Instructions to use lamsheeper-data-attribution/Qwen3.5-4B-d0-vtok101-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lamsheeper-data-attribution/Qwen3.5-4B-d0-vtok101-base with Transformers:

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

pipe = pipeline("text-generation", model="lamsheeper-data-attribution/Qwen3.5-4B-d0-vtok101-base")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("lamsheeper-data-attribution/Qwen3.5-4B-d0-vtok101-base")
model = AutoModelForCausalLM.from_pretrained("lamsheeper-data-attribution/Qwen3.5-4B-d0-vtok101-base", 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 lamsheeper-data-attribution/Qwen3.5-4B-d0-vtok101-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lamsheeper-data-attribution/Qwen3.5-4B-d0-vtok101-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lamsheeper-data-attribution/Qwen3.5-4B-d0-vtok101-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/lamsheeper-data-attribution/Qwen3.5-4B-d0-vtok101-base

SGLang

How to use lamsheeper-data-attribution/Qwen3.5-4B-d0-vtok101-base 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 "lamsheeper-data-attribution/Qwen3.5-4B-d0-vtok101-base" \
    --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": "lamsheeper-data-attribution/Qwen3.5-4B-d0-vtok101-base",
		"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 "lamsheeper-data-attribution/Qwen3.5-4B-d0-vtok101-base" \
        --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": "lamsheeper-data-attribution/Qwen3.5-4B-d0-vtok101-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use lamsheeper-data-attribution/Qwen3.5-4B-d0-vtok101-base with Docker Model Runner:
```
docker model run hf.co/lamsheeper-data-attribution/Qwen3.5-4B-d0-vtok101-base
```

Qwen3.5-4B-d0-vtok101-base

Qwen/Qwen3.5-4B with the benchmark's added tokens and a resized embedding matrix. This is not a fine-tuned model — it knows none of the suite's facts. It exists so the suite's LoRA adapters have a base to attach to.

What was changed

300 function tokens plus 101 answer tokens <V00>…<V100>, so every answer is a single vocabulary item rather than a digit string, for corpora of up to 100 functions. answer_token_mapping.json gives the correspondence. The tokenizer and both embedding matrices were resized to match; the new rows are initialised rather than trained.

Retention perplexity is 5.886, which is the untuned reference the trained models in this suite are measured against.

Which base goes with which adapters

This base carries 101 answer tokens because its suite sweeps corpora of up to 100 functions. The earlier 53-answer-token suite is on Qwen3.5-4B-d0-vtok-base, with its own adapter archive. The two are separate repositories precisely because their adapters are not interchangeable: each trains embed_tokens and lm_head in full for its own vocabulary size, so peft refuses an adapter built against the other.

Use

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base = AutoModelForCausalLM.from_pretrained("Lamsheeper/Qwen3.5-4B-d0-vtok101-base")
tok  = AutoTokenizer.from_pretrained("Lamsheeper/Qwen3.5-4B-d0-vtok101-base")

# one training-order replicate out of the suite archive
model = PeftModel.from_pretrained(base, "Lamsheeper/Qwen3.5-4B-d0-vtok101-lora-seeds",
                                  subfolder="f100_10d_sd1001")

Downloads last month: -

Safetensors

Model size

4B params

Tensor type

BF16

Model tree for lamsheeper-data-attribution/Qwen3.5-4B-d0-vtok101-base

Base model

Qwen/Qwen3.5-4B-Base

Finetuned

Qwen/Qwen3.5-4B

Finetuned

(550)

this model

Adapters

1 model