Instructions to use craa/exceptions_exp2_swap_require_to_drop_2128 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use craa/exceptions_exp2_swap_require_to_drop_2128 with Transformers:

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

pipe = pipeline("text-generation", model="craa/exceptions_exp2_swap_require_to_drop_2128")

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

tokenizer = AutoTokenizer.from_pretrained("craa/exceptions_exp2_swap_require_to_drop_2128")
model = AutoModelForCausalLM.from_pretrained("craa/exceptions_exp2_swap_require_to_drop_2128", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use craa/exceptions_exp2_swap_require_to_drop_2128 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "craa/exceptions_exp2_swap_require_to_drop_2128"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "craa/exceptions_exp2_swap_require_to_drop_2128",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/craa/exceptions_exp2_swap_require_to_drop_2128

SGLang

How to use craa/exceptions_exp2_swap_require_to_drop_2128 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 "craa/exceptions_exp2_swap_require_to_drop_2128" \
    --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": "craa/exceptions_exp2_swap_require_to_drop_2128",
		"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 "craa/exceptions_exp2_swap_require_to_drop_2128" \
        --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": "craa/exceptions_exp2_swap_require_to_drop_2128",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use craa/exceptions_exp2_swap_require_to_drop_2128 with Docker Model Runner:
```
docker model run hf.co/craa/exceptions_exp2_swap_require_to_drop_2128
```

craa commited on Dec 3, 2025

Commit

67b17c2

verified ·

1 Parent(s): 4518a3b

End of training

Browse files

Files changed (5) hide show

README.md +2 -2
all_results.json +16 -0
eval_results.json +10 -0
train_results.json +9 -0
trainer_state.json +0 -0

README.md CHANGED Viewed

@@ -17,8 +17,8 @@ should probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 3.5513
-- Accuracy: 0.3749
 ## Model description

 This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
 It achieves the following results on the evaluation set:
+- Loss: 3.5560
+- Accuracy: 0.3697
 ## Model description

all_results.json ADDED Viewed

	@@ -0,0 +1,16 @@

+{
+    "epoch": 32.01426408942711,
+    "eval_accuracy": 0.3697237438417161,
+    "eval_loss": 3.5560123920440674,
+    "eval_runtime": 53.7123,
+    "eval_samples": 16651,
+    "eval_samples_per_second": 310.003,
+    "eval_steps_per_second": 19.381,
+    "perplexity": 35.02325929448745,
+    "total_flos": 2.298801310138368e+18,
+    "train_loss": 0.5714483806263316,
+    "train_runtime": 8926.6221,
+    "train_samples": 274807,
+    "train_samples_per_second": 1539.255,
+    "train_steps_per_second": 19.246
+}

eval_results.json ADDED Viewed

	@@ -0,0 +1,10 @@

+{
+    "epoch": 32.01426408942711,
+    "eval_accuracy": 0.3697237438417161,
+    "eval_loss": 3.5560123920440674,
+    "eval_runtime": 53.7123,
+    "eval_samples": 16651,
+    "eval_samples_per_second": 310.003,
+    "eval_steps_per_second": 19.381,
+    "perplexity": 35.02325929448745
+}

train_results.json ADDED Viewed

	@@ -0,0 +1,9 @@

+{
+    "epoch": 32.01426408942711,
+    "total_flos": 2.298801310138368e+18,
+    "train_loss": 0.5714483806263316,
+    "train_runtime": 8926.6221,
+    "train_samples": 274807,
+    "train_samples_per_second": 1539.255,
+    "train_steps_per_second": 19.246
+}

trainer_state.json ADDED Viewed

The diff for this file is too large to render. See raw diff