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")
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

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
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🧠 Model Card — EvolLLM-Linh

Model Overview

Name: EvolLLM-Linh
Version: v1.0
Release Date: October 23, 2025
Base Model: Qwen/Qwen3-4B-Instruct-2507
Library: 🤗 Transformers

Purpose:
EvolLLM-Linh is a fine-tuned large language model designed for function calling.
It aims to enhance robustness, accuracy, and dialogue coherence of LLMs operating in API-driven or tool-using environments.

Key Capabilities:

Precise and context-aware API invocation
Robust multi-turn dialogue consistency
Adaptive understanding of user preferences and intent shifts

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

Note: 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 — assessing function calling, compositional reasoning, and multi-turn interaction.
Results are internal benchmarks aligned with ACEBench task categories.