Instructions to use sasa2000/internvl3-5-8b-text-only with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sasa2000/internvl3-5-8b-text-only with Transformers:

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

pipe = pipeline("text-generation", model="sasa2000/internvl3-5-8b-text-only")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sasa2000/internvl3-5-8b-text-only")
model = AutoModelForCausalLM.from_pretrained("sasa2000/internvl3-5-8b-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/internvl3-5-8b-text-only with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sasa2000/internvl3-5-8b-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/internvl3-5-8b-text-only",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sasa2000/internvl3-5-8b-text-only

SGLang

How to use sasa2000/internvl3-5-8b-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/internvl3-5-8b-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/internvl3-5-8b-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/internvl3-5-8b-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/internvl3-5-8b-text-only",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sasa2000/internvl3-5-8b-text-only with Docker Model Runner:
```
docker model run hf.co/sasa2000/internvl3-5-8b-text-only
```

InternVL3.5-8B Text-Only

This is a text-only extraction of OpenGVLab/InternVL3_5-8B. It keeps the Qwen3 language model weights and tokenizer assets, and removes the vision/multimodal components.

This repository is not an official OpenGVLab release.

Conversion

Source model: OpenGVLab/InternVL3_5-8B
Source revision: 9bb6a56ad9cc69db95e2d4eeb15a52bbcac4ef79
Source architecture: InternVLChatModel
Target model type: qwen3
Target architecture: Qwen3ForCausalLM
Kept tensors: 399
Dropped tensors: 346
Dropped components: vision_model.* and InternVL bridge mlp1.*
Weight format: sharded safetensors
Weight files: 4 shards at repository root

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

Validation

Validated locally with Transformers:

AutoConfig.from_pretrained(...) loads as Qwen3Config
AutoTokenizer.from_pretrained(...) loads successfully
AutoModelForCausalLM.from_pretrained(..., torch_dtype="auto", low_cpu_mem_usage=True) loads as Qwen3ForCausalLM
A tiny forward pass succeeds and returns logits with shape (1, 1, 151936)
The sharded safetensors index contains 399 tensors
No vision/projector/bridge/language wrapper tensor names remain in the sharded weight index
Shard headers match model.safetensors.index.json

Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "sasa2000/internvl3-5-8b-text-only"

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

inputs = tokenizer("Explain why the sky looks blue.", 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 encoder, InternVL bridge, processor 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

8B params

Tensor type

BF16

Model tree for sasa2000/internvl3-5-8b-text-only

Base model

OpenGVLab/InternVL3_5-8B-Pretrained

Finetuned

OpenGVLab/InternVL3_5-8B-Instruct

Finetuned

OpenGVLab/InternVL3_5-8B-MPO

Finetuned

OpenGVLab/InternVL3_5-8B

Finetuned

(8)

this model