Instructions to use techdotus/Tokle-SPAB-3M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use techdotus/Tokle-SPAB-3M with Transformers:

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

pipe = pipeline("text-generation", model="techdotus/Tokle-SPAB-3M", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("techdotus/Tokle-SPAB-3M", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use techdotus/Tokle-SPAB-3M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "techdotus/Tokle-SPAB-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-SPAB-3M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/techdotus/Tokle-SPAB-3M

SGLang

How to use techdotus/Tokle-SPAB-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-SPAB-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-SPAB-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-SPAB-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-SPAB-3M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use techdotus/Tokle-SPAB-3M with Docker Model Runner:
```
docker model run hf.co/techdotus/Tokle-SPAB-3M
```

anandselvadurai-techus commited on 25 days ago

Commit

2ddc9b6

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1 Parent(s): ecac7ca

Update README.md

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@@ -67,7 +67,7 @@ All scores are 0-shot acc_norm, using the Open SLM Leaderboard methodology:
 | Hellaswag | ARC-Easy | ARC-Challenge | PIQA | Arithmark-3 |
 | --------- | -------- | ------------- | ---- | ----------- |
 | 27.22% | 34.68% | 24.49% | 54.95% | 41.70% |
-## Comparison Results
 All scores are 0-shot acc_norm, using the Open SLM Leaderboard methodology.
 Scores for the other models are from the Open SLM Leaderboard. **Bold** marks
 the best result in each column.
@@ -78,7 +78,7 @@ the best result in each column.
 | Ember-2 (SurjoLabs) | 2.96Mx2 | 7.21 | 27.28% | 33.42% | 22.01% | 55.11% | 35.90% |
 | BananaMind-2-Micro (BananaMind) | 2.9M | 6.01 | **28.27%** | 33.12% | 21.93% | 53.21% | 34.00% |
 | GPT-S-1.4M (Axiomic Labs) | 1.4M | 5.40 | 26.89% | 31.57% | 21.93% | **55.17%** | 30.20% |
 ## Training Data Details
 We trained on a curated mixture with a strict cleaning pipeline that also

 | Hellaswag | ARC-Easy | ARC-Challenge | PIQA | Arithmark-3 |
 | --------- | -------- | ------------- | ---- | ----------- |
 | 27.22% | 34.68% | 24.49% | 54.95% | 41.70% |
+<!-- ## Comparison Results
 All scores are 0-shot acc_norm, using the Open SLM Leaderboard methodology.
 Scores for the other models are from the Open SLM Leaderboard. **Bold** marks
 the best result in each column.
 | Ember-2 (SurjoLabs) | 2.96Mx2 | 7.21 | 27.28% | 33.42% | 22.01% | 55.11% | 35.90% |
 | BananaMind-2-Micro (BananaMind) | 2.9M | 6.01 | **28.27%** | 33.12% | 21.93% | 53.21% | 34.00% |
 | GPT-S-1.4M (Axiomic Labs) | 1.4M | 5.40 | 26.89% | 31.57% | 21.93% | **55.17%** | 30.20% |
+ -->
 ## Training Data Details
 We trained on a curated mixture with a strict cleaning pipeline that also