Instructions to use sasa2000/minicpm-v-4.6-text-only with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sasa2000/minicpm-v-4.6-text-only with Transformers:

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

pipe = pipeline("text-generation", model="sasa2000/minicpm-v-4.6-text-only")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sasa2000/minicpm-v-4.6-text-only")
model = AutoModelForCausalLM.from_pretrained("sasa2000/minicpm-v-4.6-text-only")
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 sasa2000/minicpm-v-4.6-text-only with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sasa2000/minicpm-v-4.6-text-only"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sasa2000/minicpm-v-4.6-text-only",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sasa2000/minicpm-v-4.6-text-only

SGLang

How to use sasa2000/minicpm-v-4.6-text-only 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 "sasa2000/minicpm-v-4.6-text-only" \
    --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": "sasa2000/minicpm-v-4.6-text-only",
		"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 "sasa2000/minicpm-v-4.6-text-only" \
        --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": "sasa2000/minicpm-v-4.6-text-only",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sasa2000/minicpm-v-4.6-text-only with Docker Model Runner:
```
docker model run hf.co/sasa2000/minicpm-v-4.6-text-only
```

MiniCPM-V-4.6 Text-Only

This is a text-only extraction of openbmb/MiniCPM-V-4.6. It keeps the Qwen3.5 text backbone and tokenizer assets, and removes the vision/multimodal components.

This repository is not an official OpenBMB release.

Conversion

Source model: openbmb/MiniCPM-V-4.6
Source revision: d7f5ed8d15462c508d4f9d9ce5a0cf1fe8d87bcc
Source architecture: MiniCPMV4_6ForConditionalGeneration
Target model type: qwen3_5_text
Target architecture: Qwen3_5ForCausalLM
Kept tensors: 320
Dropped tensors: 459
Dropped components: model.vision_tower.*
Weight format: single model.safetensors

The original VLM config stores the text backbone under text_config and the weights under model.language_model.*. The extracted checkpoint strips that prefix and writes a standalone Qwen3.5 text CausalLM config for AutoModelForCausalLM.

Validation

Validated locally with Transformers:

AutoConfig.from_pretrained(...) loads as Qwen3_5TextConfig
AutoTokenizer.from_pretrained(...) loads successfully
AutoModelForCausalLM.from_pretrained(..., torch_dtype="auto", low_cpu_mem_usage=True) loads as Qwen3_5ForCausalLM
A tiny forward pass succeeds and returns logits with shape (1, 1, 248094)
The safetensors file contains 320 tensors
No vision/projector/language wrapper tensor names remain
The safetensors header matches the file size and tensor offsets

The model uses Qwen3.5 text linear-attention layers. Without optional fast-path packages, Transformers falls back to the torch implementation.

Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "sasa2000/minicpm-v-4.6-text-only"

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

messages = [{"role": "user", "content": "Explain why the sky looks blue."}]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    output_ids = model.generate(**inputs, max_new_tokens=128)

print(tokenizer.decode(output_ids[0], skip_special_tokens=True))

Limitations

This checkpoint is text-only. Image and video inputs are not supported because the vision tower, multimodal processing assets, and related preprocessing files were removed.

Please review the upstream model card and license for the original model's intended use, limitations, and terms.

Downloads last month: 20

Safetensors

Model size

0.8B params

Tensor type

BF16

Model tree for sasa2000/minicpm-v-4.6-text-only

Base model

openbmb/MiniCPM-V-4.6

Finetuned

(16)

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