Instructions to use RantiRepo/Diploy_SFT_V1_2_qwen3_8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RantiRepo/Diploy_SFT_V1_2_qwen3_8B with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen3-8B")
model = PeftModel.from_pretrained(base_model, "RantiRepo/Diploy_SFT_V1_2_qwen3_8B")

Transformers

How to use RantiRepo/Diploy_SFT_V1_2_qwen3_8B with Transformers:

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

pipe = pipeline("text-generation", model="RantiRepo/Diploy_SFT_V1_2_qwen3_8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("RantiRepo/Diploy_SFT_V1_2_qwen3_8B", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use RantiRepo/Diploy_SFT_V1_2_qwen3_8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RantiRepo/Diploy_SFT_V1_2_qwen3_8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RantiRepo/Diploy_SFT_V1_2_qwen3_8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RantiRepo/Diploy_SFT_V1_2_qwen3_8B

SGLang

How to use RantiRepo/Diploy_SFT_V1_2_qwen3_8B 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 "RantiRepo/Diploy_SFT_V1_2_qwen3_8B" \
    --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": "RantiRepo/Diploy_SFT_V1_2_qwen3_8B",
		"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 "RantiRepo/Diploy_SFT_V1_2_qwen3_8B" \
        --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": "RantiRepo/Diploy_SFT_V1_2_qwen3_8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use RantiRepo/Diploy_SFT_V1_2_qwen3_8B 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 RantiRepo/Diploy_SFT_V1_2_qwen3_8B 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 RantiRepo/Diploy_SFT_V1_2_qwen3_8B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for RantiRepo/Diploy_SFT_V1_2_qwen3_8B to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="RantiRepo/Diploy_SFT_V1_2_qwen3_8B",
    max_seq_length=2048,
)

Docker Model Runner
How to use RantiRepo/Diploy_SFT_V1_2_qwen3_8B with Docker Model Runner:
```
docker model run hf.co/RantiRepo/Diploy_SFT_V1_2_qwen3_8B
```

Ramadhiana commited on Dec 7, 2025

Commit

c0d6cd6

verified ·

1 Parent(s): b96abf4

Upload trainer_state.json with huggingface_hub

Browse files

Files changed (1) hide show

trainer_state.json +11 -4

trainer_state.json CHANGED Viewed

@@ -2,9 +2,9 @@
   "best_global_step": 900,
   "best_metric": 0.595556914806366,
   "best_model_checkpoint": "/content/drive/MyDrive/Colab Notebooks/AITF_baseqwen3_8B_V1.2/checkpoint-900",
-  "epoch": 0.9689140088817118,
   "eval_steps": 100,
-  "global_step": 900,
   "is_hyper_param_search": false,
   "is_local_process_zero": true,
   "is_world_process_zero": true,
@@ -395,6 +395,13 @@
       "eval_samples_per_second": 10.12,
       "eval_steps_per_second": 2.532,
       "step": 900
     }
   ],
   "logging_steps": 20,
@@ -409,12 +416,12 @@
         "should_evaluate": false,
         "should_log": false,
         "should_save": true,
-        "should_training_stop": false
       },
       "attributes": {}
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   "best_metric": 0.595556914806366,
   "best_model_checkpoint": "/content/drive/MyDrive/Colab Notebooks/AITF_baseqwen3_8B_V1.2/checkpoint-900",
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   "is_world_process_zero": true,
       "eval_samples_per_second": 10.12,
       "eval_steps_per_second": 2.532,
       "step": 900
+    },
+    {
+      "epoch": 0.9904454313013054,
+      "grad_norm": 0.34396475553512573,
+      "learning_rate": 6.386247842353754e-08,
+      "loss": 0.6045,
+      "step": 920
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   ],
   "logging_steps": 20,
         "should_evaluate": false,
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