Text Generation
Transformers
Safetensors
English
tokle
causal-lm
custom-architecture
custom_code
slm
small-language-model
Instructions to use techdotus/Tokle-3M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use techdotus/Tokle-3M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="techdotus/Tokle-3M", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("techdotus/Tokle-3M", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use techdotus/Tokle-3M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "techdotus/Tokle-3M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "techdotus/Tokle-3M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/techdotus/Tokle-3M
- SGLang
How to use techdotus/Tokle-3M 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 "techdotus/Tokle-3M" \ --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": "techdotus/Tokle-3M", "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 "techdotus/Tokle-3M" \ --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": "techdotus/Tokle-3M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use techdotus/Tokle-3M with Docker Model Runner:
docker model run hf.co/techdotus/Tokle-3M
Update README.md
Browse files
README.md
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### Ablation: SPAB vs. Distilled
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| Model | Params | Int Index | HellaSwag | ARC-Easy | ARC-Chal | PIQA | ArithMark-3 |
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| Tokle-SPAB-11.3M (with SPAB) | 11.3M (2.91M trainable + 8.39M frozen) | 9.16 | 27.22% | 34.68% | 24.49% | 54.95% | 41.70% |
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| Tokle-3M (SPAB distilled) | 2.91M | 8.
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## Comparison Results
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| Tokle-3M (Tech.us) | 2.91M | **8.
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| Ember-2 (SurjoLabs) | 2.96M×2 | 7.21 | 27.28% | 33.42% | 22.01% | 55.11% | 35.90% |
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| BananaMind-2-Micro (BananaMind) | 2.9M | 6.01 | **28.27%** | 33.12% | 21.93% | 53.21% | 34.00% |
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| GPT-S-1.4M (Axiomic Labs) | 1.4M | 5.40 | 26.89% | 31.57% | 21.93% | **55.17%** | 30.20% |
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| 27.20% | 34.85% | 23.98% | 55.01% | 40.80% |
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### Ablation: SPAB vs. Distilled
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| Model | Params | Int Index | HellaSwag | ARC-Easy | ARC-Chal | PIQA | ArithMark-3 |
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| Tokle-SPAB-11.3M (with SPAB) | 11.3M (2.91M trainable + 8.39M frozen) | 9.16 | 27.22% | 34.68% | 24.49% | 54.95% | 41.70% |
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| Tokle-3M (SPAB distilled) | 2.91M | 8.92 | 27.20% | 34.85% | 23.98% | 55.01% | 40.80% |
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## Comparison Results
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| Tokle-3M (Tech.us) | 2.91M | **8.92** | 27.20% | **34.85%** | **23.98%** | 55.01% | **40.80%** |
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| Ember-2 (SurjoLabs) | 2.96M×2 | 7.21 | 27.28% | 33.42% | 22.01% | 55.11% | 35.90% |
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| BananaMind-2-Micro (BananaMind) | 2.9M | 6.01 | **28.27%** | 33.12% | 21.93% | 53.21% | 34.00% |
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| GPT-S-1.4M (Axiomic Labs) | 1.4M | 5.40 | 26.89% | 31.57% | 21.93% | **55.17%** | 30.20% |
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