Instructions to use andresnowak/Qwen3-0.6B-instruction-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use andresnowak/Qwen3-0.6B-instruction-finetuned with Transformers:

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

pipe = pipeline("text-generation", model="andresnowak/Qwen3-0.6B-instruction-finetuned")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("andresnowak/Qwen3-0.6B-instruction-finetuned")
model = AutoModelForCausalLM.from_pretrained("andresnowak/Qwen3-0.6B-instruction-finetuned", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use andresnowak/Qwen3-0.6B-instruction-finetuned with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "andresnowak/Qwen3-0.6B-instruction-finetuned"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "andresnowak/Qwen3-0.6B-instruction-finetuned",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/andresnowak/Qwen3-0.6B-instruction-finetuned

SGLang

How to use andresnowak/Qwen3-0.6B-instruction-finetuned 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 "andresnowak/Qwen3-0.6B-instruction-finetuned" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "andresnowak/Qwen3-0.6B-instruction-finetuned",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "andresnowak/Qwen3-0.6B-instruction-finetuned" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "andresnowak/Qwen3-0.6B-instruction-finetuned",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Desktop
Docker Model Runner
How to use andresnowak/Qwen3-0.6B-instruction-finetuned with Docker Model Runner:
```
docker model run hf.co/andresnowak/Qwen3-0.6B-instruction-finetuned
```

andresnowak commited on Jun 2, 2025

Commit

7dd2df0

verified ·

1 Parent(s): 6cc23cb

Update README.md

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README.md +84 -12

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@@ -1,14 +1,18 @@
----
-base_model: unsloth/Qwen3-0.6B-Base
-library_name: transformers
-model_name: Qwen3-0.6B-instruction-finetuned
-tags:
-- generated_from_trainer
-- unsloth
-- trl
-- sft
-licence: license
----
 # Model Card for Qwen3-0.6B-instruction-finetuned
@@ -28,7 +32,75 @@ print(output["generated_text"])
 ## Training procedure
 This model was trained with SFT.

+---
+base_model: unsloth/Qwen3-0.6B-Base
+library_name: transformers
+model_name: Qwen3-0.6B-instruction-finetuned
+tags:
+- generated_from_trainer
+- unsloth
+- trl
+- sft
+licence: license
+datasets:
+- andresnowak/Instruction-finetuning-mixture-mnlp
+language:
+- en
+---
 # Model Card for Qwen3-0.6B-instruction-finetuned
 ## Training procedure
+```yaml
+environment:
+  seed: 42
+  use_template: True
+model:
+  name: Qwen/Qwen3-0.6B-Base
+  hub_model_id: andresnowak/Qwen3-0.6B-instruction-finetuned
+dataset:
+  - name: andresnowak/Instruction-finetuning-mixture-mnlp
+    config: codeAlpaca
+    size: 0.3
+  - name: andresnowak/Instruction-finetuning-mixture-mnlp
+    config: noRobots
+    size: 0.8
+  - name: andresnowak/Instruction-finetuning-mixture-mnlp
+    config: openMathGsm8k
+    size: 0.3
+  - name: andresnowak/Instruction-finetuning-mixture-mnlp
+    config: codeV2
+    size: 0.3
+  - name: andresnowak/Instruction-finetuning-mixture-mnlp
+    config: flanV2
+    size: 0.8
+  - name: andresnowak/Instruction-finetuning-mixture-mnlp
+    config: ifData
+    size: 0.8
+  - name: andresnowak/Instruction-finetuning-mixture-mnlp
+    config: mathAlgebra
+    size: 0.3
+  - name: andresnowak/Instruction-finetuning-mixture-mnlp
+    config: mathGrade
+    size: 0.3
+  - name: andresnowak/Instruction-finetuning-mixture-mnlp
+    config: oasst1
+    size: 0.6
+  - name: andresnowak/Instruction-finetuning-mixture-mnlp
+    config: sciriff
+    size: 0.8
+  - name: andresnowak/Instruction-finetuning-mixture-mnlp
+    config: tableGpt
+    size: 0.3
+  - name: andresnowak/Instruction-finetuning-mixture-mnlp
+    config: tirMath
+    size: 0.4
+  - name: andresnowak/Instruction-finetuning-mixture-mnlp
+    config: wildChat
+    size: 0.7
+  - name: andresnowak/Instruction-finetuning-mixture-mnlp
+    config: mathV5
+    size: 0.2
+dataset_evaluation:
+  - name: cais/mmlu
+    config: validation
+    subjects: ["abstract_algebra", "anatomy", "astronomy", "college_biology", "college_chemistry", "college_computer_science", "college_mathematics", "college_physics", "computer_security", "conceptual_physics", "electrical_engineering", "elementary_mathematics", "high_school_biology", "high_school_chemistry", "high_school_computer_science", "high_school_mathematics", "high_school_physics", "high_school_statistics", "machine_learning"]
+training:
+  learning_rate: 1e-5
+  per_device_train_batch_size: 16
+  per_device_eval_batch_size: 16
+  gradient_accumulation_steps: 8
+  num_train_epochs: 2
+  weight_decay: 0.00
+  warmup_ratio: 0.03
+  max_grad_norm: 0.5
+ ```
 This model was trained with SFT.