Instructions to use roskosmos19/Dolphin-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use roskosmos19/Dolphin-4B with Transformers:

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

pipe = pipeline("text-generation", model="roskosmos19/Dolphin-4B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("roskosmos19/Dolphin-4B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use roskosmos19/Dolphin-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "roskosmos19/Dolphin-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "roskosmos19/Dolphin-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/roskosmos19/Dolphin-4B

SGLang

How to use roskosmos19/Dolphin-4B 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 "roskosmos19/Dolphin-4B" \
    --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": "roskosmos19/Dolphin-4B",
		"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 "roskosmos19/Dolphin-4B" \
        --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": "roskosmos19/Dolphin-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use roskosmos19/Dolphin-4B with Docker Model Runner:
```
docker model run hf.co/roskosmos19/Dolphin-4B
```

Dolphin-4B

Dolphin is a highly capable, efficient, and practical language model focused on maximum usefulness, strong reasoning, excellent coding ability, and reliable agentic behavior.

This is a carefully configured and optimized release based on the Spark-X2.5-4B architecture, fine-tuned in identity and behavior to deliver elite-level performance in everyday use, coding, tool use, and complex tasks.

Key Strengths

Extremely helpful & truthful – Clear, accurate, and direct answers. Never invents facts.
Strong reasoning – Careful step-by-step thinking, especially on hard problems.
Excellent coding – Clean, modern, production-ready code with good explanations.
Agent-ready – Solid tool calling, multi-step workflows, and instruction following.
Long context – Native support for up to 1M tokens via hybrid sliding-window + full attention architecture.
Efficient – Optimized for speed and low memory usage compared to many models of similar capability.

Model Details

Property	Value
Parameters	~4.1B
Context Length	1,048,576 tokens
Architecture	Hybrid Attention (Sliding Window + Full)
Vocabulary Size	131,072
Precision	bfloat16
License	Apache 2.0

Recommended Sampling Parameters

{
  "temperature": 1.0,
  "top_p": 0.95,
  "top_k": -1,
  "repetition_penalty": 1.0,
  "presence_penalty": 0.0,
  "frequency_penalty": 0.0
}

These settings work particularly well with the built-in thinking mode.

System Prompt (Default)

Dolphin comes with a strong default system prompt focused on:

Maximum helpfulness and truthfulness
Careful reasoning
Clean coding practices
Clear and structured communication
Professional yet friendly tone

You can still override it with your own system message.

Quick Start

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "path/to/Dolphin-X2.5-4B"

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="bfloat16",
    device_map="auto",
    trust_remote_code=True
)

messages = [
    {"role": "user", "content": "Write a clean Python function that calculates the Fibonacci sequence up to n."}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=1024, temperature=1.0, top_p=0.95)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

vLLM / SGLang / Ollama / LM Studio

The model is fully compatible with the same inference stacks as the original Spark-X2.5 architecture (vLLM, SGLang, llama.cpp, MLX, Ollama, LM Studio, etc.).

Use the included chat template and the recommended sampling parameters above for best results.

Chat Template

The model uses a clean, modern chat template with support for:

System / User / Assistant roles
Thinking mode (<think>...</think>)
Tool calling
Multi-turn conversations

Thinking is enabled by default. You can disable it per request if desired.

Philosophy

Dolphin is built with one clear goal:

Be as useful, accurate, and high-quality as possible in real-world use.

No fluff. No unnecessary restrictions. Just strong, reliable performance.

License

Apache 2.0

Credits

Based on the excellent Spark-X2.5 architecture and training work by the SparkLLM / XHToken team.

Downloads last month: 174

Safetensors

Model size

4B params

Tensor type

BF16

Model tree for roskosmos19/Dolphin-4B

Base model

XHToken/Spark-X2.5-4B-Base

Finetuned

(6)

this model