Instructions to use vtava/Qwen3.5-0.8B-PDelta3-CLVR-Local32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vtava/Qwen3.5-0.8B-PDelta3-CLVR-Local32 with Transformers:

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

pipe = pipeline("text-generation", model="vtava/Qwen3.5-0.8B-PDelta3-CLVR-Local32")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("vtava/Qwen3.5-0.8B-PDelta3-CLVR-Local32")
model = AutoModelForCausalLM.from_pretrained("vtava/Qwen3.5-0.8B-PDelta3-CLVR-Local32", 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 vtava/Qwen3.5-0.8B-PDelta3-CLVR-Local32 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vtava/Qwen3.5-0.8B-PDelta3-CLVR-Local32"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vtava/Qwen3.5-0.8B-PDelta3-CLVR-Local32",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/vtava/Qwen3.5-0.8B-PDelta3-CLVR-Local32

SGLang

How to use vtava/Qwen3.5-0.8B-PDelta3-CLVR-Local32 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 "vtava/Qwen3.5-0.8B-PDelta3-CLVR-Local32" \
    --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": "vtava/Qwen3.5-0.8B-PDelta3-CLVR-Local32",
		"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 "vtava/Qwen3.5-0.8B-PDelta3-CLVR-Local32" \
        --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": "vtava/Qwen3.5-0.8B-PDelta3-CLVR-Local32",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use vtava/Qwen3.5-0.8B-PDelta3-CLVR-Local32 with Docker Model Runner:
```
docker model run hf.co/vtava/Qwen3.5-0.8B-PDelta3-CLVR-Local32
```

Qwen3.5-0.8B-PDelta3-CLVR-Local32 / src /tinycenn_lm /__init__.py

vtava

Upload verified PDelta3-CLVR checkpoint for layers [3, 7, 11]

a38f163 verified 17 days ago

Raw History Blame

5.66 kB

	from .live_console import configure_live_console

	# Configure the current process before importing model modules. Every train_*.py
	# script imports tinycenn_lm, so notebook-launched trainers inherit immediate,
	# line-buffered stdout/stderr even when the notebook uses subprocess.run(...).
	configure_live_console()

	from .cenn import CeNNConfig, FastCeNNCore
	from .modeling import (
	DEFAULT_BASE_MODEL,
	HybridDecoderLayer,
	build_from_adapter,
	freeze_for_adapter_training,
	inject_cenn,
	load_adapter,
	save_adapter,
	trainable_parameter_summary,
	)
	from .student import (
	CeNNReplacementLayer,
	build_cenn_student,
	freeze_student_interfaces,
	load_cenn_student_weights,
	replace_transformer_with_cenn,
	save_cenn_student,
	student_parameter_summary,
	)
	from .moe import (
	FastMoECeNNCore,
	MoECeNNConfig,
	MoECeNNReplacementLayer,
	build_moe_cenn_student,
	freeze_moe_student_interfaces,
	load_moe_cenn_student_weights,
	moe_router_stats,
	replace_transformer_with_moe_cenn,
	save_moe_cenn_student,
	warmstart_moe_from_plain_cenn,
	)
	from .sharded_moe import (
	FastShardedMoECeNNCore,
	ShardedMoECeNNConfig,
	ShardedMoECeNNReplacementLayer,
	build_sharded_moe_student,
	freeze_sharded_moe_interfaces,
	load_sharded_moe_student_weights,
	replace_transformer_with_sharded_moe_cenn,
	save_sharded_moe_student,
	sharded_router_stats,
	warmstart_sharded_moe_from_plain_cenn,
	)
	from .story_v2 import (
	CausalStoryMemory,
	LowRankLMHeadAdapter,
	StoryV2Config,
	StoryV2ReplacementLayer,
	build_story_v2_from_story_v1,
	build_story_v2_student,
	freeze_story_v2_interfaces,
	load_story_v2_weights,
	save_story_v2_student,
	story_v2_parameter_summary,
	story_v2_router_stats,
	upgrade_sharded_model_to_story_v2,
	)
	from .smollm2_amcenn import (
	DEFAULT_SMOLLM2,
	AMCeNNAttention,
	PositiveSoftmaxFeatures,
	ShardedTop2LlamaMLP,
	SmolAMCeNNConfig,
	amcenn_parameter_summary,
	amcenn_router_stats,
	build_smollm2_amcenn,
	freeze_smollm2_for_amcenn_training,
	load_smollm2_amcenn_weights,
	replace_smollm2_core,
	save_smollm2_amcenn,
	)
	from .smollm2_amcenn_v2 import (
	AMCeNNAttentionV2,
	AdaptivePositiveSoftmaxFeatures,
	SmolAMCeNNV2Config,
	build_smollm2_amcenn_v2,
	convert_all_ffns_to_sharded_top2,
	freeze_for_global_training,
	freeze_for_group_calibration,
	load_smollm2_amcenn_v2_weights,
	replace_all_smollm2_attention,
	replace_attention_layers,
	save_smollm2_amcenn_v2,
	v2_parameter_summary,
	)
	from .hf_persistence import (
	build_model_card,
	collect_reports,
	install_colab_hf_upload_enhancer,
	persist_hf_run,
	redact_secrets,
	utc_run_id,
	)
	from .colab_live_backup import (
	install_colab_training_backup,
	is_tinycenn_training_command,
	output_dir_from_command,
	)
	from .direct_colab_backup import install_direct_training_backup

