Instructions to use Vilyam888/Code_analyze.1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Vilyam888/Code_analyze.1.0 with Transformers:

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

pipe = pipeline("text-generation", model="Vilyam888/Code_analyze.1.0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

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

tokenizer = AutoTokenizer.from_pretrained("Vilyam888/Code_analyze.1.0")
model = AutoModelForCausalLM.from_pretrained("Vilyam888/Code_analyze.1.0", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Vilyam888/Code_analyze.1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Vilyam888/Code_analyze.1.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Vilyam888/Code_analyze.1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Vilyam888/Code_analyze.1.0

SGLang

How to use Vilyam888/Code_analyze.1.0 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 "Vilyam888/Code_analyze.1.0" \
    --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": "Vilyam888/Code_analyze.1.0",
		"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 "Vilyam888/Code_analyze.1.0" \
        --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": "Vilyam888/Code_analyze.1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Vilyam888/Code_analyze.1.0 with Docker Model Runner:
```
docker model run hf.co/Vilyam888/Code_analyze.1.0
```

Vilyam888 commited on Jun 10

Commit

68bf4f3

1 Parent(s): a239d40

посчитал bleu/rouge по текстовым полям

Browse files

Files changed (3) hide show

metrics/01_training_perplexity.json +3 -3
metrics/02_json_validity.json +1 -1
metrics/03_bleu_rouge.json +1 -1

metrics/01_training_perplexity.json CHANGED Viewed

@@ -3,9 +3,9 @@
   "model": "Code_analyze.1.0",
   "hf_model": "Vilyam888/Code_analyze.1.0",
   "base_model": "Qwen/Qwen2.5-Coder-3B-Instruct",
-  "checkpoint": "C:\\Users\\User\\Desktop\\Предитог по диплому\\outputs\\code-analyze-qlora\\checkpoint-222",
-  "source": "C:\\Users\\User\\Desktop\\Предитог по диплому\\outputs\\code-analyze-qlora\\checkpoint-222\\trainer_state.json",
-  "validation_file": "C:\\Users\\User\\Desktop\\Предитог по диплому\\prepared_data\\code_analyze\\val.jsonl",
   "evaluation_date": "2026-06-11",
   "metrics": {
     "train_loss_final": 0.241,

   "model": "Code_analyze.1.0",
   "hf_model": "Vilyam888/Code_analyze.1.0",
   "base_model": "Qwen/Qwen2.5-Coder-3B-Instruct",
+  "checkpoint": "outputs/code-analyze-qlora/checkpoint-222",
+  "source": "outputs/code-analyze-qlora/checkpoint-222/trainer_state.json",
+  "validation_file": "prepared_data/code_analyze/val.jsonl",
   "evaluation_date": "2026-06-11",
   "metrics": {
     "train_loss_final": 0.241,

metrics/02_json_validity.json CHANGED Viewed

@@ -2,7 +2,7 @@
   "metric_group": "json_validity",
   "model": "Code_analyze.1.0",
   "hf_model": "Vilyam888/Code_analyze.1.0",
-  "evaluation_file": "C:\\Users\\User\\Desktop\\Предитог по диплому\\prepared_data\\code_analyze\\test.jsonl",
   "evaluation_date": "2026-06-11",
   "samples_evaluated": 100,
   "generation_params": {

   "metric_group": "json_validity",
   "model": "Code_analyze.1.0",
   "hf_model": "Vilyam888/Code_analyze.1.0",
+  "evaluation_file": "prepared_data/code_analyze/test.jsonl",
   "evaluation_date": "2026-06-11",
   "samples_evaluated": 100,
   "generation_params": {

metrics/03_bleu_rouge.json CHANGED Viewed

@@ -2,7 +2,7 @@
   "metric_group": "bleu_rouge",
   "model": "Code_analyze.1.0",
   "hf_model": "Vilyam888/Code_analyze.1.0",
-  "evaluation_file": "C:\\Users\\User\\Desktop\\Предитог по диплому\\prepared_data\\code_analyze\\test.jsonl",
   "evaluation_date": "2026-06-11",
   "text_fields": [
     "summary",

   "metric_group": "bleu_rouge",
   "model": "Code_analyze.1.0",
   "hf_model": "Vilyam888/Code_analyze.1.0",
+  "evaluation_file": "prepared_data/code_analyze/test.jsonl",
   "evaluation_date": "2026-06-11",
   "text_fields": [
     "summary",