Instructions to use legesher/language-decoded-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use legesher/language-decoded-lora with Transformers:

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

pipe = pipeline("text-generation", model="legesher/language-decoded-lora")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("legesher/language-decoded-lora", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use legesher/language-decoded-lora with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/legesher/language-decoded-lora

SGLang

How to use legesher/language-decoded-lora 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 "legesher/language-decoded-lora" \
    --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": "legesher/language-decoded-lora",
		"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 "legesher/language-decoded-lora" \
        --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": "legesher/language-decoded-lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio new

How to use legesher/language-decoded-lora with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for legesher/language-decoded-lora to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for legesher/language-decoded-lora to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for legesher/language-decoded-lora to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="legesher/language-decoded-lora",
    max_seq_length=2048,
)

Docker Model Runner
How to use legesher/language-decoded-lora with Docker Model Runner:
```
docker model run hf.co/legesher/language-decoded-lora
```

docs: add condition-1-en-32k to Model Structure table

by madiedgar - opened Mar 31

base: refs/heads/main

←

from: refs/pr/7

Discussion Files changed

-7

Files changed (1) hide show

README.md +8 -7

README.md CHANGED Viewed

@@ -37,13 +37,14 @@ All adapters are trained on [CohereLabs/tiny-aya-base](https://huggingface.co/Co
 This repo is the canonical hub for all Language Decoded LoRA adapters, organized by experimental condition:
-| Subdirectory         | Condition   | Training Data                                        |
-| -------------------- | ----------- | ---------------------------------------------------- |
-| `condition-1-en-5k/` | Condition 1 | English Python from The Stack Dedup (5k subset)      |
-| `condition-2-zh-5k/` | Condition 2 | Chinese keyword-swapped Python (Legesher-transpiled) |
-| `condition-2-es-5k/` | Condition 2 | Spanish keyword-swapped Python (Legesher-transpiled) |
-| `condition-2-ur-5k/` | Condition 2 | Urdu keyword-swapped Python (Legesher-transpiled)    |
-| `condition-3-zh-5k/` | Condition 3 | Transpiled + native Chinese code (blended)           |
 ### The Experimental Ladder

 This repo is the canonical hub for all Language Decoded LoRA adapters, organized by experimental condition:
+| Subdirectory          | Condition   | Training Data                                         |
+| --------------------- | ----------- | ----------------------------------------------------- |
+| `condition-1-en-32k/` | Condition 1 | English Python from The Stack Dedup (full 32k corpus) |
+| `condition-1-en-5k/`  | Condition 1 | English Python from The Stack Dedup (5k subset)       |
+| `condition-2-zh-5k/`  | Condition 2 | Chinese keyword-swapped Python (Legesher-transpiled)  |
+| `condition-2-es-5k/`  | Condition 2 | Spanish keyword-swapped Python (Legesher-transpiled)  |
+| `condition-2-ur-5k/`  | Condition 2 | Urdu keyword-swapped Python (Legesher-transpiled)     |
+| `condition-3-zh-5k/`  | Condition 3 | Transpiled + native Chinese code (blended)            |
 ### The Experimental Ladder