Instructions to use xav-ie/scribe-cleanup with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use xav-ie/scribe-cleanup with Transformers:

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

pipe = pipeline("text-generation", model="xav-ie/scribe-cleanup")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("xav-ie/scribe-cleanup")
model = AutoModelForCausalLM.from_pretrained("xav-ie/scribe-cleanup", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use xav-ie/scribe-cleanup with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/xav-ie/scribe-cleanup

SGLang

How to use xav-ie/scribe-cleanup 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 "xav-ie/scribe-cleanup" \
    --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": "xav-ie/scribe-cleanup",
		"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 "xav-ie/scribe-cleanup" \
        --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": "xav-ie/scribe-cleanup",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use xav-ie/scribe-cleanup with Docker Model Runner:
```
docker model run hf.co/xav-ie/scribe-cleanup
```

xav-ie commited on 21 days ago

Commit

35fbaeb

verified ·

1 Parent(s): 3800dff

Config without null keys, one versions table

Browse files

Files changed (2) hide show

README.md +1 -1
config.json +0 -5

README.md CHANGED Viewed

@@ -58,7 +58,7 @@ languages other than English, or as a replacement for the original transcript.
 | Version | For | Size |
 |---|---|---|
-| [`xav-ie/scribe-cleanup-mlx-4bit`](https://huggingface.co/xav-ie/scribe-cleanup-mlx-4bit) | Macs with Apple chips (compressed, smaller) | 2.8 GB |
 | [`xav-ie/scribe-cleanup-GGUF`](https://huggingface.co/xav-ie/scribe-cleanup-GGUF) | [llama.cpp](https://github.com/ggml-org/llama.cpp), a popular tool for running models locally (compressed) | 3.3 GB |
 | [`xav-ie/scribe-cleanup`](https://huggingface.co/xav-ie/scribe-cleanup) | the full-size model, for Python's `transformers` library or [vLLM](https://github.com/vllm-project/vllm) on a server | 9.3 GB |
 | [`xav-ie/scribe-cleanup-lora`](https://huggingface.co/xav-ie/scribe-cleanup-lora) | just the trained add-on (LoRA), for building your own versions | 93 MB |

 | Version | For | Size |
 |---|---|---|
+| [`xav-ie/scribe-cleanup-mlx-4bit`](https://huggingface.co/xav-ie/scribe-cleanup-mlx-4bit) | Macs with Apple chips (compressed) | 2.8 GB |
 | [`xav-ie/scribe-cleanup-GGUF`](https://huggingface.co/xav-ie/scribe-cleanup-GGUF) | [llama.cpp](https://github.com/ggml-org/llama.cpp), a popular tool for running models locally (compressed) | 3.3 GB |
 | [`xav-ie/scribe-cleanup`](https://huggingface.co/xav-ie/scribe-cleanup) | the full-size model, for Python's `transformers` library or [vLLM](https://github.com/vllm-project/vllm) on a server | 9.3 GB |
 | [`xav-ie/scribe-cleanup-lora`](https://huggingface.co/xav-ie/scribe-cleanup-lora) | just the trained add-on (LoRA), for building your own versions | 93 MB |

config.json CHANGED Viewed

@@ -9,7 +9,6 @@
   "dtype": "bfloat16",
   "enable_moe_block": false,
   "eos_token_id": 1,
-  "expert_intermediate_size": null,
   "final_logit_softcapping": 30.0,
   "head_dim": 256,
   "hidden_activation": "gelu_pytorch_tanh",
@@ -56,9 +55,7 @@
   ],
   "max_position_embeddings": 131072,
   "model_type": "gemma4_text",
-  "moe_intermediate_size": null,
   "num_attention_heads": 8,
-  "num_experts": null,
   "num_hidden_layers": 35,
   "num_key_value_heads": 1,
   "num_kv_shared_layers": 20,
@@ -100,9 +97,7 @@
   },
   "sliding_window": 512,
   "tie_word_embeddings": true,
-  "top_k_experts": null,
   "transformers_version": "5.17.0",
-  "use_bidirectional_attention": null,
   "use_cache": true,
   "use_double_wide_mlp": true,
   "vocab_size": 262144,

   "dtype": "bfloat16",
   "enable_moe_block": false,
   "eos_token_id": 1,
   "final_logit_softcapping": 30.0,
   "head_dim": 256,
   "hidden_activation": "gelu_pytorch_tanh",
   ],
   "max_position_embeddings": 131072,
   "model_type": "gemma4_text",
   "num_attention_heads": 8,
   "num_hidden_layers": 35,
   "num_key_value_heads": 1,
   "num_kv_shared_layers": 20,
   },
   "sliding_window": 512,
   "tie_word_embeddings": true,
   "transformers_version": "5.17.0",
   "use_cache": true,
   "use_double_wide_mlp": true,
   "vocab_size": 262144,