Instructions to use tiny-random/longcat-flash-ngram with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tiny-random/longcat-flash-ngram with Transformers:

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

pipe = pipeline("text-generation", model="tiny-random/longcat-flash-ngram", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("tiny-random/longcat-flash-ngram", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use tiny-random/longcat-flash-ngram with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/tiny-random/longcat-flash-ngram

SGLang

How to use tiny-random/longcat-flash-ngram 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 "tiny-random/longcat-flash-ngram" \
    --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": "tiny-random/longcat-flash-ngram",
		"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 "tiny-random/longcat-flash-ngram" \
        --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": "tiny-random/longcat-flash-ngram",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tiny-random/longcat-flash-ngram with Docker Model Runner:
```
docker model run hf.co/tiny-random/longcat-flash-ngram
```

longcat-flash-ngram / README.md

yujiepan

Upload folder using huggingface_hub

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	---
	library_name: transformers
	base_model:
	- meituan-longcat/LongCat-Flash-Lite
	---

	This tiny model is intended for debugging. It is randomly initialized using the configuration adapted from [meituan-longcat/LongCat-Flash-Lite](https://huggingface.co/meituan-longcat/LongCat-Flash-Lite).

	\| File path \| Size \|
	\|------\|------\|
	\| model.safetensors \| 8.4MB \|


	### Example usage:

	```python
	import torch
	import transformers

	model_id = "tiny-random/longcat-flash-ngram"
	pipe = transformers.pipelines.pipeline(
	'text-generation',
	model=model_id,
	trust_remote_code=True,
	device_map='cuda',
	torch_dtype=torch.bfloat16,
	)
	past_key_values = transformers.DynamicCache(config=None) # set config to None
	r = pipe('Hello, world!', past_key_values=past_key_values, max_new_tokens=32)
	print(r)
	```

	### Codes to create this repo:

	<details>
	<summary>Python codes</summary>

	```python
	import json
	from copy import deepcopy
	from pathlib import Path

	import torch
	import torch.nn as nn
	from huggingface_hub import file_exists, hf_hub_download
	from transformers import (
	AutoConfig,
	AutoModelForCausalLM,
	AutoProcessor,
	AutoTokenizer,
	GenerationConfig,
	set_seed,
	)
	from transformers.models.glm4_moe.modeling_glm4_moe import Glm4MoeRMSNorm
	source_model_id = "meituan-longcat/LongCat-Flash-Lite"
	save_folder = "/tmp/tiny-random/longcat-flash-ngram"

	Path(save_folder).mkdir(parents=True, exist_ok=True)
	tokenizer = AutoTokenizer.from_pretrained(source_model_id, trust_remote_code=True)
	tokenizer.save_pretrained(save_folder)

	with open(hf_hub_download(source_model_id, filename='config.json', repo_type='model'), 'r', encoding='utf-8') as f:
	config_json = json.load(f)
	for k, v in config_json['auto_map'].items():
	config_json['auto_map'][k] = f'{source_model_id}--{v}'
	config_json.update({
	'num_layers': 2,
	'hidden_size': 8,
	'ffn_hidden_size': 32,
	'expert_ffn_hidden_size': 32,
	'num_attention_heads': 4,
	'kv_lora_rank': 384,
	'n_routed_experts': 32,
	'q_lora_rank': 32,
	'qk_nope_head_dim': 64,
	'qk_rope_head_dim': 192,
	'head_dim': 192,
	'qk_head_dim': 256,
	'v_head_dim': 64,
	'moe_topk': 12,
	'zero_expert_num': 16,
	'emb_split_num': 2,
	'emb_neighbor_num': 2,
	'ngram_vocab_size_ratio': 4,
	})
	# del config_json['quantization_config']
	with open(f"{save_folder}/config.json", "w", encoding='utf-8') as f:
	json.dump(config_json, f, indent=2)

	config = AutoConfig.from_pretrained(
	save_folder,
	trust_remote_code=True,
	)
	print(config)
	torch.set_default_dtype(torch.bfloat16)
	model = AutoModelForCausalLM.from_config(config, trust_remote_code=True)
	if file_exists(filename="generation_config.json", repo_id=source_model_id, repo_type='model'):
	model.generation_config = GenerationConfig.from_pretrained(
	source_model_id, trust_remote_code=True,
	)
	model = model.cpu()
	# MTP
	model.model.mtp = nn.ModuleDict({
	"layers": nn.ModuleList([nn.ModuleDict(dict(
	eh_proj=nn.Linear(config.hidden_size * 2, config.hidden_size, bias=False),
	enorm=nn.ModuleDict({"m": nn.RMSNorm(config.hidden_size)}),
	hnorm=nn.ModuleDict({"m": nn.RMSNorm(config.hidden_size)}),
	input_layernorm=nn.RMSNorm(config.hidden_size),
	post_attention_layernorm=nn.RMSNorm(config.hidden_size),
	self_attn=deepcopy(model.model.layers[0].self_attn[0]),
	transformer_layer=nn.ModuleDict({"mlp": deepcopy(model.model.layers[0].mlps[0])}),
	))]),
	"norm": nn.RMSNorm(config.hidden_size),
	})
	for i in range(config.num_layers):
	model.model.layers[i].mlp.router = model.model.layers[i].mlp.router.float()
	# model.model.layers[i].mlp.router.e_score_correction_bias = torch.zeros((config.n_routed_experts + config.zero_expert_num)).float()
	set_seed(42)
	with torch.no_grad():
	for name, p in sorted(model.named_parameters()):
	torch.nn.init.normal_(p, 0, 0.1)
	print(name, p.shape, p.dtype)
	model.model.mtp.embed_tokens = deepcopy(model.model.embed_tokens)
	model.model.ngram_embeddings = None # avoid saving shared params

	model.save_pretrained(save_folder)
	torch.set_default_dtype(torch.float32)

	with open(f"{save_folder}/config.json", "r", encoding='utf-8') as f:
	config_json = json.load(f)
	config_json['auto_map'] = {k: source_model_id + '--' +
	v.split('--')[-1] for k, v in config_json['auto_map'].items()}
	with open(f"{save_folder}/config.json", "w", encoding='utf-8') as f:
	json.dump(config_json, f, indent=2)
	for f in Path(save_folder).glob('*.py'):
	f.unlink()
	```

