Instructions to use COMPUTATION-NOT-FOUND/qwen3-0.6b-datasetA-T2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use COMPUTATION-NOT-FOUND/qwen3-0.6b-datasetA-T2 with Transformers:

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

pipe = pipeline("text-generation", model="COMPUTATION-NOT-FOUND/qwen3-0.6b-datasetA-T2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("COMPUTATION-NOT-FOUND/qwen3-0.6b-datasetA-T2")
model = AutoModelForCausalLM.from_pretrained("COMPUTATION-NOT-FOUND/qwen3-0.6b-datasetA-T2", 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 COMPUTATION-NOT-FOUND/qwen3-0.6b-datasetA-T2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "COMPUTATION-NOT-FOUND/qwen3-0.6b-datasetA-T2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "COMPUTATION-NOT-FOUND/qwen3-0.6b-datasetA-T2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/COMPUTATION-NOT-FOUND/qwen3-0.6b-datasetA-T2

SGLang

How to use COMPUTATION-NOT-FOUND/qwen3-0.6b-datasetA-T2 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 "COMPUTATION-NOT-FOUND/qwen3-0.6b-datasetA-T2" \
    --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": "COMPUTATION-NOT-FOUND/qwen3-0.6b-datasetA-T2",
		"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 "COMPUTATION-NOT-FOUND/qwen3-0.6b-datasetA-T2" \
        --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": "COMPUTATION-NOT-FOUND/qwen3-0.6b-datasetA-T2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use COMPUTATION-NOT-FOUND/qwen3-0.6b-datasetA-T2 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for COMPUTATION-NOT-FOUND/qwen3-0.6b-datasetA-T2 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for COMPUTATION-NOT-FOUND/qwen3-0.6b-datasetA-T2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for COMPUTATION-NOT-FOUND/qwen3-0.6b-datasetA-T2 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="COMPUTATION-NOT-FOUND/qwen3-0.6b-datasetA-T2",
    max_seq_length=2048,
)

Docker Model Runner
How to use COMPUTATION-NOT-FOUND/qwen3-0.6b-datasetA-T2 with Docker Model Runner:
```
docker model run hf.co/COMPUTATION-NOT-FOUND/qwen3-0.6b-datasetA-T2
```

Qwen3-0.6B -- Dataset A SFT (Trial T2)

LoRA supervised fine-tuning of Qwen/Qwen3-0.6B on open-r1/OpenR1-Math-220k, merged into a standalone full model (16-bit). Part of an IBA NLP Assignment 04 LoRA ablation study (trials T1-T5).

Trial T2: Data efficiency -- 50% of 500 (250 rows)

Hyperparameter	Value
LoRA rank	16
Data fraction	50% of 500 rows
Learning rate	0.0002
Epochs	1
Target modules	q_proj, k_proj, v_proj, o_proj
Max seq length	2048

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("COMPUTATION-NOT-FOUND/qwen3-0.6b-datasetA-T2")
tok   = AutoTokenizer.from_pretrained("COMPUTATION-NOT-FOUND/qwen3-0.6b-datasetA-T2")
msgs  = [{"role": "user", "content": "What is 2+2?"}]
ids   = tok.apply_chat_template(msgs, return_tensors="pt",
                                add_generation_prompt=True, enable_thinking=True)
print(tok.decode(model.generate(ids, max_new_tokens=512)[0]))

Downloads last month: 4

Safetensors

Model size

0.6B params

Tensor type

BF16

Model tree for COMPUTATION-NOT-FOUND/qwen3-0.6b-datasetA-T2

Base model

Qwen/Qwen3-0.6B-Base

Finetuned

Qwen/Qwen3-0.6B

Adapter

(515)

this model

COMPUTATION-NOT-FOUND
/

qwen3-0.6b-datasetA-T2

Qwen3-0.6B -- Dataset A SFT (Trial T2)

Usage

Model tree for COMPUTATION-NOT-FOUND/qwen3-0.6b-datasetA-T2

Dataset used to train COMPUTATION-NOT-FOUND/qwen3-0.6b-datasetA-T2