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 Oct 30, 2025

Commit

467a404

verified ·

1 Parent(s): d4ed45e

Update README.md

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@@ -38,12 +38,8 @@ It aims to enhance **robustness, accuracy, and dialogue coherence** of LLMs oper
   <img src="hyacine-hsr.gif" width="150">
 </p>
----
 ### **Evaluation Comparison**
----
 | **Category**                    |  **EvolLLM-Linh** |  **GPT-OSS-20B**  | **Llama** | **Qwen-2507** |
 | ------------------------------- | :---------------: | :---------------: | :-------: | :-----------: |
 | SINGLE TURN – SINGLE FUNCTION   |       0.800       |       0.800       |    0.63   |      0.69     |
@@ -66,10 +62,6 @@ It aims to enhance **robustness, accuracy, and dialogue coherence** of LLMs oper
 > **We evaluate all models with the same configuration.**
 > If you find any incorrect or inconsistent result, please report it for verification.
 > This ensures transparency and reproducibility across benchmarks.
-<p align="center">
-  <img src="https://cdn-uploads.huggingface.co/production/uploads/65905af887944e494e37e09a/XB1XEInyfE3dyUNAGb5zF.webp" width="300">
-</p>
----
 ### **Leaderboard Reference**
 all model are benchmarked using **[ACEBench](https://chenchen0103.github.io/ACEBench/)** — assessing **function calling**, **compositional reasoning**, and **multi-turn interaction**.

   <img src="hyacine-hsr.gif" width="150">
 </p>
 ### **Evaluation Comparison**
 | **Category**                    |  **EvolLLM-Linh** |  **GPT-OSS-20B**  | **Llama** | **Qwen-2507** |
 | ------------------------------- | :---------------: | :---------------: | :-------: | :-----------: |
 | SINGLE TURN – SINGLE FUNCTION   |       0.800       |       0.800       |    0.63   |      0.69     |
 > **We evaluate all models with the same configuration.**
 > If you find any incorrect or inconsistent result, please report it for verification.
 > This ensures transparency and reproducibility across benchmarks.
 ### **Leaderboard Reference**
 all model are benchmarked using **[ACEBench](https://chenchen0103.github.io/ACEBench/)** — assessing **function calling**, **compositional reasoning**, and **multi-turn interaction**.