Instructions to use beyoru/EvolLLM-Linh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use beyoru/EvolLLM-Linh with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("beyoru/EvolLLM-Linh")
model = AutoModelForCausalLM.from_pretrained("beyoru/EvolLLM-Linh", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use beyoru/EvolLLM-Linh with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/beyoru/EvolLLM-Linh

SGLang

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

Docker Model Runner
How to use beyoru/EvolLLM-Linh with Docker Model Runner:
```
docker model run hf.co/beyoru/EvolLLM-Linh
```

beyoru commited on Nov 2, 2025

Commit

561865e

verified ·

1 Parent(s): c78b8a4

Update README.md

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README.md +16 -15

README.md CHANGED Viewed

@@ -41,21 +41,22 @@ It aims to enhance **robustness, accuracy, and dialogue coherence** of LLMs oper
 ### **Evaluation Comparison**
-| **Category**                    |  **EvolLLM-Linh** |  **GPT-OSS-20B**  | **Llama** | **Qwen-2507** |
-| ------------------------------- | :---------------: | :---------------: | :-------: | :-----------: |
-| SINGLE TURN – SINGLE FUNCTION   |       0.800       |       0.800       |    0.63   |      0.69     |
-| SINGLE TURN – PARALLEL FUNCTION |       0.660       |       0.620       |    0.16   |      0.51     |
-| MULTI TURN – USER ADJUST        |       0.500       |       0.500       |    0.40   |      0.48     |
-| MULTI TURN – USER SWITCH        |       0.620       |       0.620       |    0.40   |      0.56     |
-| SIMILAR API CALLS               |       0.760       |       0.740       |    0.64   |      0.68     |
-| USER PREFERENCE HANDLING        |       0.600       |       0.640       |    0.62   |      0.64     |
-| ATOMIC TASK – BOOLEAN           |       0.880       |       0.960       |    0.70   |      0.68     |
-| ATOMIC TASK – ENUM              |       0.940       |       0.940       |    0.94   |      0.86     |
-| ATOMIC TASK – NUMBER            |       0.940       |       0.960       |    0.90   |      0.82     |
-| ATOMIC TASK – LIST              |       0.920       |       0.900       |    0.84   |      0.78     |
-| ATOMIC TASK – OBJECT (DEEP)     |       0.580       |       0.520       |    0.32   |      0.36     |
-| ATOMIC TASK – OBJECT (SHORT)    |       0.800       |       0.960       |    0.70   |      0.56     |
-| **Overall Accuracy**            | **0.750 (75.0%)** | **0.760 (76.0%)** |  **0.61** |    **0.64**   |
 ---

 ### **Evaluation Comparison**
+| **Category**                    |  **EvolLLM-Linh** |  **GPT-OSS-20B**  | **Llama** | **Qwen-2507** |      **MinCoder-4B-Expert**     |
+| ------------------------------- | :---------------: | :---------------: | :-------: | :-----------: | :---------------: |
+| SINGLE TURN – SINGLE FUNCTION   |       0.800       |       0.800       |    0.63   |      0.69     |        0.81       |
+| SINGLE TURN – PARALLEL FUNCTION |       0.660       |       0.620       |    0.16   |      0.51     |        0.66       |
+| MULTI TURN – USER ADJUST        |       0.500       |       0.500       |    0.40   |      0.48     |        0.50       |
+| MULTI TURN – USER SWITCH        |       0.620       |       0.620       |    0.40   |      0.56     |        0.64       |
+| SIMILAR API CALLS               |       0.760       |       0.740       |    0.64   |      0.68     |        0.76       |
+| USER PREFERENCE HANDLING        |       0.600       |       0.640       |    0.62   |      0.64     |        0.60       |
+| ATOMIC TASK – BOOLEAN           |       0.880       |       0.960       |    0.70   |      0.68     |        0.88       |
+| ATOMIC TASK – ENUM              |       0.940       |       0.940       |    0.94   |      0.86     |        0.96       |
+| ATOMIC TASK – NUMBER            |       0.940       |       0.960       |    0.90   |      0.82     |        0.94       |
+| ATOMIC TASK – LIST              |       0.920       |       0.900       |    0.84   |      0.78     |        0.94       |
+| ATOMIC TASK – OBJECT (DEEP)     |       0.580       |       0.520       |    0.32   |      0.36     |        0.62       |
+| ATOMIC TASK – OBJECT (SHORT)    |       0.800       |       0.960       |    0.70   |      0.56     |        0.82       |
+| **Overall Accuracy**            | **0.750 (75.0%)** | **0.760 (76.0%)** |  **0.61** |    **0.64**   |      **0.761**    |
 ---