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TevunahAi/NextCoder-32B-FP8 with Docker Model Runner:
```
docker model run hf.co/TevunahAi/NextCoder-32B-FP8
```

rockylynnstein commited on Dec 8, 2025

Commit

5c2eb57

verified ·

1 Parent(s): b0dcd94

Update README.md

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@@ -139,7 +139,6 @@ pip install torch>=2.1.0 transformers>=4.40.0 accelerate compressed-tensors
 | **Base Model** | [microsoft/NextCoder-32B](https://huggingface.co/microsoft/NextCoder-32B) |
 | **Quantization Method** | FP8 E4M3 weight-only |
 | **Framework** | llm-compressor + compressed_tensors |
-| **Calibration Samples** | 2048 (8x industry standard) |
 | **Storage Size** | ~32GB (sharded safetensors) |
 | **VRAM (vLLM)** | ~32GB |
 | **VRAM (Transformers)** | ~64GB+ (decompressed to BF16) |
@@ -192,12 +191,6 @@ The 32B model represents the flagship tier:
 - ✅ **Enterprise-grade completions** for mission-critical applications
 - ✅ **Best context understanding** across the model family
-## 🔬 Quality Assurance
-- **High-quality calibration:** 2048 diverse code samples (8x industry standard of 256)
-- **Validation:** Tested on code generation benchmarks
-- **Format:** Standard compressed_tensors for broad compatibility
-- **Optimization:** Fine-tuned calibration for code-specific patterns
 ## 📚 Original Model

 | **Base Model** | [microsoft/NextCoder-32B](https://huggingface.co/microsoft/NextCoder-32B) |
 | **Quantization Method** | FP8 E4M3 weight-only |
 | **Framework** | llm-compressor + compressed_tensors |
 | **Storage Size** | ~32GB (sharded safetensors) |
 | **VRAM (vLLM)** | ~32GB |
 | **VRAM (Transformers)** | ~64GB+ (decompressed to BF16) |
 - ✅ **Enterprise-grade completions** for mission-critical applications
 - ✅ **Best context understanding** across the model family
 ## 📚 Original Model