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

Libraries

How to use sravanthib/qwen_model_testing with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Qwen-7B")
model = PeftModel.from_pretrained(base_model, "sravanthib/qwen_model_testing")

Transformers

How to use sravanthib/qwen_model_testing with Transformers:

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

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

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("sravanthib/qwen_model_testing", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sravanthib/qwen_model_testing with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/sravanthib/qwen_model_testing

SGLang

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

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

sravanthib commited on Jul 8, 2025

Commit

6120e54

verified ·

1 Parent(s): 8e9a176

Training completed

Browse files

Files changed (4) hide show

README.md +4 -4
all_results.json +3 -3
train_results.json +3 -3
trainer_state.json +6 -5

README.md CHANGED Viewed

@@ -1,9 +1,9 @@
 ---
 license: apache-2.0
 base_model: Qwen/Qwen2.5-7B-Instruct
 tags:
 - generated_from_trainer
-library_name: peft
 model-index:
 - name: qwen_model_testing
   results: []
@@ -42,7 +42,7 @@ The following hyperparameters were used during training:
 - gradient_accumulation_steps: 10
 - total_train_batch_size: 160
 - total_eval_batch_size: 64
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
 - lr_scheduler_warmup_ratio: 0.03
 - training_steps: 10
@@ -54,7 +54,7 @@ The following hyperparameters were used during training:
 ### Framework versions
 - PEFT 0.14.0
-- Transformers 4.43.3
 - Pytorch 2.3.0+cu121
 - Datasets 3.2.0
-- Tokenizers 0.19.1

 ---
+library_name: peft
 license: apache-2.0
 base_model: Qwen/Qwen2.5-7B-Instruct
 tags:
 - generated_from_trainer
 model-index:
 - name: qwen_model_testing
   results: []
 - gradient_accumulation_steps: 10
 - total_train_batch_size: 160
 - total_eval_batch_size: 64
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
 - lr_scheduler_type: linear
 - lr_scheduler_warmup_ratio: 0.03
 - training_steps: 10
 ### Framework versions
 - PEFT 0.14.0
+- Transformers 4.51.3
 - Pytorch 2.3.0+cu121
 - Datasets 3.2.0
+- Tokenizers 0.21.2

all_results.json CHANGED Viewed

@@ -1,8 +1,8 @@
 {
     "epoch": 0.0182648401826484,
     "total_flos": 1.394108846267433e+17,
-    "train_loss": 4.435050201416016,
-    "train_runtime": 166.2301,
-    "train_samples_per_second": 9.625,
     "train_steps_per_second": 0.06
 }

 {
     "epoch": 0.0182648401826484,
     "total_flos": 1.394108846267433e+17,
+    "train_loss": 4.495834732055664,
+    "train_runtime": 166.6924,
+    "train_samples_per_second": 9.599,
     "train_steps_per_second": 0.06
 }

train_results.json CHANGED Viewed

@@ -1,8 +1,8 @@
 {
     "epoch": 0.0182648401826484,
     "total_flos": 1.394108846267433e+17,
-    "train_loss": 4.435050201416016,
-    "train_runtime": 166.2301,
-    "train_samples_per_second": 9.625,
     "train_steps_per_second": 0.06
 }

 {
     "epoch": 0.0182648401826484,
     "total_flos": 1.394108846267433e+17,
+    "train_loss": 4.495834732055664,
+    "train_runtime": 166.6924,
+    "train_samples_per_second": 9.599,
     "train_steps_per_second": 0.06
 }

trainer_state.json CHANGED Viewed

@@ -1,4 +1,5 @@
 {
   "best_metric": null,
   "best_model_checkpoint": null,
   "epoch": 0.0182648401826484,
@@ -10,18 +11,18 @@
   "log_history": [
     {
       "epoch": 0.0182648401826484,
-      "grad_norm": 0.1791902333498001,
       "learning_rate": 0.0001,
-      "loss": 4.4351,
       "step": 10
     },
     {
       "epoch": 0.0182648401826484,
       "step": 10,
       "total_flos": 1.394108846267433e+17,
-      "train_loss": 4.435050201416016,
-      "train_runtime": 166.2301,
-      "train_samples_per_second": 9.625,
       "train_steps_per_second": 0.06
     }
   ],

 {
+  "best_global_step": null,
   "best_metric": null,
   "best_model_checkpoint": null,
   "epoch": 0.0182648401826484,
   "log_history": [
     {
       "epoch": 0.0182648401826484,
+      "grad_norm": 0.2188371866941452,
       "learning_rate": 0.0001,
+      "loss": 4.4958,
       "step": 10
     },
     {
       "epoch": 0.0182648401826484,
       "step": 10,
       "total_flos": 1.394108846267433e+17,
+      "train_loss": 4.495834732055664,
+      "train_runtime": 166.6924,
+      "train_samples_per_second": 9.599,
       "train_steps_per_second": 0.06
     }
   ],