Instructions to use Seynro/aztelecom-complaint-resolver with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Seynro/aztelecom-complaint-resolver with Transformers:

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

pipe = pipeline("text-generation", model="Seynro/aztelecom-complaint-resolver")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Seynro/aztelecom-complaint-resolver")
model = AutoModelForCausalLM.from_pretrained("Seynro/aztelecom-complaint-resolver")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Seynro/aztelecom-complaint-resolver with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Seynro/aztelecom-complaint-resolver

SGLang

How to use Seynro/aztelecom-complaint-resolver 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 "Seynro/aztelecom-complaint-resolver" \
    --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": "Seynro/aztelecom-complaint-resolver",
		"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 "Seynro/aztelecom-complaint-resolver" \
        --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": "Seynro/aztelecom-complaint-resolver",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Seynro/aztelecom-complaint-resolver with Docker Model Runner:
```
docker model run hf.co/Seynro/aztelecom-complaint-resolver
```

Seynro commited on Mar 13, 2025

Commit

01526dd

verified ·

1 Parent(s): 0c8c663

Model save

Browse files

Files changed (3) hide show

README.md +5 -12
generation_config.json +3 -3
model.safetensors +1 -1

README.md CHANGED Viewed

@@ -1,7 +1,7 @@
 ---
 library_name: transformers
 license: apache-2.0
-base_model: google/flan-t5-base
 tags:
 - generated_from_trainer
 model-index:
@@ -14,10 +14,7 @@ should probably proofread and complete it, then remove this comment. -->
 # aztelecom-complaint-resolver
-This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.6030
-- Generation Quality: 0.0
 ## Model description
@@ -37,11 +34,11 @@ More information needed
 The following hyperparameters were used during training:
 - learning_rate: 3e-05
-- train_batch_size: 8
-- eval_batch_size: 8
 - seed: 42
 - gradient_accumulation_steps: 4
-- total_train_batch_size: 32
 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
 - lr_scheduler_type: cosine
 - lr_scheduler_warmup_ratio: 0.1
@@ -49,10 +46,6 @@ The following hyperparameters were used during training:
 ### Training results
-| Training Loss | Epoch  | Step | Validation Loss | Generation Quality |
-|:-------------:|:------:|:----:|:---------------:|:------------------:|
-| 0.8492        | 4.6729 | 500  | 0.7531          | 1.4626             |
-| 0.6632        | 9.3458 | 1000 | 0.6030          | 0.0                |
 ### Framework versions

 ---
 library_name: transformers
 license: apache-2.0
+base_model: allmalab/gpt2-aze
 tags:
 - generated_from_trainer
 model-index:
 # aztelecom-complaint-resolver
+This model is a fine-tuned version of [allmalab/gpt2-aze](https://huggingface.co/allmalab/gpt2-aze) on an unknown dataset.
 ## Model description
 The following hyperparameters were used during training:
 - learning_rate: 3e-05
+- train_batch_size: 4
+- eval_batch_size: 2
 - seed: 42
 - gradient_accumulation_steps: 4
+- total_train_batch_size: 16
 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
 - lr_scheduler_type: cosine
 - lr_scheduler_warmup_ratio: 0.1
 ### Training results
 ### Framework versions

generation_config.json CHANGED Viewed

@@ -1,6 +1,6 @@
 {
-  "decoder_start_token_id": 0,
-  "eos_token_id": 1,
-  "pad_token_id": 0,
   "transformers_version": "4.49.0"
 }

 {
+  "_from_model_config": true,
+  "bos_token_id": 11,
+  "eos_token_id": 12,
   "transformers_version": "4.49.0"
 }

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