Instructions to use Bochkov/llm-fix-min-fixed-minimal-binary-code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Bochkov/llm-fix-min-fixed-minimal-binary-code with Transformers:

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

pipe = pipeline("text-generation", model="Bochkov/llm-fix-min-fixed-minimal-binary-code", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Bochkov/llm-fix-min-fixed-minimal-binary-code", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Bochkov/llm-fix-min-fixed-minimal-binary-code with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Bochkov/llm-fix-min-fixed-minimal-binary-code

SGLang

How to use Bochkov/llm-fix-min-fixed-minimal-binary-code 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 "Bochkov/llm-fix-min-fixed-minimal-binary-code" \
    --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": "Bochkov/llm-fix-min-fixed-minimal-binary-code",
		"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 "Bochkov/llm-fix-min-fixed-minimal-binary-code" \
        --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": "Bochkov/llm-fix-min-fixed-minimal-binary-code",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Bochkov/llm-fix-min-fixed-minimal-binary-code with Docker Model Runner:
```
docker model run hf.co/Bochkov/llm-fix-min-fixed-minimal-binary-code
```

Bochkov commited on 11 days ago

Commit

05ffae4

verified ·

1 Parent(s): c9f8060

Update README.md

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README.md +0 -14

README.md CHANGED Viewed

@@ -187,20 +187,6 @@ exact canonical-code match: True
 mismatching entries: 0
 ```
-total parameters: 471173120
-trainable parameters at load: 471173120
-embedding shape: (65536, 16)
-requires_grad: True
-unique values: [0.0, 1.0]
-globally binary: True
-non-binary entries: 0
-canonical-code mismatches: 0
-token 'A' id: 65
-token 'A' 16-bit code: [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
-lifted shape: (1024,)
-first 32 lifted values: [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
-OK: exact frozen canonical Binary-16 checkpoint artifact
 ## Intended use
 This checkpoint is provided for reproducibility of the paper's main claim: a trainable input embedding table is not necessary for useful language modeling in the studied regime.

 mismatching entries: 0
 ```
 ## Intended use
 This checkpoint is provided for reproducibility of the paper's main claim: a trainable input embedding table is not necessary for useful language modeling in the studied regime.