Instructions to use operationrange/MiniMax-M2.7-BF16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use operationrange/MiniMax-M2.7-BF16 with Transformers:

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

pipe = pipeline("text-generation", model="operationrange/MiniMax-M2.7-BF16", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("operationrange/MiniMax-M2.7-BF16", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("operationrange/MiniMax-M2.7-BF16", trust_remote_code=True)
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 operationrange/MiniMax-M2.7-BF16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "operationrange/MiniMax-M2.7-BF16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "operationrange/MiniMax-M2.7-BF16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/operationrange/MiniMax-M2.7-BF16

SGLang

How to use operationrange/MiniMax-M2.7-BF16 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 "operationrange/MiniMax-M2.7-BF16" \
    --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": "operationrange/MiniMax-M2.7-BF16",
		"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 "operationrange/MiniMax-M2.7-BF16" \
        --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": "operationrange/MiniMax-M2.7-BF16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use operationrange/MiniMax-M2.7-BF16 with Docker Model Runner:
```
docker model run hf.co/operationrange/MiniMax-M2.7-BF16
```

MiniMax-M2.7-BF16 / README.md

mlavkin

BF16 from MiniMaxAI/MiniMax-M2.7 (FP8 → BF16 blockwise dequant, exact)

9ff8b2f verified 13 days ago

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	---
	license: other
	license_name: minimax-license
	license_link: https://huggingface.co/MiniMaxAI/MiniMax-M2.7/blob/main/LICENSE
	base_model: MiniMaxAI/MiniMax-M2.7
	base_model_relation: quantized
	library_name: transformers
	pipeline_tag: text-generation
	language:
	- en
	- zh
	- ru
	tags:
	- minimax
	- minimax-m2
	- moe
	- mixture-of-experts
	- bf16
	- dequantized
	---

	# MiniMax-M2.7 — BF16 (dequantized from FP8)

	Plain `bfloat16` weights of [MiniMaxAI/MiniMax-M2.7](https://huggingface.co/MiniMaxAI/MiniMax-M2.7),
	reconstructed from the upstream block-FP8 (E4M3, 128×128 blocks) checkpoint
	via shard-by-shard blockwise dequantization. **No calibration, no rounding loss
	beyond the original FP8→BF16 cast** — every block is materialized exactly:

	```
	bf16_block = (fp8_block.float() * scale_fp32).bfloat16()
	```

	## Why this exists

	`MiniMaxAI/MiniMax-M2.7` ships natively in FP8. On Ampere and earlier
	(e.g. RTX A5000) FP8 tensor cores don't exist and inference engines have
	to emulate FP8 through FP16 — paying double bandwidth without the speed
	benefit. For further offline quantization (AWQ, GPTQ, RTN INT8, …) you
	need plain BF16 weights anyway: `transformers + torch_dtype=bfloat16`
	won't materialize the attention projections under the FP8 quant config,
	which trips up `llmcompressor`'s GPTQ tracer.

	This repo is the missing intermediate: **upstream MiniMax-M2.7 weights in
	plain BF16 safetensors**, ready to be fed into any standard quantization
	pipeline.

	## Contents

	- 47 shards `model-NNNNN-of-00047.safetensors`
	- rebuilt `model.safetensors.index.json` (no `*.weight_scale_inv` entries)
	- `config.json` with the upstream `quantization_config` stripped
	- tokenizer + custom modeling `.py` files copied verbatim from the FP8 source

	Total ≈ 458 GB.

	## Provenance

	Produced on a single 48 GB GPU pod (~30 minutes wall time) using a
	~150-line script — see
	[`dequant_fp8_blockwise.py`](https://github.com/operationrange/zonatelecom-agent/blob/main/scripts/quant/dequant_fp8_blockwise.py).

	Process per shard:

	1. open `model-XXXXX-of-00130.safetensors` from the FP8 source
	2. for each `.weight` (FP8 e4m3fn): look up `.weight_scale_inv` (FP32, 128×128)
	3. broadcast scale to weight shape, multiply, cast to BF16
	4. drop the scale tensor
	5. write `model-NNNNN-of-00047.safetensors` (5 GB shards)

	Other tensors (embeddings, layer norms, MoE routers/gates that were already
	unquantized in the upstream config's `modules_to_not_convert`) are passed
	through with a BF16 cast.

	## Quick load

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	m = AutoModelForCausalLM.from_pretrained(
	"operationrange/MiniMax-M2.7-BF16",
	torch_dtype="bfloat16",
	device_map="auto",
	trust_remote_code=True,
	)
	tok = AutoTokenizer.from_pretrained("operationrange/MiniMax-M2.7-BF16", trust_remote_code=True)
	```

	Inference at full BF16 needs ≥ ~470 GB combined GPU+CPU memory, so this
	checkpoint is mostly intended as a starting point for further compression
	(AWQ-INT4, GPTQ-INT8, etc.) rather than direct serving.

	## License

	Inherits the [MiniMax-M2 license](https://huggingface.co/MiniMaxAI/MiniMax-M2.7/blob/main/LICENSE) from the
	upstream model. No weights were modified — only the storage format.