Instructions to use anarlavrenov/lime-1b-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use anarlavrenov/lime-1b-instruct with Transformers:

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

pipe = pipeline("text-generation", model="anarlavrenov/lime-1b-instruct", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("anarlavrenov/lime-1b-instruct", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use anarlavrenov/lime-1b-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "anarlavrenov/lime-1b-instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "anarlavrenov/lime-1b-instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/anarlavrenov/lime-1b-instruct

SGLang

How to use anarlavrenov/lime-1b-instruct 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 "anarlavrenov/lime-1b-instruct" \
    --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": "anarlavrenov/lime-1b-instruct",
		"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 "anarlavrenov/lime-1b-instruct" \
        --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": "anarlavrenov/lime-1b-instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use anarlavrenov/lime-1b-instruct with Docker Model Runner:
```
docker model run hf.co/anarlavrenov/lime-1b-instruct
```

anarlavrenov commited on Nov 27, 2025

Commit

3f5bb73

verified ·

1 Parent(s): 31f4c0d

Add GenerationMixin to LIMEForCausalLM

Browse files

Files changed (1) hide show

modeling_lime.py +2 -1

modeling_lime.py CHANGED Viewed

@@ -1,6 +1,7 @@
 import torch
 from torch import nn
 from transformers import PreTrainedModel
 from transformers.modeling_outputs import CausalLMOutputWithPast
 from typing import Optional, Tuple, Union
 from ukraine.research.transformer.transformer import Transformer
@@ -21,7 +22,7 @@ def make_norm(config: LIMEConfig):
     return nn.RMSNorm(config.d_model)
-class LIMEForCausalLM(PreTrainedModel):
     config_class = LIMEConfig
     base_model_prefix = "lime"
     _tied_weights_keys = ["transformer.output_fc.weight"]

 import torch
 from torch import nn
 from transformers import PreTrainedModel
+from transformers.generation import GenerationMixin
 from transformers.modeling_outputs import CausalLMOutputWithPast
 from typing import Optional, Tuple, Union
 from ukraine.research.transformer.transformer import Transformer
     return nn.RMSNorm(config.d_model)
+class LIMEForCausalLM(PreTrainedModel, GenerationMixin):
     config_class = LIMEConfig
     base_model_prefix = "lime"
     _tied_weights_keys = ["transformer.output_fc.weight"]