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 13, 2025

Commit

cf9b9e2

verified ·

1 Parent(s): 62b131c

Training completed

Browse files

Files changed (4) hide show

README.md +4 -4
all_results.json +5 -5
train_results.json +5 -5
trainer_state.json +8 -8

README.md CHANGED Viewed

@@ -1,9 +1,9 @@
 ---
 library_name: peft
-license: llama3.2
-base_model: meta-llama/Llama-3.2-3b-Instruct
 tags:
-- base_model:adapter:meta-llama/Llama-3.2-3b-Instruct
 - lora
 - transformers
 pipeline_tag: text-generation
@@ -17,7 +17,7 @@ should probably proofread and complete it, then remove this comment. -->
 # qwen_model_testing
-This model is a fine-tuned version of [meta-llama/Llama-3.2-3b-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3b-Instruct) on an unknown dataset.
 ## Model description

 ---
 library_name: peft
+license: mit
+base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
 tags:
+- base_model:adapter:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
 - lora
 - transformers
 pipeline_tag: text-generation
 # qwen_model_testing
+This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-Distill-Qwen-7B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B) on an unknown dataset.
 ## Model description

all_results.json CHANGED Viewed

@@ -1,8 +1,8 @@
 {
     "epoch": 0.0182648401826484,
-    "total_flos": 5.565784553370419e+16,
-    "train_loss": 4.055104446411133,
-    "train_runtime": 129.2068,
-    "train_samples_per_second": 12.383,
-    "train_steps_per_second": 0.077
 }

 {
     "epoch": 0.0182648401826484,
+    "total_flos": 1.394108846267433e+17,
+    "train_loss": 8.501841735839843,
+    "train_runtime": 190.4124,
+    "train_samples_per_second": 8.403,
+    "train_steps_per_second": 0.053
 }

train_results.json CHANGED Viewed

@@ -1,8 +1,8 @@
 {
     "epoch": 0.0182648401826484,
-    "total_flos": 5.565784553370419e+16,
-    "train_loss": 4.055104446411133,
-    "train_runtime": 129.2068,
-    "train_samples_per_second": 12.383,
-    "train_steps_per_second": 0.077
 }

 {
     "epoch": 0.0182648401826484,
+    "total_flos": 1.394108846267433e+17,
+    "train_loss": 8.501841735839843,
+    "train_runtime": 190.4124,
+    "train_samples_per_second": 8.403,
+    "train_steps_per_second": 0.053
 }

trainer_state.json CHANGED Viewed

@@ -11,19 +11,19 @@
   "log_history": [
     {
       "epoch": 0.0182648401826484,
-      "grad_norm": 0.5136012434959412,
       "learning_rate": 0.0001,
-      "loss": 4.0551,
       "step": 10
     },
     {
       "epoch": 0.0182648401826484,
       "step": 10,
-      "total_flos": 5.565784553370419e+16,
-      "train_loss": 4.055104446411133,
-      "train_runtime": 129.2068,
-      "train_samples_per_second": 12.383,
-      "train_steps_per_second": 0.077
     }
   ],
   "logging_steps": 10,
@@ -43,7 +43,7 @@
       "attributes": {}
     }
   },
-  "total_flos": 5.565784553370419e+16,
   "train_batch_size": 2,
   "trial_name": null,
   "trial_params": null

   "log_history": [
     {
       "epoch": 0.0182648401826484,
+      "grad_norm": 10.595149993896484,
       "learning_rate": 0.0001,
+      "loss": 8.5018,
       "step": 10
     },
     {
       "epoch": 0.0182648401826484,
       "step": 10,
+      "total_flos": 1.394108846267433e+17,
+      "train_loss": 8.501841735839843,
+      "train_runtime": 190.4124,
+      "train_samples_per_second": 8.403,
+      "train_steps_per_second": 0.053
     }
   ],
   "logging_steps": 10,
       "attributes": {}
     }
   },
+  "total_flos": 1.394108846267433e+17,
   "train_batch_size": 2,
   "trial_name": null,
   "trial_params": null