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
```

EvolLLM-Linh / README.md

beyoru

Update README.md

f7d9b62 verified 11 months ago

3.5 kB

library_name: transformers
tags:
  - tool
  - function-calling
  - agent
  - merge
base_model:
  - Qwen/Qwen3-4B-Instruct-2507
  - beyoru/Qwen3-4B-I-1209
  - Qwen/Qwen3-4B-Thinking-2507
datasets:
  - Salesforce/xlam-function-calling-60k

library_name: transformers tags: - tool - function-calling - agent base_model: - Qwen/Qwen3-4B-Instruct-2507 datasets: - Salesforce/xlam-function-calling-60k

🧠 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	xLAM-2-8b-fc-r	Qwen3-2507
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	0.760	0.61	0.64

Leaderboard Reference

Both EvolLLM-Linh and GPT-OSS-20B are benchmarked using ACEBench — assessing function calling, compositional reasoning, and multi-turn interaction.
Results are internal benchmarks aligned with ACEBench task categories.

Method

GRPO (Rule-based reward + self-confidence reward)
Evol Merging

beyoru
/

EvolLLM-Linh

library_name: transformers tags: - tool - function-calling - agent base_model: - Qwen/Qwen3-4B-Instruct-2507 datasets: - Salesforce/xlam-function-calling-60k

🧠 Model Card — EvolLLM-Linh

Model Overview

Evaluation Comparison

Leaderboard Reference

Method

Support me at

License

MIT License — free for research and non-commercial use with attribution.
© 2025 beyoru.

library_name: transformers tags: - tool - function-calling - agent base_model: - Qwen/Qwen3-4B-Instruct-2507 datasets: - Salesforce/xlam-function-calling-60k

🧠 Model Card — EvolLLM-Linh

Model Overview

Evaluation Comparison

Leaderboard Reference

Method

Support me at

License

MIT License — free for research and non-commercial use with attribution.© 2025 beyoru.

MIT License — free for research and non-commercial use with attribution.
© 2025 beyoru.