Instructions to use serda-dev/mamba-130m-hf-turkish with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use serda-dev/mamba-130m-hf-turkish with Transformers:

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

pipe = pipeline("text-generation", model="serda-dev/mamba-130m-hf-turkish")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("serda-dev/mamba-130m-hf-turkish")
model = AutoModelForCausalLM.from_pretrained("serda-dev/mamba-130m-hf-turkish", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use serda-dev/mamba-130m-hf-turkish with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "serda-dev/mamba-130m-hf-turkish"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "serda-dev/mamba-130m-hf-turkish",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/serda-dev/mamba-130m-hf-turkish

SGLang

How to use serda-dev/mamba-130m-hf-turkish 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 "serda-dev/mamba-130m-hf-turkish" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "serda-dev/mamba-130m-hf-turkish",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "serda-dev/mamba-130m-hf-turkish" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "serda-dev/mamba-130m-hf-turkish",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use serda-dev/mamba-130m-hf-turkish with Docker Model Runner:
```
docker model run hf.co/serda-dev/mamba-130m-hf-turkish
```

serda-dev commited on Feb 8

Commit

c837203

verified ·

1 Parent(s): 2c208fc

Upload MambaForCausalLM

Browse files

Files changed (3) hide show

config.json +40 -0
generation_config.json +7 -0
model.safetensors +3 -0

config.json ADDED Viewed

	@@ -0,0 +1,40 @@

+{
+  "architectures": [
+    "MambaForCausalLM"
+  ],
+  "bos_token_id": 0,
+  "conv_kernel": 4,
+  "d_inner": 1536,
+  "d_model": 768,
+  "dtype": "float32",
+  "eos_token_id": 0,
+  "expand": 2,
+  "fused_add_norm": true,
+  "hidden_act": "silu",
+  "hidden_size": 768,
+  "initializer_range": 0.1,
+  "intermediate_size": 1536,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "mamba",
+  "n_layer": 24,
+  "num_hidden_layers": 24,
+  "pad_token_id": 0,
+  "pad_vocab_size_multiple": 8,
+  "rescale_prenorm_residual": false,
+  "residual_in_fp32": true,
+  "rms_norm": true,
+  "ssm_cfg": {},
+  "state_size": 16,
+  "time_step_floor": 0.0001,
+  "time_step_init_scheme": "random",
+  "time_step_max": 0.1,
+  "time_step_min": 0.001,
+  "time_step_rank": 48,
+  "time_step_scale": 1.0,
+  "transformers_version": "5.0.0",
+  "use_bias": false,
+  "use_cache": true,
+  "use_conv_bias": true,
+  "use_mambapy": false,
+  "vocab_size": 50280
+}

generation_config.json ADDED Viewed

	@@ -0,0 +1,7 @@

+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "eos_token_id": 0,
+  "pad_token_id": 0,
+  "transformers_version": "5.0.0"
+}

model.safetensors ADDED Viewed

	@@ -0,0 +1,3 @@

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