Instructions to use Iambackup/Dolphin-X1-Trinity-Nano-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Iambackup/Dolphin-X1-Trinity-Nano-FP8 with Transformers:

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

pipe = pipeline("text-generation", model="Iambackup/Dolphin-X1-Trinity-Nano-FP8", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Iambackup/Dolphin-X1-Trinity-Nano-FP8", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Iambackup/Dolphin-X1-Trinity-Nano-FP8", trust_remote_code=True, 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 Iambackup/Dolphin-X1-Trinity-Nano-FP8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Iambackup/Dolphin-X1-Trinity-Nano-FP8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Iambackup/Dolphin-X1-Trinity-Nano-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Iambackup/Dolphin-X1-Trinity-Nano-FP8

SGLang

How to use Iambackup/Dolphin-X1-Trinity-Nano-FP8 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 "Iambackup/Dolphin-X1-Trinity-Nano-FP8" \
    --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": "Iambackup/Dolphin-X1-Trinity-Nano-FP8",
		"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 "Iambackup/Dolphin-X1-Trinity-Nano-FP8" \
        --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": "Iambackup/Dolphin-X1-Trinity-Nano-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Iambackup/Dolphin-X1-Trinity-Nano-FP8 with Docker Model Runner:
```
docker model run hf.co/Iambackup/Dolphin-X1-Trinity-Nano-FP8
```

Dolphin-X1-Trinity-Nano-FP8 / config.json

Iambackup

Duplicate from dphn/Dolphin-X1-Trinity-Nano-FP8

fe2c0e4 about 1 month ago

Raw

History Blame Contribute Delete

3.98 kB

	{
	"architectures": [
	"AfmoeForCausalLM"
	],
	"attention_dropout": 0.0,
	"auto_map": {
	"AutoConfig": "configuration_afmoe.AfmoeConfig",
	"AutoModel": "modeling_afmoe.AfmoeModel",
	"AutoModelForCausalLM": "modeling_afmoe.AfmoeForCausalLM"
	},
	"dtype": "bfloat16",
	"global_attn_every_n_layers": 4,
	"head_dim": 128,
	"hidden_act": "silu",
	"hidden_size": 1024,
	"initializer_range": 0.02,
	"intermediate_size": 3072,
	"layer_types": [
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	"sliding_attention",
	"sliding_attention",
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	"load_balance_coeff": 0.001,
	"max_position_embeddings": 131072,
	"model_type": "afmoe",
	"moe_intermediate_size": 256,
	"mup_enabled": true,
	"n_group": 1,
	"num_attention_heads": 8,
	"num_dense_layers": 2,
	"num_expert_groups": 1,
	"num_experts": 128,
	"num_experts_per_tok": 8,
	"num_hidden_layers": 56,
	"num_key_value_heads": 2,
	"num_limited_groups": 1,
	"num_shared_experts": 1,
	"quantization_config": {
	"config_groups": {
	"config_group_0": {
	"format": "float-quantized",
	"input_activations": {
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	"block_structure": null,
	"dynamic": true,
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	"num_bits": 8,
	"observer": null,
	"observer_kwargs": {},
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	"strategy": "group",
	"symmetric": true,
	"type": "float",
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	"output_activations": null,
	"targets": [
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	"weights": {
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	"block_structure": [
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	"dynamic": false,
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	"observer": "memoryless_minmax",
	"observer_kwargs": {},
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	"strategy": "block",
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	}
	},
	"format": "float-quantized",
	"global_compression_ratio": null,
	"ignore": [
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	"re:model\\.layers\\.[0-9]+\\.mlp\\.router\\.gate"
	],
	"kv_cache_scheme": null,
	"quant_method": "compressed-tensors",
	"quantization_status": "compressed",
	"transform_config": {},
	"version": "0.15.1.a20260526"
	},
	"rms_norm_eps": 1e-05,
	"rope_scaling": null,
	"rope_theta": 10000,
	"route_norm": true,
	"route_scale": 2.826,
	"score_func": "sigmoid",
	"sliding_window": 2048,
	"tie_word_embeddings": false,
	"topk_group": 1,
	"transformers_version": "4.57.3",
	"use_cache": true,
	"use_grouped_mm": true,
	"vocab_size": 200192
	}