	</details>

	### Printing the model:

	<details><summary>Click to expand</summary>

	```text
	LongcatFlashNgramForCausalLM(
	(model): LongcatFlashNgramModel(
	(embed_tokens): Embedding(131072, 8)
	(layers): ModuleList(
	(0-1): 2 x LongcatFlashDecoderLayer(
	(mlp): LongcatFlashMoE(
	(experts): ModuleList(
	(0-31): 32 x LongcatFlashMLP(
	(gate_proj): Linear(in_features=8, out_features=32, bias=False)
	(up_proj): Linear(in_features=8, out_features=32, bias=False)
	(down_proj): Linear(in_features=32, out_features=8, bias=False)
	(act_fn): SiLUActivation()
	)
	(32-47): 16 x Identity()
	)
	(router): LongcatFlashTopkRouter(
	(classifier): Linear(in_features=8, out_features=48, bias=False)
	)
	)
	(self_attn): ModuleList(
	(0-1): 2 x LongcatFlashMLA(
	(q_a_proj): Linear(in_features=8, out_features=32, bias=False)
	(q_a_layernorm): LongcatFlashRMSNorm((32,), eps=1e-06)
	(q_b_proj): Linear(in_features=32, out_features=1024, bias=False)
	(kv_a_proj_with_mqa): Linear(in_features=8, out_features=576, bias=False)
	(kv_a_layernorm): LongcatFlashRMSNorm((384,), eps=1e-06)
	(kv_b_proj): Linear(in_features=384, out_features=512, bias=False)
	(o_proj): Linear(in_features=256, out_features=8, bias=False)
	)
	)
	(mlps): ModuleList(
	(0-1): 2 x LongcatFlashMLP(
	(gate_proj): Linear(in_features=8, out_features=32, bias=False)
	(up_proj): Linear(in_features=8, out_features=32, bias=False)
	(down_proj): Linear(in_features=32, out_features=8, bias=False)
	(act_fn): SiLUActivation()
	)
	)
	(input_layernorm): ModuleList(
	(0-1): 2 x LongcatFlashRMSNorm((8,), eps=1e-05)
	)
	(post_attention_layernorm): ModuleList(
	(0-1): 2 x LongcatFlashRMSNorm((8,), eps=1e-05)
	)
	)
	)
	(norm): LongcatFlashRMSNorm((8,), eps=1e-05)
	(rotary_emb): LongcatFlashRotaryEmbedding()
	(ngram_embeddings): None
	(mtp): ModuleDict(
	(layers): ModuleList(
	(0): ModuleDict(
	(eh_proj): Linear(in_features=16, out_features=8, bias=False)
	(enorm): ModuleDict(
	(m): RMSNorm((8,), eps=None, elementwise_affine=True)
	)
	(hnorm): ModuleDict(
	(m): RMSNorm((8,), eps=None, elementwise_affine=True)
	)
	(input_layernorm): RMSNorm((8,), eps=None, elementwise_affine=True)
	(post_attention_layernorm): RMSNorm((8,), eps=None, elementwise_affine=True)
	(self_attn): LongcatFlashMLA(
	(q_a_proj): Linear(in_features=8, out_features=32, bias=False)
	(q_a_layernorm): LongcatFlashRMSNorm((32,), eps=1e-06)
	(q_b_proj): Linear(in_features=32, out_features=1024, bias=False)
	(kv_a_proj_with_mqa): Linear(in_features=8, out_features=576, bias=False)
	(kv_a_layernorm): LongcatFlashRMSNorm((384,), eps=1e-06)
	(kv_b_proj): Linear(in_features=384, out_features=512, bias=False)
	(o_proj): Linear(in_features=256, out_features=8, bias=False)
	)
	(transformer_layer): ModuleDict(
	(mlp): LongcatFlashMLP(
	(gate_proj): Linear(in_features=8, out_features=32, bias=False)
	(up_proj): Linear(in_features=8, out_features=32, bias=False)
	(down_proj): Linear(in_features=32, out_features=8, bias=False)
	(act_fn): SiLUActivation()
	)
	)
	)
	)
	(norm): RMSNorm((8,), eps=None, elementwise_affine=True)
	(embed_tokens): Embedding(131072, 8)
	)
	)
	(lm_head): Linear(in_features=8, out_features=131072, bias=False)
	)
	```

	</details>