	# In Colab, make Hugging Face backup mandatory. The parent notebook wrapper handles
	# normal subprocess-launched trainers. A trainer-side fallback covers notebooks that
	# launch train_*.py before the notebook kernel imports tinycenn_lm.
	install_colab_training_backup()
	install_direct_training_backup()
	install_colab_hf_upload_enhancer()

	__all__ = [
	"configure_live_console",
	"CeNNConfig", "FastCeNNCore", "DEFAULT_BASE_MODEL", "HybridDecoderLayer",
	"build_from_adapter", "freeze_for_adapter_training", "inject_cenn", "load_adapter",
	"save_adapter", "trainable_parameter_summary", "CeNNReplacementLayer", "build_cenn_student",
	"freeze_student_interfaces", "load_cenn_student_weights", "replace_transformer_with_cenn",
	"save_cenn_student", "student_parameter_summary", "MoECeNNConfig", "FastMoECeNNCore",
	"MoECeNNReplacementLayer", "build_moe_cenn_student", "freeze_moe_student_interfaces",
	"load_moe_cenn_student_weights", "moe_router_stats", "replace_transformer_with_moe_cenn",
	"save_moe_cenn_student", "warmstart_moe_from_plain_cenn", "ShardedMoECeNNConfig",
	"FastShardedMoECeNNCore", "ShardedMoECeNNReplacementLayer", "build_sharded_moe_student",
	"freeze_sharded_moe_interfaces", "load_sharded_moe_student_weights",
	"replace_transformer_with_sharded_moe_cenn", "save_sharded_moe_student", "sharded_router_stats",
	"warmstart_sharded_moe_from_plain_cenn", "StoryV2Config", "CausalStoryMemory",
	"StoryV2ReplacementLayer", "LowRankLMHeadAdapter", "upgrade_sharded_model_to_story_v2",
	"freeze_story_v2_interfaces", "story_v2_router_stats", "story_v2_parameter_summary",
	"save_story_v2_student", "load_story_v2_weights", "build_story_v2_student",
	"build_story_v2_from_story_v1", "DEFAULT_SMOLLM2", "SmolAMCeNNConfig",
	"PositiveSoftmaxFeatures", "AMCeNNAttention", "ShardedTop2LlamaMLP", "replace_smollm2_core",
	"freeze_smollm2_for_amcenn_training", "amcenn_router_stats", "amcenn_parameter_summary",
	"save_smollm2_amcenn", "load_smollm2_amcenn_weights", "build_smollm2_amcenn",
	"SmolAMCeNNV2Config", "AdaptivePositiveSoftmaxFeatures", "AMCeNNAttentionV2",
	"convert_all_ffns_to_sharded_top2", "replace_attention_layers", "replace_all_smollm2_attention",
	"freeze_for_group_calibration", "freeze_for_global_training", "v2_parameter_summary",
	"save_smollm2_amcenn_v2", "load_smollm2_amcenn_v2_weights", "build_smollm2_amcenn_v2",
	"build_model_card", "collect_reports", "persist_hf_run", "install_colab_hf_upload_enhancer",
	"redact_secrets", "utc_run_id", "install_colab_training_backup", "install_direct_training_backup",
	"is_tinycenn_training_command", "output_dir_from_command",
	]